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Are you leading an organization that has a large campus, e.g., a large university? You are probably thinking of introducing an electric scooter/bicycle fleet on the campus, and why wouldn’t you?
Introducing micro-mobility in your campus with the help of such a fleet would help the people on the campus significantly. People would save money since they don’t need to use a car for a short distance. Your campus will see a drastic reduction in congestion, moreover, its carbon footprint will reduce.
Micro-mobility is relatively new though and you would need help. You would need to select an appropriate fleet of vehicles. The people on your campus would need to find electric scooters or electric bikes for commuting, and you need to provide a solution for this.
To be more specific, you need a short-term electric bike rental app. With such an app, you will be able to easily offer micro-mobility to the people on the campus. We at Devathon have built Autorent exactly for this.
What does Autorent do and how can it help you? How does it enable you to introduce micro-mobility on your campus? We explain these in this article, however, we will touch upon a few basics first.
You are probably thinking about micro-mobility relatively recently, aren’t you? A few relevant insights about it could help you to better appreciate its importance.
Micro-mobility is a new trend in transportation, and it uses vehicles that are considerably smaller than cars. Electric scooters (e-scooters) and electric bikes (e-bikes) are the most popular forms of micro-mobility, however, there are also e-unicycles and e-skateboards.
You might have already seen e-scooters, which are kick scooters that come with a motor. Thanks to its motor, an e-scooter can achieve a speed of up to 20 km/h. On the other hand, e-bikes are popular in China and Japan, and they come with a motor, and you can reach a speed of 40 km/h.
You obviously can’t use these vehicles for very long commutes, however, what if you need to travel a short distance? Even if you have a reasonable public transport facility in the city, it might not cover the route you need to take. Take the example of a large university campus. Such a campus is often at a considerable distance from the central business district of the city where it’s located. While public transport facilities may serve the central business district, they wouldn’t serve this large campus. Currently, many people drive their cars even for short distances.
As you know, that brings its own set of challenges. Vehicular traffic adds significantly to pollution, moreover, finding a parking spot can be hard in crowded urban districts.
Well, you can reduce your carbon footprint if you use an electric car. However, electric cars are still new, and many countries are still building the necessary infrastructure for them. Your large campus might not have the necessary infrastructure for them either. Presently, electric cars don’t represent a viable option in most geographies.
As a result, you need to buy and maintain a car even if your commute is short. In addition to dealing with parking problems, you need to spend significantly on your car.
All of these factors have combined to make people sit up and think seriously about cars. Many people are now seriously considering whether a car is really the best option even if they have to commute only a short distance.
This is where micro-mobility enters the picture. When you commute a short distance regularly, e-scooters or e-bikes are viable options. You limit your carbon footprints and you cut costs!
Businesses have seen this shift in thinking, and e-scooter companies like Lime and Bird have entered this field in a big way. They let you rent e-scooters by the minute. On the other hand, start-ups like Jump and Lyft have entered the e-bike market.
Think of your campus now! The people there might need to travel short distances within the campus, and e-scooters can really help them.
What advantages can you get from micro-mobility? Let’s take a deeper look into this question.
Micro-mobility can offer several advantages to the people on your campus, e.g.:
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The electric scooter revolution has caught on super-fast taking many cities across the globe by storm. eScooters, a renovated version of old-school scooters now turned into electric vehicles are an environmentally friendly solution to current on-demand commute problems. They work on engines, like cars, enabling short traveling distances without hassle. The result is that these groundbreaking electric machines can now provide faster transport for less — cheaper than Uber and faster than Metro.
Since they are durable, fast, easy to operate and maintain, and are more convenient to park compared to four-wheelers, the eScooters trend has and continues to spike interest as a promising growth area. Several companies and universities are increasingly setting up shop to provide eScooter services realizing a would-be profitable business model and a ready customer base that is university students or residents in need of faster and cheap travel going about their business in school, town, and other surrounding areas.
In many countries including the U.S., Canada, Mexico, U.K., Germany, France, China, Japan, India, Brazil and Mexico and more, a growing number of eScooter users both locals and tourists can now be seen effortlessly passing lines of drivers stuck in the endless and unmoving traffic.
A recent report by McKinsey revealed that the E-Scooter industry will be worth― $200 billion to $300 billion in the United States, $100 billion to $150 billion in Europe, and $30 billion to $50 billion in China in 2030. The e-Scooter revenue model will also spike and is projected to rise by more than 20% amounting to approximately $5 billion.
