This is a record-low for the appliance, dropping the price to $500 instead of the MSRP of $800. In the past, discounts for the j5 stalled at around $200 off. Just enter "ENGBF300" at checkout and you'll be good to go. This is a record-low price for the appliance. The Roomba Combo j5, as the name suggests, is a combination unit that includes both a vacuum and a mopping feature.

Booths, a high-end supermarket chain in northern England, has announced it is removing self-checkouts in the majority of its stores. The retailer said it was not a fan of the machines and prided itself on great customer service "and you can't do that through a robot". It is believed to be the first supermarket chain in the UK to return to fully-staffed tills, so the Guardian asked people for their views about self-checkouts. Here, four of them share their experiences of the machines and the effect they have on their supermarket shop. Self-checkouts are one of the things that rile me – if they're my only choice in a shop I feel really aggravated.

Online gradient descent (OGD) is well known to be doubly optimal under strong convexity or monotonicity assumptions: (1) in the single-agent setting, it achieves an optimal regret of $\Theta(\log T)$ for strongly convex cost functions; and (2) in the multi-agent setting of strongly monotone games, with each agent employing OGD, we obtain last-iterate convergence of the joint action to a unique Nash equilibrium at an optimal rate of $\Theta(\frac{1}{T})$. While these finite-time guarantees highlight its merits, OGD has the drawback that it requires knowing the strong convexity/monotonicity parameters. In this paper, we design a fully adaptive OGD algorithm, \textsf{AdaOGD}, that does not require a priori knowledge of these parameters. In the single-agent setting, our algorithm achieves $O(\log^2(T))$ regret under strong convexity, which is optimal up to a log factor. Further, if each agent employs \textsf{AdaOGD} in strongly monotone games, the joint action converges in a last-iterate sense to a unique Nash equilibrium at a rate of $O(\frac{\log^3 T}{T})$, again optimal up to log factors. We illustrate our algorithms in a learning version of the classical newsvendor problem, where due to lost sales, only (noisy) gradient feedback can be observed. Our results immediately yield the first feasible and near-optimal algorithm for both the single-retailer and multi-retailer settings. We also extend our results to the more general setting of exp-concave cost functions and games, using the online Newton step (ONS) algorithm.

Black Friday is fast approaching, and with it are more and more opportunities to get great devices for a steal. Take the Ring Video Doorbell and Echo Show 5 bundle, which is currently down to $65 from $190 -- a 65 percent sale. The Prime Member exclusive is available with the second-generation video doorbell in either Satin Nickel or Venetian Bronze. The bundle is currently 65 percent off. Amazon's Echo Show 5 allows you to easily see any motion Ring's video doorbell detects without always having your phone handy.

Stylistic headline generation is the task to generate a headline that not only summarizes the content of an article, but also reflects a desired style that attracts users. As style-specific article-headline pairs are scarce, previous researches focus on unsupervised approaches with a standard headline generation dataset and mono-style corpora. In this work, we follow this line and propose StyleBART, an unsupervised approach for stylistic headline generation. Our method decorates the pretrained BART model with adapters that are responsible for different styles and allows the generation of headlines with diverse styles by simply switching the adapters. Different from previous works, StyleBART separates the task of style learning and headline generation, making it possible to freely combine the base model and the style adapters during inference. We further propose an inverse paraphrasing task to enhance the style adapters. Extensive automatic and human evaluations show that StyleBART achieves new state-of-the-art performance in the unsupervised stylistic headline generation task, producing high-quality headlines with the desired style.

