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Using the original KORCC database9, two recent studies have been reported28,29. At first, Byun et al.28 assessed the prognosis of non-metastatic clear cell RCC using a deep learning-based survival predictions model. Harrels C-indices of DeepSurv for recurrence and cancer-specific survival were 0.802 and 0.834, respectively. More recently, Kim et al.29 developed ML-based algorithm predicting the probability of recurrence at 5 and 10years after surgery. The highest area under the receiver operating characteristic curve (AUROC) was obtained from the nave Bayes (NB) model, with values of 0.836 and 0.784 at 5 and 10years, respectively.

In the current study, we used the updated KORCC database. It now contains clinical data of more than 10,000 patients. To the best of our knowledge, this is the largest dataset in Asian population with RCC. With this dataset, we could develop much more accurate models with very high accuracy (range, 0.770.94) and F1-score (range, 0.770.97, Table 3). The accuracy values were relatively high compared to the previous models, including the Kattan nomogram, Leibovich model, the GRANT score, which were around 0.75,6,7,8. Among them, the Kattan nomogram was developed using a cohort of 601 patients with clinically localized RCC, and the overall C-index was 74%5. In a subsequent analysis with the same patient group using an additional prognostic variables including tumor necrosis, vascular invasion, and tumor grade, the C-index was as high as 82%30. Their prediction accuracies were not as high as ours yet.

In addition, we could include short-term (3-year) recurrence and survival data, which would be helpful for developing more sophisticated surveillance strategy. The other strength of current study was that most algorithms introduced so far had been applied18,19,20,21,22,23,24,25,26, showing relatively consistent performance with high accuracy. Finally, we also performed an external validation by using a separate (SNUBH) cohort, and achieved well maintained high accuracy and F1-score in both recurrence and survival (Fig.2). External validation of prediction models is essential, especially in case of using the multi-institutional dataset, to ensure and correct for differences between institutions.

AUROC has been mostly used as the standard evaluating performance of prediction models5,6,7,8,29. However, AUROC weighs changes in sensitivity and specificity equally without considering clinically meaningful information6. In addition, the lack of ability to compare performance of different ML models is another limitation of AUROC technique31. Thus, we adopted accuracy and F1-score instead of AUROC as evaluation metrics. F1-score, in addition to SMOTE17, is used as better accuracy metrics to solve the imbalanced data problems27.

RCC is not a single disease, but multiple histologically defined cancers with different genetic characteristics, clinical courses, and therapeutic responses32. With regard to metastatic RCC, the International Metastatic Renal Cell Carcinoma Database Consortium and the Memorial Sloan Kettering Cancer Center risk model have been extensively validated and widely used to predict survival outcomes of patients receiving systemic therapy33,34. However, both risk models had been developed without considering histologic subtypes. Thus, the predictive performance was presumed to have been strongly affected by clear cell type (predominant histologic subtype) RCC. Interestingly, in our previous study using the Korean metastatic RCC registry, we found the both risk models reliably predicted progression and survival even in non-clear cell type RCC35. In the current study, after performing subgroup analysis according to the histologic type (clear vs. non-clear cell type RCC), we also found very high accuracy and F1-score in all tested metrics (Supplemental Tables 3 and 4). Taking together, these findings suggest that the prognostic difference between clear and non-clear cell type RCC seems to be offset both in metastatic and non-metastatic RCC. Further effort is needed to develop and validate a sophisticated prediction model for individual subtypes of non-clear cell type RCC.

