Presenting a vision for a range of future electric delivery vehicles, each fully operational prototype features REE's revolutionary, modular and flexible platform architecture, which answers the exponential demand for entirely new types of EVs – particularly commercial vehicles – driven by the surge in e-commerce, a trend further accelerated by Covid-19. REE's EV platform consists of the REEcorner which powers full X-by-Wire technology for steering, braking and drive, and integrates all drivetrain, powertrain, suspension and steering components into the arch of the wheel. In addition, the breakthrough REEboard – which enables the EV platform to be completely flat offers customers the freedom to place any shape or size of body design on top. Daniel Barel, REE Co-Founder and CEO: "EVs, particularly e-delivery vehicles, are in huge demand, with growth drivers including global carbon-emission regulatory policies coupled with a boom e-commerce. There is also a rapid rise in'mobility as a service' (MaaS). Our modular platform is set to revolutionize electric mobility and as we shared today, the journey is well underway. Our platform provides the perfect blank canvas for our customers on which to build EVs tailored to their needs, whether it's a fully autonomous last-mile delivery vehicle, a spacious yet compact urban shuttle or a flexible delivery truck with higher load capability on a smaller footprint."

Amid a pandemic implosion, these startups showcase the strength, diversity and adaptability of America's entrepreneurs--and provide hope for the country's economic future. Even in the most challenging times, the best entrepreneurs find ways to excel. The 25 small companies listed here--all of which have less than $50 million in 2019 sales and fewer than 200 employees--are successfully navigating this turbulent year, even as some of their founders cope with personal losses from Covid-19. Some make things that are increasingly critical, such as software that improves hospital operations or robots that clean schools. Others have shifted to adapt to the pandemic, such as the extended-stay hotel operator using its rooms to house displaced international students and traveling doctors, or the maker of rolling buffets that started producing plexiglass dividers. These small-business standouts showcase the strength, adaptability and diversity of America's entrepreneurs, giving us hope for the country's economic future.

We have trained 12,000 professionals across India, UK, USA, Middle-East and Africa. Since 2014, Devu.in is the preferred destination for working professionals, looking at switching their career, looking at upskilling their career. Devu.in will be at the Python Development summit in Bengaluru next month. Contact us to know more about us. Devu.in will be at the Python Development summit in Bengaluru next month.

Ensembles of geophysical models improve prediction accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bayesian Neural Networks, which infers spatiotemporally varying model weights and bias, while accounting for heteroscedastic uncertainties in the observations. This produces more accurate and uncertaintyaware predictions without sacrificing interpretability. Applied to the prediction of total column ozone from an ensemble of 15 chemistry-climate models, we find that the Bayesian neural network ensemble (BayNNE) outperforms existing methods for ensembling physical models, achieving a 49.4% reduction in RMSE for temporal extrapolation, and a 67.4% reduction in RMSE for polar data voids, compared to a weighted mean. Uncertainty is also well-characterized, with 91.9% of the data points in our extrapolation validation dataset lying within 2 standard deviations and 98.9% within 3 standard deviations.

Existing approaches to federated learning suffer from a communication bottleneck as well as convergence issues due to sparse client participation. In this paper we introduce a novel algorithm, called FetchSGD, to overcome these challenges. FetchSGD compresses model updates using a Count Sketch, and then takes advantage of the mergeability of sketches to combine model updates from many workers. A key insight in the design of FetchSGD is that, because the Count Sketch is linear, momentum and error accumulation can both be carried out within the sketch. This allows the algorithm to move momentum and error accumulation from clients to the central aggregator, overcoming the challenges of sparse client participation while still achieving high compression rates and good convergence. We prove that FetchSGD has favorable convergence guarantees, and we demonstrate its empirical effectiveness by training two residual networks and a transformer model.

Many successful deep learning architectures are equivariant to certain transformations in order to conserve parameters and improve generalization: most famously, convolution layers are equivariant to shifts of the input. This approach only works when practitioners know the symmetries of the task and can manually construct an architecture with the corresponding equivariances. Our goal is an approach for learning equivariances from data, without needing to design custom task-specific architectures. We present a method for learning and encoding equivariances into networks by learning corresponding parameter sharing patterns from data. Our method can provably represent equivariance-inducing parameter sharing for any finite group of symmetry transformations. Our experiments suggest that it can automatically learn to encode equivariances to common transformations used in image processing tasks. We provide our experiment code at https://github.com/AllanYangZhou/metalearning-symmetries.