A supermarket chain has become Britain's first to return to fully-staffed checkouts after axing most of its self-service tills after its boss said: 'We like to talk to people.' Booths - which has 27 stores in the North across Lancashire, Cumbria, Yorkshire and Cheshire - has been finding the machines to be'slow, unreliable and impersonal' and decided that'rather than artificial intelligence, we're going for actual intelligence'. Staff at the upmarket firm, dubbed the'northern Waitrose', added that they wanted to ensure customers were served by people with'high levels of warm, personal care'. The move by Booths, which was founded in 1847, has provoked much debate on the benefits of self-checkouts as retailers continue to battle a shoplifting epidemic. The British Independent Retailers Association said there could be a'reality check with the current level of retail theft and self-service tills becoming an expensive risk'. All but two Booths stores will put staff back on the tills - with the exceptions being in the Lake District at Keswick and Windermere which can become very busy at times. Booths managing director Nigel Murray said staff at the northern chain'like to talk to people' Booths managing director Nigel Murray told BBC Radio Lancashire today: 'Our customers have told us this over time, that the self-scan machines that we've got in our stores they can be slow, they can be unreliable, they're obviously impersonal.

While deep learning (DL) models are state-of-the-art in text and image domains, they have not yet consistently outperformed Gradient Boosted Decision Trees (GBDTs) on tabular Learning-To-Rank (LTR) problems. Most of the recent performance gains attained by DL models in text and image tasks have used unsupervised pretraining, which exploits orders of magnitude more unlabeled data than labeled data. To the best of our knowledge, unsupervised pretraining has not been applied to the LTR problem, which often produces vast amounts of unlabeled data. In this work, we study whether unsupervised pretraining of deep models can improve LTR performance over GBDTs and other non-pretrained models. By incorporating simple design choices--including SimCLR-Rank, an LTR-specific pretraining loss--we produce pretrained deep learning models that consistently (across datasets) outperform GBDTs (and other non-pretrained rankers) in the case where there is more unlabeled data than labeled data. This performance improvement occurs not only on average but also on outlier queries. We base our empirical conclusions off of experiments on (1) public benchmark tabular LTR datasets, and (2) a large industry-scale proprietary ranking dataset. Code is provided at https://anonymous.4open.science/r/ltr-pretrain-0DAD/README.md.

The Apple Watch Series 9 is only a few months old, but it's already on sale. You can grab the smartwatch for $349 from Amazon or from Walmart as part of an early Black Friday deal. The standard price is $399, so that's a savings of $50 or 13 percent. The discount only applies to the 41mm model but includes multiple band and color options. The larger 45mm model is also on sale, but for $379.

We tackle the challenge of robotic bin packing with irregular objects, such as groceries. Given the diverse physical attributes of these objects and the complex constraints governing their placement and manipulation, employing preprogrammed strategies becomes unfeasible. Our approach is to learn directly from expert demonstrations in order to extract implicit task knowledge and strategies to ensure safe object positioning, efficient use of space, and the generation of human-like behaviors that enhance human-robot trust. We rely on human demonstrations to learn a Markov chain for predicting the object packing sequence for a given set of items and then compare it with human performance. Our experimental results show that the model outperforms human performance by generating sequence predictions that humans classify as human-like more frequently than human-generated sequences. The human demonstrations were collected using our proposed VR platform, BoxED, which is a box packaging environment for simulating real-world objects and scenarios for fast and streamlined data collection with the purpose of teaching robots. We collected data from 43 participants packing a total of 263 boxes with supermarket-like objects, yielding 4644 object manipulations. Our VR platform can be easily adapted to new scenarios and objects, and is publicly available, alongside our dataset, at https://github.com/andrejfsantos4/BoxED.

In this paper, we present a novel approach for fleet sizing in the context of flash delivery, a time-sensitive delivery service that requires the fulfilment of customer requests in minutes. Our approach effectively combines individual delivery requests into groups and generates optimized operational plans that can be executed by a single vehicle or autonomous robot. The groups are formed using a modified routing approach for the flash delivery problem. Combining the groups into operational plans is done by solving an integer linear problem. To evaluate the effectiveness of our approach, we compare it against three alternative methods: fixed vehicle routing, non-pooled deliveries and a strategy encouraging the pooling of requests. The results demonstrate the value of our proposed approach, showcasing its ability to optimize the fleet and improve operational efficiency. Our experimental analysis is based on a real-world dataset provided by a Dutch retailer, allowing us to gain valuable insights into the design of flash delivery operations and to analyze the effect of the maximum allowed delay, the number of stores to pick up goods from and the employed cost functions.