And, with a necessity to move people away from high carbon prints, traffic and congestion issues brought about by car-centric transport systems in cities, more and more city planners are developing more bike/scooter lanes and adopting zero-emission plans. This is the force behind the booming electric scooter market and the numbers will only go higher and higher.
Companies that have taken advantage of the growing eScooter trend develop an appthat allows them to provide efficient eScooter services. Such an app enables them to be able to locate bike pick-up and drop points through fully integrated google maps.
It’s clear that e scooters will increasingly become more common and the e-scooter business model will continue to grab the attention of manufacturers, investors, entrepreneurs. All this should go ahead with a quest to know what are some of the best electric bikes in the market especially for anyone who would want to get started in the electric bikes/scooters rental business.
We have done a comprehensive list of the best electric bikes! Each bike has been reviewed in depth and includes a full list of specs and a photo.
To start us off is the Billy eBike, a powerful go-anywhere urban electric bike that’s specially designed to offer an exciting ride like no other whether you want to ride to the grocery store, cafe, work or school. The Billy eBike comes in 4 color options – Billy Blue, Polished aluminium, Artic white, and Stealth black.
Available in the USA, Europe, Asia, South Africa and Australia.This item ships from the USA. Buyers are therefore responsible for any taxes and/or customs duties incurred once it arrives in your country.
Why Should You Buy This?
**Who Should Ride Billy? **
Both new and experienced riders
**Where to Buy? **Local distributors or ships from the USA.
Featuring a sleek and lightweight aluminum frame design, the 200-Series ebike takes your riding experience to greater heights. Available in both black and white this ebike comes with a connected app, which allows you to plan activities, map distances and routes while also allowing connections with fellow riders.
The Genze 200 series e-Bike is available at GenZe retail locations across the U.S or online via GenZe.com website. Customers from outside the US can ship the product while incurring the relevant charges.
The Norco VLT S2 is a front suspension e-Bike with solid components alongside the reliable Bosch Performance Line Power systems that offer precise pedal assistance during any riding situation.
This item is available via the various Norco bikes international distributors.
Manufactured by Bodo Vehicle Group Limited, the Bodo EV is specially designed for strong power and extraordinary long service to facilitate super amazing rides. The Bodo Vehicle Company is a striking top in electric vehicles brand field in China and across the globe. Their Bodo EV will no doubt provide your riders with high-level riding satisfaction owing to its high-quality design, strength, breaking stability and speed.
This item ships from China with buyers bearing the shipping costs and other variables prior to delivery.
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With more of us using smartphones, the popularity of mobile applications has exploded. In the digital era, the number of people looking for products and services online is growing rapidly. Smartphone owners look for mobile applications that give them quick access to companies’ products and services. As a result, mobile apps provide customers with a lot of benefits in just one device.
Likewise, companies use mobile apps to increase customer loyalty and improve their services. Mobile Developers are in high demand as companies use apps not only to create brand awareness but also to gather information. For that reason, mobile apps are used as tools to collect valuable data from customers to help companies improve their offer.
There are many types of mobile applications, each with its own advantages. For example, native apps perform better, while web apps don’t need to be customized for the platform or operating system (OS). Likewise, hybrid apps provide users with comfortable user experience. However, you may be wondering how long it takes to develop an app.
To give you an idea of how long the app development process takes, here’s a short guide.
_Average time spent: two to five weeks _
This is the initial stage and a crucial step in setting the project in the right direction. In this stage, you brainstorm ideas and select the best one. Apart from that, you’ll need to do some research to see if your idea is viable. Remember that coming up with an idea is easy; the hard part is to make it a reality.
All your ideas may seem viable, but you still have to run some tests to keep it as real as possible. For that reason, when Web Developers are building a web app, they analyze the available ideas to see which one is the best match for the targeted audience.
Targeting the right audience is crucial when you are developing an app. It saves time when shaping the app in the right direction as you have a clear set of objectives. Likewise, analyzing how the app affects the market is essential. During the research process, App Developers must gather information about potential competitors and threats. This helps the app owners develop strategies to tackle difficulties that come up after the launch.
The research process can take several weeks, but it determines how successful your app can be. For that reason, you must take your time to know all the weaknesses and strengths of the competitors, possible app strategies, and targeted audience.
The outcomes of this stage are app prototypes and the minimum feasible product.
