Show HN: Real-time multiplayer games with cubes. Early feedback on dev docs? https://ift.tt/2Nh8uoL

Show HN: Real-time multiplayer games with cubes. Early feedback on dev docs? https://ift.tt/3d5f5xt February 12, 2021 at 05:21AM

Show HN: EffectNode Studio, trying to make complex graphics code more manageable https://ift.tt/375Uvt0

Show HN: EffectNode Studio, trying to make complex graphics code more manageable https://ift.tt/3dctlnY February 12, 2021 at 04:27AM

Show HN: git-peek – git repo to local editor instantly https://ift.tt/3aRv9jG

Show HN: git-peek – git repo to local editor instantly https://ift.tt/2Z5EgI0 February 12, 2021 at 03:37AM

Geary Rapid Project Provides Safer Crossings

Geary Rapid Project Provides Safer Crossings
By Amy Fowler

A new traffic signal, crosswalk striping, pedestrian bulbs and streetlights help improve safety for people walking at Geary and Cook

The Geary Rapid Project may be best known for helping to make 38 Geary trips—well, more rapid. But, along with improving transit performance and reliability along a three-mile stretch of Geary, an equally important feature of the project is to make the streets safer.  

Geary is part of the high-injury network, and people walking there are eight times more likely to be involved in a serious or deadly collision than the average San Francisco street. Highway-like conditions on Geary Boulevard in the Western Addition make for challenging crossings. And the Tenderloin to the east, with its high concentration of children, seniors and people with disabilities, is uniquely vulnerable to traffic violence.

Fortunately, many of the safety improvements included in the Geary Rapid Project have already been completed, with more underway. Here are a few highlights:

• New crosswalks. Steiner Street has a new look since the non-ADA compliant and seldom-used pedestrian bridge was removed last May. The surface-level crosswalk on the west side of the intersection, where the bridge had been, has been improved with new median refuges, while the east side is sporting a newly activated crosswalk. At Webster, two new crosswalks were built across Geary to supplement the Japantown pedestrian bridge. When the final striping and decorative pavers are completed, the new crosswalks will be not only be safer, but easier on the eyes.
• New traffic signals. A new traffic signal at Cook Street was just turned on in January and another signal at the intersection of Commonwealth/Beaumont will be activated this spring, which means every crosswalk on Geary Boulevard as far west as 31st Avenue will benefit from signals.
• New pedestrian countdown signals. The Fillmore intersection, which had no pedestrian countdown signals crossing Geary, now has them at all four crosswalks. And Divisadero, Scott and Laguna streets also gained countdowns for people crossing along Geary, so now every signalized intersection on the Geary corridor—over five miles—has countdowns.
• Signal rephasing and “leading pedestrian intervals.” Re-timed traffic signals at the Masonic intersection added more time to cross the street and protection for westbound bicycles turning left. Other intersections, such as Scott Street, received upgrades to give people walking a head start before vehicles.
• New "pedestrian bulb-outs” between Stanyan and Market streets. These curb extensions shorten crossing distances for people walking and encourage vehicles making turns to slow down.
• Left turn safety. Left turns are one of the leading causes of traffic collisions. At Geary and Leavenworth, a protected left turn arrow was added for northbound traffic to separate people walking from turning vehicles.

Based on advocacy from local communities, we also added safety improvements that weren't envisioned in the initial Geary Rapid Project design. For example, a new traffic signal will be installed at Commonwealth Avenue. "The crosswalk on Geary at Beaumont and Commonwealth has been a dangerous intersection for many years," noted Tom Barton, a long-time Richmond resident who campaigned for the signal. "Having a crossing signal there will provide a safe way to cross, especially with heavy traffic, for myself and families, and other people crossing there."  

Last summer, following a tragic fatality at Geary and Gough, the SFMTA met with District Supervisors Preston and Stefani as well as local residents, who expressed a desire for additional safety measures such as reducing the speed limit near senior facilities. While existing state law makes it difficult to lower speed limits, the SFMTA was able to make use of a seldom-used exemption to reduce the speed limit on Geary from 35 to 25 mph on blocks near senior centers: between Gough and Laguna, Steiner and Scott, and Baker and Lyon streets.

