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Exclusive – Waymo, Cruise seek permits to charge for self-driving car rides in San Francisco

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By Hyunjoo Jin and Paresh Dave

BERKELEY, Calif. (Reuters) – Alphabet Inc’s Waymo and rival Cruise have applied for permits needed to start charging for rides and delivery using autonomous vehicles in San Francisco, state documents reviewed by Reuters showed, setting the stage for the biggest tests yet of their technology in a dense urban environment.

Neither company revealed when they intend to launch services. But they detailed contrasting deployment plans, with Waymo starting with “drivered operations” and Cruise expecting to deploy vehicles without humans behind the wheel.

California’s Department of Motor Vehicles (DMV) has yet to decide on the previously unreported applications made by Waymo on Jan. 19 and by Cruise on March 29, according to the documents. The agency had no immediate comment on Tuesday.

The efforts come at a turning point for Waymo, which Google launched over a decade ago. Waymo has given paid, driverless rides hailed through its app in suburban Chandler, Arizona, since 2019. But it has failed to scale up Arizona operations as quickly as analysts once envisioned.

Its longtime chief executive, John Krafcik, stepped down in April and was replaced by two co-CEOs.

Cruise, backed by General Motors Co, Honda Motor Co Ltd and SoftBank Group Corp, has focused on San Francisco since its beginning. It said in the permit application it has logged 2 million autonomous driving miles (3.22 million km) in the city. Waymo said it has had over 83,000 autonomous miles in its proposed deployment area, according to its application.

“You have a faster path to meaningful revenue in dense urban environments such as San Francisco than in the suburbs such as Chandler,” said Grayson Brulte, a consultant who advises companies around autonomous mobility strategies.

Waymo and Cruise could not immediately be reached for comment.

The companies would not be the first to obtain one of two permits required to operate robotaxis for hire in California. Silicon Valley startup Nuro in December became the only company to secure a DMV deployment permit. Nuro in March announced an unspecified investment from Chipotle Mexican Grill Inc, which said it was interested in new delivery systems.

A DMV official wrote to Nuro in March asking whether it intended to make deliveries for the fast-casual chain in California, records show. The results of a scheduled meeting on April 16 are unclear.

LIMITED OPERATION

If Waymo and Cruise secure DMV approval, they would next need a permit from the California Public Utilities Commission to begin charging passengers.

Until now, self-driving cars in San Francisco and Silicon Valley primarily have been used on a test basis even though the vehicles – with whirring lidar gear on their roofs – have become an increasingly common sight. Cruise and Waymo plan to maintain some limits during commercial operations as public concerns grow over the safety of self-driving systems.

Waymo said in its application it would have a safety driver in its hybrid Chrysler Pacifica minivans and all-electric Jaguar I-Pace SUVs. They would operate around the clock, offering rides or transporting goods on roadways with posted speed limits of up to 65 mph in San Francisco and in the northern part of bordering San Mateo County.

Waymo said it may switch off autonomous mode in specific areas such as freeway ramps and construction zones, or for heavy rain and wet roads.

Cruise said its service hours would be late evening to early morning with speeds of up to 30 mph, according to the documents.

When ready for commercial deployment, the company would receive $1.35 billion from SoftBank’s Vision Fund as part of an earlier investment agreement.

The timeline for revenue-generating deployment of self-driving vehicles has been pushed back repeatedly.

“It’s an incredibly difficult thing that we’re trying to solve for,” said Timothy Papandreou, a former Waymo employee who now leads consultancy Emerging Transport Advisors.

This year, Waymo also has been discussing collaborating with San Francisco transportation authorities and university labs to secure an unspecified U.S. Department of Energy grant “to investigate the use of AVs to provide first-/last-mile transit service” in San Francisco, according to public records seen by Reuters.