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As COVID-19 staggeringly lands blows to nations across the world, governments are considering ways to see their citizens through this pandemic. At the moment, a WHO situation report clocks the number of confirmed cases above two million along with more than one hundred thousand deaths. With vaccines dubbed as the best possible chance to tackle COVID-19 having no precise time frame of being ready, the talk is quickly shifting away to Contact Tracing Applications.
Contact tracing apps are digital solutions that use mobile technology to power the process of manual contact tracing. The apps follow a user’s movement, either by the use of Bluetooth technology, QR codes, or geo-location data while also tracking and keeping data from other user phones nearby. If one user gets diagnosed, the apps alert other users that they may have been exposed to the virus. As such, Contact Tracing Applications are being welcomed and perceived as an important approach to stem the spread of COVID-19 by providing a more accurate platform with data and information about affected individuals.
As mentioned above, contact tracing apps leverage mobile technology to trace cases of possible infection more accurately. But how exactly? Once installed and operative, the phone runs the app simultaneously with Bluetooth or location data to transmit signals with unique keys or IDs to phones in the designated range of connection. Similarly, the other phones with the app installed to detect and send back the signals.
For instance, if ‘Individual A’ has the app installed and goes outdoors to run some errands, they will interact with other individuals. In such a case, supposing all the other individuals had functional Contact Tracing Apps, each phone would exchange and store the contact data anonymously. It is important to note that the data collected only covers the app range distance to disregard irrelevant contacts and that their keys repeatedly change as individuals move. In any event that ‘Individual A’ tests positive for COVID-19 through confirmed tests, users who were previously within the proximity of ‘Individual A’ are alerted. Consequently, they are notified to check for symptoms, self-isolate, or get tested. Each time a person tests positive, the app notifies and advises the affected individuals.
In a nutshell, Contact Tracing Apps automate and supplement the traditional concept of tracing contacts to achieve extensive and realistic results in the least time possible.
Contract Tracing Apps are assets that offer indispensable solutions to health institutions and the public against COVID-19. There are several reasons why many governments are urging their citizens to use digital contact tracing apps to combat the spread of COVID-19. They include:
Currently, the role of contact tracing apps is limited to accurately identifying infected individuals and their contacts as well as facilitating a quicker response to the Covid-19 threat.
Beyond that, the use of contact tracing apps is projected to take a different turn. One key area bound to change is how people’s privacy is handled. Tech institutions are under growing pressure to devise ways to develop privacy-preserving Contact Tracing Apps.
This will earn the users-trust, which is a pillar for these apps to help contain the disease. Technically, the technology will also have to improve drastically. The apps will have to seamlessly integrate with the user’s phone lifestyle causing minimal or no interference. With most applications having an open-source code, Artificial Intelligence, Beacon Technology, and Big Data solutions will be increasingly harnessed to power and improve them. The apps may also cut across various types of industries apart from health institutions.
Contact Tracing Apps will effectively help stem lowering the cases of COVID-19. By using the apps, officials are able to monitor high-risk individuals easily. Also, should any new case arise, both users and health officials get notified they will swiftly act to trace, test, or isolate infected individuals.
Unlike traditional contact tracing, which may not get all contacts, these apps ensure that once Covid-19 cases are detected, they are all treated early, and those other individuals are not exposed to the infection. They also ward off users from high-risk areas. In the long run, they help break the COVID-19 chain by preventing further spread. Illustratively, an online publication by CNBC states that more than 500,000 using a Singapore-registered mobile number downloaded the TraceTogether app within the first 24 hours of its launch. Subsequently, together with other government efforts, Singapore has since lowered the infection rate and eased restrictions.
If Contact Tracing Apps are implemented and used alongside other policies, we may as well be a few steps way to curbing this virus.
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** DEPRECATED **
This repo has been deprecated. Please visit Megatron-LM for our up to date Large-scale unsupervised pretraining and finetuning code.
If you would still like to use this codebase, see our tagged releases and install required software/dependencies that was available publicly at that date.
PyTorch Unsupervised Sentiment Discovery
This codebase contains pretrained binary sentiment and multimodel emotion classification models as well as code to reproduce results from our series of large scale pretraining + transfer NLP papers: Large Scale Language Modeling: Converging on 40GB of Text in Four Hours and Practical Text Classification With Large Pre-Trained Language Models. This effort was born out of a desire to reproduce, analyze, and scale the Generating Reviews and Discovering Sentiment paper from OpenAI.