The current study had several limitations. First, due to the paucity of long-term follow-up cases at 10years, data imbalance problem could not be avoided. Subsequently, recurrence-free rate at 10-year was reported only to be 45.3%. In the majority of patients, further long-term follow up had not been performed in case of no evidence of disease at five years. However, we adopted both SMOTE and F1-score to solve these imbalanced data problems. The retrospective design of this study was also an inherent limitation. Another limitation was that the developed prediction model only included the Korean population. Validation of the model using data from other countries and races is also needed. In regard of non-clear cell type RCC, the current study cohort is still relatively small due to the rarity of the disease, we could not avoid integrating each subtype and analyzing together. Thus, further studies is still needed to develop and validate a prediction model for each subtypes. In addition, the lack of more accurate classifiers such as cross-validation and bootstrapping is another limitation of current study. Finally, the web-embedded deployment of model should be followed to improve accessibility and transportability.

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Machine learning based prediction for oncologic outcomes of renal ... - Nature.com

In a new study, North Carolina State University researchers had 28 high school students create their own machine-learning artificial intelligence (AI) models for analyzing data. The goals of the project were to help students explore the challenges, limitations and promise of AI, and to ensure a future workforce is prepared to make use of AI tools.

The study was conducted in conjunction with a high school journalism class in the Northeast. Since then, researchers have expanded the program to high school classrooms in multiple states, including North Carolina. NCState researchers are looking to partner with additional schools to collaborate in bringing the curriculum into classrooms.

We want students, from a very young age, to open up that black box so they arent afraid of AI, said the studys lead author Shiyan Jiang, assistant professor of learning design and technology at NCState. We want students to know the potential and challenges of AI, and so they think about how they, the next generation, can respond to the evolving role of AI and society. We want to prepare students for the future workforce.

For the study, researchers developed a computer program called StoryQ that allows students to build their own machine-learning models. Then, researchers hosted a teacher workshop about the machine learning curriculum and technology in one-and-a-half hour sessions each week for a month. For teachers who signed up to participate further, researchers did another recap of the curriculum for participating teachers, and worked out logistics.

We created the StoryQ technology to allow students in high school or undergraduate classrooms to build what we call text classification models, Jiang said. We wanted to lower the barriers so students can really know whats going on in machine-learning, instead of struggling with the coding. So we created StoryQ, a tool that allows students to understand the nuances in building machine-learning and text classification models.

A teacher who decided to participate led a journalism class through a 15-day lesson where they used StoryQ to evaluate a series of Yelp reviews about ice cream stores. Students developed models to predict if reviews were positive or negative based on the language.

The teacher saw the relevance of the program to journalism, Jiang said. This was a very diverse class with many students who are under-represented in STEM and in computing. Overall, we found students enjoyed the lessons a lot, and had great discussions about the use and mechanism of machine-learning.

Researchers saw that students made hypotheses about specific words in the Yelp reviews, which they thought would predict if a review would be positive, or negative. For example, they expected reviews containing the word like to be positive. Then, the teacher guided the students to analyze whether their models correctly classified reviews. For example, a student who used the word like to predict reviews found that more than half of reviews containing the word were actually negative. Then, researchers said students used trial and error to try to improve the accuracy of their models.

Students learned how these models make decisions, and the role that humans can play in creating these technologies, and the kind of perspectives that can be brought in when they create AI technology, Jiang said.

From their discussions, researchers found that students had mixed reactions to AI technologies. Students were deeply concerned, for example, about the potential to use AI to automate processes for selecting students or candidates for opportunities like scholarships or programs.

For future classes, researchers created a shorter, five-hour program. Theyve launched the program in two high schools in North Carolina, as well as schools in Georgia, Maryland and Massachusetts. In the next phase of their research, they are looking to study how teachers across disciplines collaborate to launch an AI-focused program and create a community of AI learning.

We want to expand the implementation in North Carolina, Jiang said. If there are any schools interested, we are always ready to bring this program to a school. Since we know teachers are super busy, were offering a shorter professional development course, and we also provide a stipend for teachers. We will go into the classroom to teach if needed, or demonstrate how we would teach the curriculum so teachers can replicate, adapt, and revise it. We will support teachers in all the ways we can.

