IF TALKING OPENLY means being willing to expose to others what is inside of us, then listening openly means being willing to expose ourselves to something new from others.
I observed the power of this simple directional shift in Houston, Texas. I was working with a group of powerful and public-spirited businessmen. They had a high confidence in their ability to wisely guide the city into the future and a low confidence in government and politicians.
The businessmen were concerned that the younger generation of business leaders were not sufficiently enthusiastic about becoming responsible “city fathers” and that politicians would step into this vacuum and ruin the city. They organized a team that included younger and minority businesspeople and a few leaders of large nonprofit organizations to talk about the situation and decide what to do. They were reluctant, however, to broaden the membership of the team further to include politicians and community leaders. They were afraid that a more diverse group would be both more awkward to work with and unnecessary. Businesspeople had previously figured out amongst themselves what was best for the city, and they could continue to do so.
Int'l Conf. Genetic and Evolutionary Methods | GEM'14 |
A Novel Autoregressive Model for System State Prediction
De Z. Li1, Fathy Ismail1, and Wilson Wang2
Department of Mechanical & Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, Canada
Department of Mechanical Engineering, Lakehead University, Thunder Bay, Ontario, Canada
Abstract - Autoregressive (AR) model is one of the commonly used predictors for system state forecasting. Several training methods have been used to optimize AR model parameters, such as least square estimate and maximum likelihood estimate; however, both of these techniques are sensitive to noisy samples and to outliers. To address these problems, a novel AR predictor, NAR, is proposed in this work to improve the prediction accuracy and reduce the effect of noise and outliers. In NAR the model parameters of AR are trained using an adaptive least square estimate (ALSE) method. The proposed ALSE is used to learn samples characteristics more effectively. In each training epoch, the ALSE can discern the samples associated with their fitting accuracy. The samples with larger errors will be assigned a larger penalty value in the cost function; however the penalties of difficult-to-predict samples will be reduced to improve the overall prediction accuracy. The effectiveness of the developed NAR predictor is demonstrated by simulation tests. Test results show that the proposed NAR predictor can capture system dynamics effectively and track system characteristics accurately.
Quick. Who comes to mind when you think of male partnerships? We asked ourselves that question and came up with an impressive list of men who have made a sizable impact on the world: hugely successful ice cream entrepreneurs Ben and Jerry; historically revered explorers Lewis and Clark; cultural icons and famed magicians Penn and Teller; mega-hit film producers Bob and Harvey Weinstein; Google co-founders Larry Page and Sergey Brin; DNA discoverers Watson and Crick; Book of Mormon and Southpark creators Matt Stone and Trey Parker, to name just a few.
Now think of female partners. How many can you name? If you’re drawing a blank, you’re not alone. Yes, there are plenty of powerful female partners out there—we know that is true after interviewing 125 of them—but none have immediate name recognition like the men on the list above.
Figuring we were overlooking the obvious, we turned to Google. Here’s who popped up: Lucy and Ethel, the zany duo of 1950s television fame, two best friends who were always scheming (often unsuccessfully, though hilariously) to outwit their husbands; Laverne and Shirley, the Milwaukee beer bottlers, roommates, and sitcom characters who struggled to make it in life and love; Cagney and Lacey, two smart, tough television cops; and Thelma and Louise, movie heroines who, when all roads led to despair, drove their car off a cliff.