Final safety improvements planned as part of the Geary Rapid Project include building a new signalized crosswalk at Buchanan Street, completing the remaining traffic signal upgrades and pedestrian bulb-outs and roadway restriping to “calm the Expressway.” Those are expected to be completed this summer.

For more information visit the Geary Rapid Project website.

Published February 12, 2021 at 03:47AM
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Show HN: Sleepy Time Conference—conferences that comes together while you sleep https://ift.tt/3rJ2h3V

Show HN: Sleepy Time Conference—conferences that comes together while you sleep https://ift.tt/3jNZ5RW February 12, 2021 at 03:20AM

Show HN: Nirvana.Work – Automated Task Scheduling – Put Sprints on Autopilot https://ift.tt/3rHYRhH

Show HN: Nirvana.Work – Automated Task Scheduling – Put Sprints on Autopilot https://nirvana.work February 11, 2021 at 10:38PM

Launch HN: Cord (YC W21) – training data toolbox for computer vision https://ift.tt/3jDuBlx

Launch HN: Cord (YC W21) – training data toolbox for computer vision Hey HN community - I’m Ulrik from Cord ( https://cord.tech ) in the current YC W21 batch [1] - we are building software that allows people to label their data intelligently using a toolbox of various ‘labeling algorithms’. Labeling algorithms are any units of intelligence (e.g. a pre-trained model, or an interpolation algorithm) that help automate the annotation process. This enables data science and machine learning teams to rapidly iterate on their ML models without having to farm out labeling tasks to an external workforce. Today we’re launching the first part of our product, our Web App, which serves our initial set of automation features through a GUI. It also allows you to classify images and draw vector labels, visualize data, and perform collaborative QA. Computer vision ML algorithms are widely used for tasks like detecting everyday objects such as cars and pedestrians. However, they are yet to see widespread adoption for things like detecting cancerous polyps during an endoscopic procedure or blood clots in MRI scans. The lack of massive-scale labeled training datasets that fuel contemporary approaches is often the blocking element in building ML applications that solve these more specialised tasks. We also believe that the core part of the IP of an ML application stems from the labeled data used to train it. Creating these datasets is challenging for several reasons. Labeling the data requires expensive domain-expert annotators, and privacy might prevent the data from being sent to an external workforce. Ultimately most labeling work tends to be done using open-source tools that were not created for speed and purpose-built to handle massive-scale datasets[2]. These tools also tend to provide a poor experience for the end consumer of the training data (e.g., data scientists, ML engineers) because they lack intelligence and require high manual input. The initial seed of the idea came while I was working on a CS master’s project of visualizing massive-scale medical image datasets. I saw saw how much time and effort was being spent by doctors on labeling data. I met my co-founder Eric, who had worked as a quant researcher in finance, and after meeting him we realized we could take an algorithmic approach to tackling the labeling problem. Instead of writing trading algorithms, we turned our focus to writing labeling algorithms. For example, for a food calorie estimation project we translated image level classifications of food items to individualized bounding box labels using a labeling algorithm we wrote with our SDK, requiring only one manual label per food item. Although it was an image dataset, our algorithm approximated noisy bounding box labels by using a CSRT object tracker across images. It then trained a shallow Faster RCNN ‘micro-model’ on the noisy labels, ran inference on the data, and suppressed earlier noisy labels. We then quickly visually reviewed and adjusted the results on our Web App[3]. We have applied a similar approach in areas such as gastroenterology[4] and pathology. The days of relying on an army of human annotators and waiting to start the model building process are hopefully (soon) over. We are incredibly excited to be driving for that change - and are delighted to be sharing Cord with the HN community! We would love to hear your feedback. How are you going about creating and managing training data today? What are your key constraints? If you have used a creative method to label your data before, please share. Thank you so much in advance! [1] What I Learned From My First Month at Y Combinator - https://ift.tt/374heFH... [2] Why You Should Ditch Your In-House Training Data Tools (And Avoid Building Your Own) - https://ift.tt/3rI5Mrl [3] Label a Dataset with a Few Lines of Code - https://ift.tt/372UCFE... [4] Pain Relief for Doctors Labelling Data - https://ift.tt/3jE73wE... February 11, 2021 at 10:36PM