International Plumbing Code

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International Plumbing Code

March 15, 2022
mike@standardsmichigan.com

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The International Plumbing Code (IPC) is developed to harmonize with the full span of ICC’s family of building codes.  The IPC sets minimum regulations for plumbing systems and components to protect life, health and safety of building occupants and the public. The IPC is available for adoption by jurisdictions ranging from states to towns, and is currently adopted on the state or local level in 35 states in the U.S, the District of Columbia, Guam, and Puerto Rico.

CLICK HERE for the 2021 Public Access Edition 

SOURCE: CLICK ON IMAGE | Contact ICC for most recent IPC adoption map

 

The IPC is developed in the ICC Group A Code development framework and concluded its revision cycle in late 2021 under the circumstances of the pandemic.  The 2023 International Plumbing Code revision cycle will not begin until early 2023 but it is never too soon to understand the issues from previous revision cycles to enlighten approaches to the forthcoming Group A revision cycle.   The complete monograph of the Group A Codes is linked below, with comments on IPC proposals starting on Page 1417 of this 1613 page document:

2021 IPC | Group A Public Comment Monograph

Because transgender issues are on the agenda of many facility managers we direct you to Page 1424 of the rather large document linked above.

As always, we persist in encouraging education industry facility managers (especially those with operations and maintenance data) to participate in the ICC code development process.  You may do so by CLICKING HERE.

Real asset managers for school districts, colleges, universities and technical schools in the Las Vegas region should take advantage of the opportunity to observe the ICC code-development process during the upcoming ICC Annual Conference in Las Vegas, October 20-23 during which time the Group B c Public Comment Hearings will take place.  Even though the IPC has moved farther along the ICC code development process it is still enlightening to observe how it work.   The Group B Hearings are usually webcast — and we will signal the link to the webcast when it becomes available — but the experience of seeing how building codes are determined is enlightening when you can watch it live and on site.

Issue: [16-133]

Category: Plumbing, Water, Mechanical

Colleagues: Eric Albert, Richard Robben, Larry Spielvogel

#StandardsNewMexico

 


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March 14, 2022
mike@standardsmichigan.com
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pedagogue (n.) late 14c., pedagoge, “schoolmaster, teacher of children,” from Old French pedagoge “teacher of children” (14c.), from Latin paedagogus, from Greek paidagōgos “slave who escorts boys to school and generally supervises them,” later “a teacher or trainer of boys,” from pais (genitive paidos) “child” (see pedo-) + agōgos “leader,” from agein “to lead” (from PIE root *ag- “to drive, draw out or forth, move”).   Now used, generally with a sense of contempt, for a dogmatic and narrow-minded teacher” [Century Dictionary, 1895]; the hostile implications in the word are from at least the time of Pepys (1650s). Related: Pedagogal.

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March 11, 2022
mike@standardsmichigan.com
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Leveraging User-Provided Noisy Labels for Fashion Understanding

March 11, 2022
mike@standardsmichigan.com
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臺科大 國立臺灣科技大學

 

From Street Photos to Fashion Trends: Leveraging User-Provided Noisy Labels for Fashion Understanding

Fu-Hsien Huang –  Hsin-Min Lu –   Yao-Wen Hsu

Abstract: There is increased interest in using street photos to understand fashion trends. Though street photos usually contain rich clothing information, there are several technical challenges to their analysis. First, street photos collected from social media sites often contain user-provided noisy labels, and training models using these labels may deteriorate prediction performance. Second, most existing methods predict multiple clothing attributes individually and do not consider the potential to share knowledge between related tasks. In addition to these technical challenges, most fashion image datasets created by previous studies focus on American and European fashion styles. To address these technical challenges and understand fashion trends in Asia, we created RichWear, a new street fashion dataset containing 322,198 images with various text labels for fashion analysis. This dataset, collected from an Asian social network site, focuses on street styles in Japan and other Asian areas. RichWear provides a subset of expert-verified labels in addition to user-provided noisy labels for model training and evaluation. We propose the Fashion Attributes Recognition Network (FARNet) based on the multi-task learning framework to improve fashion recognition. Instead of predicting each clothing attribute individually, FARNet predicts three types of attributes simultaneously, and, once trained, this network leverages the noisy labels and generates corrected labels based on the input images. Experimental results show that this approach significantly outperforms existing methods. Applying the trained model to the RichWear dataset, we report Asian fashion trends and street styles based on predicted labels and image clusters from latent feature vectors.

 

 

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