The study, High school students data modeling practices and processes: From modeling unstructured data to evaluating automated decisions, was published online March 13 in the journal Learning, Media and Technology. Co-authors included Hengtao Tang, Cansu Tatar, Carolyn P. Ros and Jie Chao. The work was supported by the National Science Foundation under grant number 1949110.

-oleniacz-

Note to Editors: The study abstract follows.

High school students data modeling practices and processes: From modeling unstructured data to evaluating automated decisions

Authors: Shiyan Jiang, Hengtao Tang, Cansu Tatar, Carolyn P. Ros and Jie Chao.

Published: March 13, 2023, Learning, Media and Technology

DOI: 10.1080/17439884.2023.2189735

Abstract: Its critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies.

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Students Use Machine Learning in Lesson Designed to Reveal ... - NC State News

With quantum machine learning, the internet of things can become even more powerful, enabling people to create more efficient and safer systems.

FREMONT, CA: The Internet of Things (IoT) is altering how people interact with their surrounding environment. From intelligent homes to autonomous vehicles, the possibilities are limitless. Researchers are investigating the possibility of merging IoT with quantum machine learning (QML) to create even more powerful and efficient systems.

QML is an artificial intelligence (AI) that processes data using quantum computing. It offers the ability to provide quicker and more precise decision-making than conventional AI. Researchers hope to create a potent new data analysis and prediction tool by merging it with the IoT.

QML and IoT could be combined to create smarter, more efficient systems for various applications. For instance, it might optimize city traffic flow by forecasting traffic patterns and modifying traffic light timing accordingly. It could also be utilized to optimize building energy consumption and monitor and predict disease spread

IoT facilitates the huge potential of QML enabled by IoT. It could transform how people interact with the environment around them and create new opportunities for data analysis and forecasting. As researchers continue to investigate the possibilities, it is evident that this technology can alter the way of life.

Using the IoT to Advance QML

The IoT is altering how people interact with their surrounding environment. IoT technology's potential applications appear limitless, from intelligent homes to self-driving vehicles. Now, scientists are investigating how IoT can transform QML.

QML is a fast-developing research topic that blends quantum computing capabilities with machine learning methods. QML can enable robots to learn more effectively and precisely than ever before by harnessing the potential of quantum computing.

The IoT is ideally suited to supporting QML applications. IoT devices can collect and communicate vast quantities of data, which can be utilized to train and optimize machine learning algorithms. In addition, IoT devices can be used to monitor and control the environment in which QML algorithms are deployed, ensuring that they operate under optimal conditions.

Also, researchers are investigating how IoT devices might be leveraged to enhance the security of QML applications. IoT devices can identify and prevent harmful attacks on QML systems by harnessing the power of distributed networks. IoT devices can also be used to monitor the performance of QML algorithms, enabling the immediate identification and resolution of any problems.

The potential uses of the IoT for QML are vast, and researchers are just beginning to investigate them. By leveraging the power of the IoT, researchers are paving the way for a new era of QML that might transform how people interact with the world.

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Exploring the Possibilities of IoT-Enabled Quantum Machine Learning - CIOReview

Item 1.01. Entry into a Material Definitive Agreement.

On March 30, 2023, Travel + Leisure Co. (the "Borrower") entered into the FourthAmendment (the "Fourth Amendment" to the Credit Agreement, dated as of May 31,2018 with Bank of America, N.A., as administrative agent (the "AdministrativeAgent"), the several lenders from time to time party thereto, and the otherparties thereto (as amended, restated, amended and restated, supplemented orotherwise modified from time to time, the "Credit Agreement"). Pursuant to theterms of the Fourth Amendment, the Administrative Agent and the Borrower agreedto replace the London interbank offered rate-based interest rate applicable toborrowings under the Credit Agreement with a secured overnight financingrate-based interest rate, subject to the adjustments as specified in the FourthAmendment.

