Amazon's Big Spring Sale is nearly over and if you're interested in saving on tech, you've come to the right place. This sale event may not have been a boon for discounts on laptops, tablets, wearables and the like, but we were able to dig up a number of decent tech deals available right now. While most of these prices are not the same as those we saw during the Black Friday time period, some get pretty close (as a general rule of thumb, a good price in March isn't necessarily the same thing as a good price in November). As a reminder, the Big Spring Sale comes to a close at the end of the day March 25, so you have limited time left to shop these deals. Here are the best Amazon Spring Sale discounts on tech we love that you can get before the event ends. A single AirTag is on sale for 24, which is 5 off and close to its record low price. These are the best Bluetooth trackers for those with iOS devices since they use the vast Find My network to keep track of your belongings. You can force them to ring to help you find your stuff if you're within close range, and newer iPhones can even display directions on their screens to guide you to your lost stuff. Just make sure to pick up a holder if you intend on attaching the AirTag to your keys.

The Amazon Spring Sale is here and if you're interested in tech deals, you've come to the right place. However, don't mistake this for a spring Prime Day -- unlike Amazon's bigger, traditional sale events, this one doesn't revolve around Prime-exclusive discounts. And that's a good thing; that means anyone who shops on Amazon can take advantage of the deals. Given the seasonal nature of this event, it's not a boon for discounts on laptops, tablets, wearables and the like. However, we were able to find a number of decent discounts worth your time and money. While most of these Amazon deal prices are not the same as those we saw around Black Friday last year, some get pretty close (as a general rule of thumb, a good price in March isn't necessarily the same thing as a good price in November). Here are the best Amazon Big Spring Sale deals on tech we love that you can get right now. Apple's AirPods Pro are once again available for 189, which matches the best price we've seen for the latest iteration with a USB-C charging case. Apple normally sells the noise-canceling earphones for 249, though we often see them go closer to 200 at third-party retailers. Either way, they remain our favorite wireless earbuds for iOS users, as they provide an array of perks when paired with an iPhone, from faster pairing to hands-free Siri. Their battery life and mic quality are just OK these days, but this pair should serve you well if you're all-in on Apple.

Dyson is finally bringing its latest robot vacuum to the US and Canada, after launching in other regions. The Dyson 360 Vis Nav is now available to order in the US for a heady 1,199 from both Dyson's online store and retailers like Amazon. I tested Dyson's first attempt almost a decade ago, but the new model is more powerful, smarter and apparently capable of cleaning for longer. It also looks like what would happen if a GameCube married a fighter from Robot Wars. On paper, the technology could set the 360 Vis Nav apart from rivals with raw suction power -- but we'll have to review it to test that.

Amazon's Big Spring Sale is now live. The early deals of the past few days gave us some insight into what to expect, but now it's official. Unlike the company's Prime Day sales in July and October, this sale is by and large for anyone, non-Prime members included. That's good news for those who don't pay the 79 yearly membership fee. In another departure from Prime events, the Amazon spring sale looks to be focused on seasonal fare, particularly Spring apparel and home decor. While that's not our beat, we did find a good number of deals on some of the gadgets and devices we recommend, including a new low price on our favorite Android smartphone, 72 drop on our top wireless Sony headphones and up to 43 percent off Anker charging accessories. The Big Spring Sale runs through Monday, March 25th. From now until then, we'll continue to sift through Amazon's offerings to find the best tech deals on the site, updating this guide as we do.

As modern Large Language Models (LLMs) shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairness, focusing on protected group bias. We conduct a two-part study: first, we solicit sentence continuations describing the occupations of individuals from different protected groups, including gender, sexuality, religion, and race. Second, we have the model generate stories about individuals who hold different types of occupations. We collect >10k sentence completions made by a publicly available LLM, which we subject to human annotation. We find bias across minoritized groups, but in particular in the domains of gender and sexuality, as well as Western bias, in model generations. The model not only reflects societal biases, but appears to amplify them. The model is additionally overly cautious in replies to queries relating to minoritized groups, providing responses that strongly emphasize diversity and equity to an extent that other group characteristics are overshadowed. This suggests that artificially constraining potentially harmful outputs may itself lead to harm, and should be applied in a careful and controlled manner.