(Reporting by Paresh Dave in Oakland, Calif., and Hyunjoo Jin in Berkeley, Calif.; Additional reporting by Jane Lanhee Lee in Oakland, Calif.; Editing by Joe White and Matthew Lewis)

Image Credit: Reuters

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Source: https://datafloq.com/read/exclusive-waymo-cruise-seek-permits-charge-self-driving-car-rides-san-francisco/14585

Big Data

If you did not already know

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Multivariate Bayesian Model with Shrinkage Priors (MBSP) google


The method is described in Bai and Ghosh (2018) <arXiv:1711.07635>. …

Tensor Graphical Lasso (TeraLasso) google


The Bigraphical Lasso estimator was proposed to parsimoniously model the precision matrices of matrix-normal data based on the Cartesian product of graphs. By enforcing extreme sparsity (the number of parameters) and explicit structures on the precision matrix, this model has excellent potential for improving scalability of the computation and interpretability of complex data analysis. As a result, this model significantly reduces the size of the sample in order to learn the precision matrices, and hence the conditional probability models along different coordinates such as space, time and replicates. In this work, we extend the Bigraphical Lasso (BiGLasso) estimator to the TEnsor gRAphical Lasso (TeraLasso) estimator and propose an analogous method for modeling the precision matrix of tensor-valued data. We establish consistency for both the BiGLasso and TeraLasso estimators and obtain the rates of convergence in the operator and Frobenius norm for estimating the precision matrix. We design a scalable gradient descent method for solving the objective function and analyze the computational convergence rate, showing that the composite gradient descent algorithm is guaranteed to converge at a geometric rate to the global minimizer. Finally, we provide simulation evidence and analysis of a meteorological dataset, showing that we can recover graphical structures and estimate the precision matrices, as predicted by theory. …

Iterative Nonnegative Matrix Factorization (INOM) google


Matrix decomposition is ubiquitous and has applications in various fields like speech processing, data mining and image processing to name a few. Under matrix decomposition, nonnegative matrix factorization is used to decompose a nonnegative matrix into a product of two nonnegative matrices which gives some meaningful interpretation of the data. Thus, nonnegative matrix factorization has an edge over the other decomposition techniques. In this paper, we propose two novel iterative algorithms based on Majorization Minimization (MM)-in which we formulate a novel upper bound and minimize it to get a closed form solution at every iteration. Since the algorithms are based on MM, it is ensured that the proposed methods will be monotonic. The proposed algorithms differ in the updating approach of the two nonnegative matrices. The first algorithm-Iterative Nonnegative Matrix Factorization (INOM) sequentially updates the two nonnegative matrices while the second algorithm-Parallel Iterative Nonnegative Matrix Factorization (PARINOM) parallely updates them. We also prove that the proposed algorithms converge to the stationary point of the problem. Simulations were conducted to compare the proposed methods with the existing ones and was found that the proposed algorithms performs better than the existing ones in terms of computational speed and convergence. KeyWords: Nonnegative matrix factorization, Majorization Minimization, Big Data, Parallel, Multiplicative Update …

Joint and Progressive Learning strAtegY (J-Play) google


Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called textbf{j}oint and textbf{p}rogressive textbf{l}earning strtextbf{a}tegtextbf{y} (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods. …

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Source: https://analytixon.com/2021/06/20/if-you-did-not-already-know-1428/

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Exploring Mito: Automatic Python Code for SpreadSheet Operations

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Exploring Mito: Automatic Python Code for SpreadSheet Operations – Analytics Vidhya





















Learn everything about Analytics


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Source: https://www.analyticsvidhya.com/blog/2021/06/exploring-mito-automatic-python-code-for-spreadsheet-operations/

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Part- 4: Step by Step Guide to Master Natural Language Processing in Python

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Guide to Master Natural Language Processing -part 4 – Analytics Vidhya






















Learn everything about Analytics


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Source: https://www.analyticsvidhya.com/blog/2021/06/part-4-step-by-step-guide-to-master-natural-language-processing-in-python/

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How To Add Textual Watermarks To The Images With OpenCv and PIL!

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How To Add Textual Watermarks To The Images – Analytics Vidhya





















Learn everything about Analytics


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