The description of the Fourth Amendment in this Current Report on Form 8-K (this"Current Report) is a summary and is qualified in its entirety by reference tothe complete terms of the Fourth Amendment included therein. The FourthAmendment is filed hereto as Exhibit 10.1 and is incorporated by referenceherein.

Item 2.03. Creation of a Direct Financial Obligation or an Obligation under anOff-Balance Sheet Arrangement of a Registrant.

The information set forth in Item 1.01 of this Current Report is incorporated byreference into this item.

Item 9.01. Financial Statements and Exhibits.

d) Exhibits. The following exhibit is furnished with this report:

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TRAVEL & LEISURE CO. : Entry into a Material Definitive Agreement, Creation of a Direct Financial Obligation or an Obligation under an Off-Balance...

In Puerto Rico, we must change the current view of some that capitalism is wrong for the island. To those that say so; in my opinion, view socialism as the equal sharing of resources, when in fact, it is the equal sharing of hunger, dependency, unemployment, inequality and misery as you will read below.

The more we read or watch news stories in Puerto Rico, the more we have become canvassed with a rant of rich against poor, of those who have succeeded against those who have not.

Many preeminent columnists have a leftist flair or inclination that borders into communism.

While, for one, we must respect each person's political views or tendencies, we must wonder to what extent these leftists, including press or media personalities, would venture to life and work in a place that lives their leftist views each and every day, nations like Cuba, Venezuela, Nicaragua, Russia, North Korea, China or Belarus.

Cuba

One of the closest examples and a favorite among local leftists is Cuba, and the sheer attractive nature of the nation makes us wonder why they have not moved there.

In Cuba, the average monthly salary equals $148.73 per month or $1,784.76 per year, which is more or less what an entry-level per-hour employee makes in a month.

Also, according to the website Reporters Without Borders, the Island nation of Cuba remains the worst country for press freedom in Latin America, with a rank of 173 out of 180, and is outranked by China, Iran and North Korea. The government closely monitors all television, radio and newspapers. The Constitution prohibits privately-owned press. All independent journalists are kept under surveillance to diminish their ability to perform their jobs.

Nicaragua

Another great example is Nicaragua; the average monthly salary equals $307.81 per month or $3,693.72 per year, which is more or less what a manager makes monthly.

Since President Daniel Ortega came to power, the independent media has endured censorship, intimidation and threats. Journalists are constantly stigmatized and subjected to harassment campaigns, arbitrary arrests and death threats and remain among the worst countries for press freedom in Latin America, with a rank of 160 out of 180. Most of the best journalists have had to flee the country. There are practically no independent media within the country due to a strong wave of repression that the Daniel Ortega regime launched against opposition politicians, civil organizations, and independent media. The media that continues to report on government abuses are digital, with most of its journalists in exile.

Venezuela

The last example is Venezuela; the average monthly salary equals $53 per month or $636 per year, which is more or less what many workers make here in a week.

After the arrival of Nicols Maduro in 2013, government policies against pluralism in the media increased; the official monopoly on the imports of paper and printing supplies resulted in the disappearance of the printed editions of dozens of newspapers, remains one of the worst countries for press freedom in Latin America, with a rank of 159 out of 180. A blurred policy for granting or revoking concessions for radio broadcasting decimated the sector, with 200 radio stations closing. The Venezuelan government practices a sustained policy of blocking news content on the Internet, affecting all independent media portals. The leading independent media are Radio Fe y Alegra, Efecto Cocuyo, Unin Radio, El Estmulo, El Pitazo and El Diario.

Remembering The Bill of Rights

As we consider the liberties granted to us by the U.S. Constitution, we thought it prudent to remind ourselves of the Bill of Rights' power and some of its amendments.

1. First Amendment: Congress makes no law respecting an establishment of religion or prohibiting its free exercise. It protects our freedom of speech, the press, assembly, and the right to petition the Government to redress grievances.