Noname manuscript No. (will be inserted by the editor) Abstract Bayesian Additive Regression Trees (BART) 1 Introduction is a tree-based machine learning method that has been successfully applied to regression and classification problems. Bayesian Additive Regression Trees (BART) is a statistical BART assumes regularisation priors on a set of method proposed by Chipman et al (2010) that has trees that work as weak learners and is very flexible for become popular in recent years due to its competitive predicting in the presence of non-linearity and highorder performance on regression and classification problems, interactions. In this paper, we introduce an extension when compared to other supervised machine learning of BART, called Model Trees BART (MOTR-methods, such as Random Forests (RF) (Breiman, 2001) BART), that considers piecewise linear functions at node and Gradient Boosting (GB) (Friedman, 2001). In MOTR-BART, differs from other tree-based methods as it controls the rather than having a unique value at node level for the structure of each tree via a prior distribution and generates prediction, a linear predictor is estimated considering the predictions via an MCMC backfitting algorithm the covariates that have been used as the split variables that is responsible for accepting and rejecting the in the corresponding tree. In our approach, local linearities proposed trees along the iterations.

Transformer models have garnered a lot of interest in recent years by delivering state-of-the-art performance in a range of Natural Language Processing (NLP) tasks. However, these models can have over a hundred billion parameters, presenting very high computational and memory requirements. We address this challenge through Approximate Computing, specifically targeting the use of Transformers in NLP tasks. Transformers are typically pre-trained and subsequently specialized for specific tasks through transfer learning. Based on the observation that pre-trained Transformers are often over-parameterized for several downstream NLP tasks, we propose a framework to create smaller, faster and in some cases more accurate models. The key cornerstones of the framework are a Significance Analysis (SA) method that identifies components in a pre-trained Transformer that are less significant for a given task, and techniques to approximate the less significant components. Our approximations include pruning of blocks, attention heads and weight groups, quantization of less significant weights and a low-complexity sign-matching based attention mechanism. Our framework can be adapted to produce models that are faster, smaller and/or more accurate, depending on the user's constraints. We apply our framework to seven Transformer models, including optimized models like DistilBERT and Q8BERT, and three downstream tasks. We demonstrate that our framework produces models that are up to 4 faster and up to 14 smaller (with less than 0.5% relative accuracy degradation), or up to 5.5% more accurate with simultaneous improvements of up to 9.83 in model size or 2.94 in speed. Transformer networks with hundreds of billions of parameters, such as T5 ([17]), Megatron ([22]), BERT ([2]), GPT-2 ( [16]) and GPT-3 ([1]), have achieved state-of-the-art performance in several Natural Language Processing tasks. Model sizes are expected to grow further in the future as increasing the number of parameters has been shown to improve performance.

We present a method for learning representations of entities, that uses a Transformer-based architecture as an entity encoder, and link prediction training on a knowledge graph with textual entity descriptions. We demonstrate that our approach can be applied effectively for link prediction in different inductive settings involving entities not seen during training, outperforming related state-of-the-art methods (22% MRR improvement on average). We provide evidence that the learned representations transfer to other tasks that do not require fine-tuning the entity encoder. In an entity classification task we obtain an average improvement of 16% accuracy compared with baselines that also employ pre-trained models. For an information retrieval task, significant improvements of up to 8.8% in NDCG@10 were obtained for natural language queries.

Over the next few years, society as a whole will need to address what core values it wishes to protect when dealing with technology. Anthropology, a field dedicated to the very notion of what it means to be human, can provide some interesting insights into how to cope and tackle these changes in our Western society and other areas of the world. It can be challenging for social science practitioners to grasp and keep up with the pace of technological innovation, with many being unfamiliar with the jargon of AI. This short guide serves as both an introduction to AI ethics and social science and anthropological perspectives on the development of AI. It intends to provide those unfamiliar with the field with an insight into the societal impact of AI systems and how, in turn, these systems can lead us to rethink how our world operates.