The techniques used in this repository are general purpose and our easy to use command line interface can be used to train state of the art classification models on your own difficult classification datasets.
This codebase supports mixed precision training as well as distributed, multi-gpu, multi-node training for language models (support is provided based on the NVIDIA APEx project). In addition to training language models, this codebase can be used to easily transfer and finetune trained models on custom text classification datasets.
For example, a Transformer language model for unsupervised modeling of large text datasets, such as the amazon-review dataset, is implemented in PyTorch. We also support other tokenization methods, such as character or sentencepiece tokenization, and language models using various recurrent architectures.
The learned language model can be transferred to other natural language processing (NLP) tasks where it is used to featurize text samples. The featurizations provide a strong initialization point for discriminative language tasks, and allow for competitive task performance given only a few labeled samples. For example, we consider finetuning our models on the difficult task of multimodal emotion classification based on a subset of the plutchik wheel of emotions.
Created by Robert Plutchik, this wheel is used to illustrate different emotions in a compelling and nuanced way. He suggested that there are 8 primary bipolar emotions (joy versus sadness, anger versus fear, trust versus disgust, and surprise versus anticipation) with different levels of emotional intensity. For our classification task we utilize tweets from the SemEval2018 Task 1E-c emotion classification dataset to perform multilabel classification of anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. This is a difficult task that suffers from real world classification problems such as class imbalance and labeler disagreement.
On the full SemEval emotion classification dataset we find that finetuning our model on the data achieves competitive state of the art performance with no additional domain-specific feature engineering.
Install the sentiment_discovery package with
python3 setup.py install in order to run the modules/scripts within this repo.
At this time we only support python3.
We've included our sentencepiece tokenizer model and vocab as a zip file:
We've included a transformer language model base as well as a 4096-d mlstm language model base. For examples on how to use these models please see our finetuning and transfer sections. Even though these models were trained with FP16 they can be used in FP32 training/inference.
We've also included classifiers trained on a subset of SemEval emotions corresponding to the 8 plutchik emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust):
Lastly, we've also included already trained classification models for SST and IMDB binary sentiment classification:
To use classification models that reproduce results from our original large batch language modeling paper please use the following commit hash and set of models.
We did not include pretrained models leveraging ELMo. To reproduce our papers' results with ELMo, please see our available resources.
Each file has a dictionary containing a PyTorch
state_dict consisting of a language model (lm_encoder keys) trained on Amazon reviews and a classifier (classifier key) as well as accompanying
args necessary to run a model with that
./data folder we've provided processed copies of the Binary Stanford Sentiment Treebank (Binary SST), IMDB Movie Review, and the SemEval2018 Tweet Emotion datasets as part of this repository. In order to train on the amazon dataset please download the "aggressively deduplicated data" version from Julian McAuley's original site. Access requests to the dataset should be approved instantly. While using the dataset make sure to load it with the
In addition to providing easily reusable code of the core functionalities (models, distributed, fp16, etc.) of this work, we also provide scripts to perform the high-level functionalities of the original paper:
Classify an input csv/json using one of our pretrained models or your own. Performs classification on Binary SST by default. Output classification probabilities are saved to a
python3 run_classifier.py --load_model ama_sst.pt # classify Binary SST python3 run_classifier.py --load_model ama_sst_16.pt --fp16 # run classification in fp16 python3 run_classifier.py --load_model ama_sst.pt --text-key <text-column> --data <path.csv> # classify your own dataset
See here for more documentation.
Train a language model on a csv/json corpus. By default we train a weight-normalized, 4096-d mLSTM, with a 64-d character embedding. This is the first step of a 2-step process to training your own sentiment classifier. Saves model to
lang_model.pt by default.
python3 pretrain.py #train a large model on imdb python3 pretrain.py --model LSTM --nhid 512 #train a small LSTM instead python3 pretrain.py --fp16 --dynamic-loss-scale #train a model with fp16 python3 -m multiproc pretrain.py #distributed model training python3 pretrain.py --data ./data/amazon/reviews.json --lazy --loose-json \ #train a model on amazon data --text-key reviewText --label-key overall --optim Adam --split 1000,1,1 python3 pretrain.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \ #train a model with our sentencepiece tokenization --tokenizer-type bpe --tokenizer-path ama_32k_tokenizer.model python3 pretrain.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \ #train a transformer model with our sentencepiece tokenization --tokenizer-type bpe --tokenizer-path ama_32k_tokenizer.model --model transformer \ --decoder-layers 12 --decoder-embed-dim 768 --decoder-ffn-embed-dim 3072 \ --decoder-learned-pos --decoder-attention-heads 8 bash ./experiments/train_mlstm_singlenode.sh #run our mLSTM training script on 1 DGX-1V bash ./experiments/train_transformer_singlenode.sh #run our transformer training script on 1 DGX-1V
For more documentation of our language modeling functionality look here
In order to learn about our language modeling experiments and reproduce results see the training reproduction section in analysis.