2. Second Amendment gives citizens the right to bear arms.

3. Fourth Amendment protects citizens from unreasonable search and seizure. The Government may not conduct any searches without a warrant, which must be issued by a judge based on probable cause.

4. Fifth Amendment provides that citizens not be subject to criminal prosecution and punishment without due process. A citizen may not be tried on the same set of facts twice and is protected from self-incrimination (the right to remain silent). The Amendment also establishes the power of an eminent domain, ensuring that private property is not seized for public use without just compensation.

In the nations we described, none of these rights protect their citizens, much less the press. We often wonder how easy it must be to have a leftist or socialistic bias in a nation that protects your rights as a citizen or member of the press.

Moreover, the columns or radio programs many of these leftist bias media members have would not be possible under the leftist's regime they so vehemently venerate, respect, and highlight. Instead, if not all had they been living and working in Cuba, Venezuela or Nicaragua by now, they would have left for the United States seeking political asylum to be protected by the Bill of Rights every Puerto Rico, USA citizen enjoys.

Sadly more and more these days, we read or watch programs where reporters disdain successful entrepreneurs, calling most of us "Colmillus" or "Bourgeoisie."

Too often, most speak as if we lived on two different islands when we are one country; we share the same soil and dreams and face the same problems and challenges.

Puerto Rico is a mix of groups that coexists and intertwines. We are all parts of the puzzle in which we must seek solutions to create sustained development and economic growth for Puerto Rico. We must ever forget that each sector is the strength of the other, so it is our responsibility as Puerto Ricans to work together.

Transforming Puerto Rico is the Key

For more than 20 years, my life's work has been to promote Puerto Rico's transformation into a sustained growing economy with ample opportunities for all citizens to develop their future, whether as an entrepreneur, teacher, chef, plumber, electrician, nurse, doctor, or business owner. The Transforming Puerto Rico Foundation has developed the Puerto Rico First Goals, which are the basis for Puerto Ricos transformation.

Goal 1: Transform Puerto Rico into a country with robust economic development and sustained 4% growth over the next ten years.

Goal 2: Transform our industrial structure into one in which employment in activities related to a knowledge-based economy with not less than 25% within ten years.

Goal 3: Create 300,000 new jobs in the private sector within ten years.

Goal 4: Increase the labor participation rate to 55% within ten years.

Goal 5: Reduce the unemployment rate to 5% within ten years.

Goal 6: Close the development gap; the gap is created by the percentage of GNP that represents consumption, and the rate that represents the investment, in Puerto Rico far exceeds that of our peers.

Goal 7: Reduce the government apparatus by transferring to the private sector any corporation, operation, or service that the private sector can perform more efficiently- by moving to a governance structure that is characterized by the following: employing no more than 15% of the employed workforce and a Consolidated Budget that does not exceed 25% of GNP.

Goal 8: Transform the education system from primary to university level into one focused on entrepreneurship, trades, and transformation.

Remember that "transformations are marathons, not 100-meter races." To accomplish them, we have to work together without losing sight of the fact that the role of the private sector is vital. We cannot forget that the private sector is made up of the cashier, the construction worker, the office worker, the clerk, the nurse, and yes, also the engineer, the doctor, and the businessman.

Indeed, the private sector represents the backbone that supports the economy. This puzzle represents 80% of the country's labor force, with over one million workers and a payroll of $31 billion annually. The private sector works hand in hand with municipalities, non-profit entities and also makes up 83% of the economy's total income.

Undoubtedly, there is a fair perception of what some call the big interests or "grandes intereses" in Spanish, and yes, we all create jobs and risk our capital every day to have a genuine "big interest" in making Puerto Rico the best place to work and live in the world.

In conclusion, Sir Winston Churchill said it best in a speech October 22, 1945 in the House of Commons, saying, "The inherent vice of capitalism is to distribute benefits unequally. The inherent virtue of Socialism is the equal sharing of Misery."

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Socialism and the Equal Sharing of Misery | Business ... - The Weekly Journal