The CT COLT（康州语言教师协会）Poetry Competition Committee is happy to announce the opening of registration for the 2022 St. Louis COLT Poetry Competition, taking place on Friday, July 21, 2022, from 3:30 to5:30 PM. The Poetry Recitation Competition is an annul event open to students whose teachers are members of CT, COLT. As you know, last yea we sadly lost our long time board member and treasurer, Paul M. St Louis. In honor of his yeas of commitment to the COLT board and poetry competition, the competition is being renamed.

REGISTRATION

All registration materials are now online.

● School Registration / Member Rate

Registration is free for members, and an additional $9 per student will be added.

●School Registration/Non-Member Rate/Includes Membership-$30

This rate includes membership for the person in charge and an additional $9 per student will be added.

Please note registration closes on June 30, 2022.

AWARDS FOR EXCELLENCE

CT COLT is pleased to recognize and reward the excellence and achievement of deserving students. Winners:

●will receive a paper certificate.

● will receive a collection of poems.

●will have their names published on the CT COLT website.

●will have a special letter of recognition sent to their headteacher.

●will receive a well designed pen.

We hope that you and your students will be able to participate in the competition this year.

We know that artificial intelligence (AI) is smart enough to do a few things our minds cannot, and with incredible accuracy. And now, it seems it also has the capacity to detect loneliness in humans, which is an otherwise challenging task.

A new study, led by researchers at the University of California San Diego School of Medicine, US, has shown how AI tools can predict levels of loneliness from a person's speech with an accuracy rate of 94 percent.

The study focused on 80 participants aged 66 to 94, a population particularly vulnerable to loneliness. The subjects were asked 20 questions from the UCLA Loneliness Scale, which uses a four-point rating scale (四分制评价量表) for questions such as "How often do you feel left out?" and "How often do you feel part of a group of friends?"

They were also interviewed in private conversations, which were recorded and transcribed (转 录 ) by researchers. The transcripts (文 字 记 录 ) were then examined using natural language processing tools, including IBM Watson Natural Language Understanding (WNLU) software, to quantify (量化) expressed emotions.

The interesting thing about this system is that it not only uses dictionary-based methods, such as searching for specific words that express fear, but also presents corresponding patterns by testing the words used in the response.

Varsha Badal, the first author of the study, noted that the WNLU software system uses deep learning to extract (提取) data from keywords, categories, emotions and grammar.

"Natural language processing and machine learning can systematically examine long interviews from multiple individuals and explore how subtle speech features such as emotions may indicate loneliness," Badal said. "Similar emotion analyses by humans would be open to bias ( 偏 见 ), lack consistency, and require extensive (大 量 的 ) training to standardize. "

The lonelier a person felt, the longer their responses to direct questions regarding loneliness. The system was capable of not just detecting the degree of loneliness in each subject, but also showing differences between the way men and women spoke about loneliness. The men were found to use more fearful and joyful words in their responses, while the women tended to acknowledge feeling lonely during interviews.

Co-author Dilip Jeste said that the IBM-UC San Diego Center is now exploring natural language patterns of loneliness and wisdom, which are inversely (成反比地) linked in older adults. "Speech data can be combined with our other assessments of cognition (认 知 ), mobility, sleep, physical activity and mental health to improve understanding of aging and to help contribute to successful aging," he said.

Remember Malcolm Gladwell's famous "10,000-hour rule" from his bestselling book Outliers? It proposes that achieving mastery in any field requires roughly 10,000 hours of practice, emphasizing the importance of early professionalization and intensive practice for becoming an elite in a particular field.

This approach, known as "deliberate practice", encourages setting goals from a young age, investing time and specializing in a specific field. On paper, it sounds like a good path to success, but when we look at the lives of elite athletes, musicians and scholars, we find a different story. In reality, many individuals who have risen to the top of their respective fields took a more diverse and winding route. They often explored a wide range of activities, gained a broad skill set, and only specialized at a later stage in life. In other words, the idea that one must know one's specialization from a young age is fundamentally wrong.

Studies have shown that an approach called the "sampling period" is often more effective for achieving success. During this phase, individuals try various activities, gain diverse skills, identify their strengths and weaknesses, and only choose a field to specialize in once they have a clearer understanding of their passions and abilities.

Elite athletes, for example, rarely specialize in a single sport at a very young age. Instead, they engage in a variety of sports during their formative years, which allows them to acquire broad physical skills and discover their interests and talents. These early diversification experiences provide valuable lessons that can be applied to their chosen sports.

Consider the journey of Roger Federer, one of the greatest tennis players of all time. At the age of 6, he dabbled in rugby, skiing and wrestling, only returning to tennis later. The trial period benefited him not just because he found his true passion but also because his experiences in various sports enriched his tennis game with a unique set of skills.