For information about how we achieve numerical stability with FP16 training see our fp16 training analysis.
Given a trained language model, this script will featurize text from train, val, and test csv/json's. It then uses sklearn logistic regression to fit a classifier to predict sentiment from these features. Lastly it performs feature selection to try and fit a regression model to the top n most relevant neurons (features). By default only one neuron is used for this second regression.
python3 transfer.py --load mlstm.pt #performs transfer to SST, saves results to `<model>_transfer/` directory python3 transfer.py --load mlstm.pt --neurons 5 #use 5 neurons for the second regression python3 transfer.py --load mlstm.pt --fp16 #run model in fp16 for featurization step bash ./experiments/run_sk_sst.sh #run transfer learning with mlstm on imdb dataset bash ./experiments/run_sk_imdb.sh #run transfer learning with mlstm on sst dataset
Additional documentation of the command line arguments available for transfer can be found here
Given a trained language model and classification dataset, this script will build a classifier that leverages the trained language model as a text feature encoder. The difference between this script and
transfer.py is that the model training is performed end to end: the loss from the classifier is backpropagated into the language model encoder as well. This script allows one to build more complex classification models, metrics, and loss functions than
transfer.py. This script supports building arbitrary multilable, multilayer, and multihead perceptron classifiers. Additionally it allows using language modeling as an auxiliary task loss during training and multihead variance as an auxiliary loss during training. Lastly this script supports automatically selecting classification thresholds from validation performance. To measure validation performance this script includes more complex metrics including: f1-score, mathew correlation coefficient, jaccard index, recall, precision, and accuracy.
python3 finetune_classifier.py --load mlstm.pt --lr 2e-5 --aux-lm-loss --aux-lm-loss-weight .02 #finetune mLSTM model on sst (default dataset) with auxiliary loss python3 finetune_classifier.py --load mlstm.pt --automatic-thresholding --threshold-metric f1 #finetune mLSTM model on sst and automatically select classification thresholds based on the validation f1 score python3 finetune_classifier.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \ #finetune transformer with sentencepiece on SST --tokenizer-type bpe tokenizer-path ama_32k_tokenizer.model --model transformer --lr 2e-5 \ --decoder-layers 12 --decoder-embed-dim 768 --decoder-ffn-embed-dim 3072 \ --decoder-learned-pos --decoder-attention-heads 8 --load transformer.pt --use-final-embed python3 finetune_classifier.py --automatic-thresholding --non-binary-cols l1 l2 l3 --lr 2e-5\ #finetune multilayer classifier with 3 classes and 4 heads per class on some custom dataset and automatically select classfication thresholds --classifier-hidden-layers 2048 1024 3 --heads-per-class 4 --aux-head-variance-loss-weight 1. #`aux-head-variance-loss-weight` is an auxiliary loss to increase the variance between each of the 4 head's weights --data <custom_train>.csv --val <custom_val>.csv --test <custom_test>.csv --load mlstm.pt bash ./experiments/se_transformer_multihead.sh #finetune a multihead transformer on 8 semeval categories
See how to reproduce our finetuning experiments in the finetuning reproduction section of analysis.
Additional documentation of the command line arguments available for
finetune_classifier.py can be found here
A special thanks to our amazing summer intern Neel Kant for all the work he did with transformers, tokenization, and pretraining+finetuning classification models.
A special thanks to @csarofeen and @Michael Carilli for their help developing and documenting our RNN interface, Distributed Data Parallel model, and fp16 optimizer. The latest versions of these utilities can be found at the APEx github page.
Thanks to @guillitte for providing a lightweight pytorch port of openai's sentiment-neuron repo.
This project uses the amazon review dataset collected by J. McAuley
Want to help out? Open up an issue with questions/suggestions or pull requests ranging from minor fixes to new functionality.
May your learning be Deep and Unsupervised.
License: View license