Amazon's Echo Buds are on sale for their lowest price yet. You can get the retailer's AirPods alternatives today for a mere 35 (that's 15 off). One of Engadget's picks for the best budget wireless earbuds, the Echo Buds have a clear, detailed and balanced sound profile, built-in Alexa support and five hours of battery life. The discount comes a few days before Amazon's Big Spring Sale is set to kick off on March 20. Amazon's Echo Buds are available today for a record-low price.

Recently, there has been a growing interest in the use of deep learning techniques for tasks in natural language processing (NLP), with sentiment analysis being one of the most challenging areas, particularly in the Persian language. The vast amounts of content generated by Persian users on thousands of websites, blogs, and social networks such as Telegram, Instagram, and Twitter present a rich resource of information. Deep learning techniques have become increasingly favored for extracting insights from this extensive pool of raw data, although they face several challenges. In this study, we introduced and implemented a hybrid deep learning-based model for sentiment analysis, using customer review data from the Digikala Online Retailer website. We employed a variety of deep learning networks and regularization techniques as classifiers. Ultimately, our hybrid approach yielded an impressive performance, achieving an F1 score of 78.3 across three sentiment categories: positive, negative, and neutral.

Inventory monitoring in homes, factories, and retail stores relies on maintaining data despite objects being swapped, added, removed, or moved. We introduce Lifelong LERF, a method that allows a mobile robot with minimal compute to jointly optimize a dense language and geometric representation of its surroundings. Lifelong LERF maintains this representation over time by detecting semantic changes and selectively updating these regions of the environment, avoiding the need to exhaustively remap. Human users can query inventory by providing natural language queries and receiving a 3D heatmap of potential object locations. To manage the computational load, we use Fog-ROS2, a cloud robotics platform, to offload resource-intensive tasks. Lifelong LERF obtains poses from a monocular RGBD SLAM backend, and uses these poses to progressively optimize a Language Embedded Radiance Field (LERF) for semantic monitoring. Experiments with 3-5 objects arranged on a tabletop and a Turtlebot with a RealSense camera suggest that Lifelong LERF can persistently adapt to changes in objects with up to 91% accuracy.

This paper deals with price optimization, which is to find the best pricing strategy that maximizes revenue or profit, on the basis of demand forecasting models. Though recent advances in regression technologies have made it possible to reveal price-demand relationship of a large number of products, most existing price optimization methods, such as mixed integer programming formulation, cannot handle tens or hundreds of products because of their high computational costs. To cope with this problem, this paper proposes a novel approach based on network flow algorithms. We reveal a connection between supermodularity of the revenue and cross elasticity of demand. On the basis of this connection, we propose an efficient algorithm that employs network flow algorithms. The proposed algorithm can handle hundreds or thousands of products, and returns an exact optimal solution under an assumption regarding cross elasticity of demand. Even if the assumption does not hold, the proposed algorithm can efficiently find approximate solutions as good as other state-of-the-art methods, as empirical results show.

We consider the assortment optimization problem when customer preferences follow a mixture of Mallows distributions. The assortment optimization problem focuses on determining the revenue/profit maximizing subset of products from a large universe of products; it is an important decision that is commonly faced by retailers in determining what to offer their customers. There are two key challenges: (a) the Mallows distribution lacks a closed-form expression (and requires summing an exponential number of terms) to compute the choice probability and, hence, the expected revenue/profit per customer; and (b) finding the best subset may require an exhaustive search. Our key contributions are an efficiently computable closed-form expression for the choice probability under the Mallows model and a compact mixed integer linear program (MIP) formulation for the assortment problem.