The Aiper Scuba X1 looks--and is priced--like a high-end robotic pool cleaner, but it's a weak performer and it's a bear to clean after a session in the pool. Aiper makes some excellent robotic pool cleanrs--such as its stellar workhorse, the Seagull Pro--but it also has a few duds in its arsenal, including the Seagull Plus and the Scuba S1. With its latest robot, the Scuba X1, Aiper looks to bring some higher-end features (including smart connectivity) to the lineup. With a street price of 1,200, it's one of Aiper's most expensive models–and it's got the gold trim to prove it. The Aiper Scuba X1 doesn't change the basic design that most of Aiper's full-size robots have followed for years: Compare its design to the aforementioned Seagull Pro, Seagull Plus, and Scuba S1.

After years of lagging behind rivals when it comes to video capture (and then suddenly buying cinema camera manufacturer RED), Nikon is pushing new boundaries in that area. Its latest salvo is the 1,699 24-megapixel full-frame Z5 II, perhaps the cheapest mirrorless camera so far to support internal RAW video. It also offers improved autofocus with new AI powers, cleaner images and enhanced image stabilization. The Z5 II is a wholesale remake of the original Z5 and that starts with video. While still limited to 4K 30 fps and cropped 4K 60 fps, it can now capture those formats internally using the company's 12-bit N-RAW format with N-log, along with 10-bit H.265 and 8-bit H.264. Interestingly, it will record in N-RAW to SDXC UHS-II cards, since the camera lacks high-speed CFexpress slots.

The increasing capability of large language models (LLMs) to generate synthetic content has heightened concerns about their misuse, driving the development of Machine-Generated Text (MGT) detection models. However, these detectors face significant challenges due to the lack of systematically generated, high-quality datasets for training. To address this issue, we propose five novel data augmentation frameworks for synthetic user dialogue generation through a structured prompting approach, reducing the costs associated with traditional data collection methods. Our proposed method yields 14 new dialogue datasets, which we benchmark against seven MGT detection models. The results demonstrate improved generalization performance when utilizing a mixed dataset produced by our proposed augmentation framework. Furthermore, considering that real-world agents lack knowledge of future opponent utterances, we simulate online dialogue detection and examine the relationship between chat history length and detection accuracy. We also benchmark online detection performance with limited chat history on our frameworks. Our open-source datasets can be downloaded from https://github.com/AngieYYF/SPADE-customer-service-dialogue.

At the moment they may exist only in our wildest dreams or in Hollywood science-fiction epics. But humanoid robots that wash dishes, vacuum the carpets, cook and pick up dirty laundry could be available within a decade – and all for the price of a family car. These machines – equipped with hands, arms and legs capable of doing basic household chores – are currently in development around the world. Pulkit Agrawal, associate professor in the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), said: 'Silicon Valley companies are promising this year you can buy a robot, but my guess would be more like five to ten years, at least. 'The technology is progressing, but it's good to be realistic that it will take time to deploy.'

This paper applies deep reinforcement learning (DRL) to optimize liquidity provisioning in Uniswap v3, a decentralized finance (DeFi) protocol implementing an automated market maker (AMM) model with concentrated liquidity. We model the liquidity provision task as a Markov Decision Process (MDP) and train an active liquidity provider (LP) agent using the Proximal Policy Optimization (PPO) algorithm. The agent dynamically adjusts liquidity positions by using information about price dynamics to balance fee maximization and impermanent loss mitigation. We use a rolling window approach for training and testing, reflecting realistic market conditions and regime shifts. This study compares the data-driven performance of the DRL-based strategy against common heuristics adopted by small retail LP actors that do not systematically modify their liquidity positions. By promoting more efficient liquidity management, this work aims to make DeFi markets more accessible and inclusive for a broader range of participants. Through a data-driven approach to liquidity management, this work seeks to contribute to the ongoing development of more efficient and user-friendly DeFi markets.

A hallmark of intelligence is the ability to flesh out underspecified situations using "common sense." We propose to extract that common sense from large language models (LLMs), in a form that can feed into probabilistic inference. We focus our investigation on $\textit{guesstimation}$ questions such as "How much are Airbnb listings in Newark, NJ?" Formulating a sensible answer without access to data requires drawing on, and integrating, bits of common knowledge about how $\texttt{Price}$ and $\texttt{Location}$ may relate to other variables, such as $\texttt{Property Type}$. Our framework answers such a question by synthesizing an $\textit{ad hoc}$ probabilistic model. First we prompt an LLM to propose a set of random variables relevant to the question, followed by moment constraints on their joint distribution. We then optimize the joint distribution $p$ within a log-linear family to maximize the overall constraint satisfaction. Our experiments show that LLMs can successfully be prompted to propose reasonable variables, and while the proposed numerical constraints can be noisy, jointly optimizing for their satisfaction reconciles them. When evaluated on probabilistic questions derived from three real-world tabular datasets, we find that our framework performs comparably to a direct prompting baseline in terms of total variation distance from the dataset distribution, and is similarly robust to noise.

Over the past decade, bidding in power markets has attracted widespread attention. Reinforcement Learning (RL) has been widely used for power market bidding as a powerful AI tool to make decisions under real-world uncertainties. However, current RL methods mostly employ low dimensional bids, which significantly diverge from the N price-power pairs commonly used in the current power markets. The N-pair bidding format is denoted as High Dimensional Bids (HDBs), which has not been fully integrated into the existing RL-based bidding methods. The loss of flexibility in current RL bidding methods could greatly limit the bidding profits and make it difficult to tackle the rising uncertainties brought by renewable energy generations. In this paper, we intend to propose a framework to fully utilize HDBs for RL-based bidding methods. First, we employ a special type of neural network called Neural Network Supply Functions (NNSFs) to generate HDBs in the form of N price-power pairs. Second, we embed the NNSF into a Markov Decision Process (MDP) to make it compatible with most existing RL methods. Finally, experiments on Energy Storage Systems (ESSs) in the PJM Real-Time (RT) power market show that the proposed bidding method with HDBs can significantly improve bidding flexibility, thereby improving the profit of the state-of-the-art RL bidding methods.

DJI has just unveiled the Neo, its much-leaked 200 drone aimed at content creators and casual users. It's tiny and easy to use thanks to novice-friendly features like propeller guards, palm takeoff and voice control. However, the Neo is no toy (or Snap Pixy). It has a suite of powerful features like ActiveTrack, Quick Shots, FPV controller support, smartphone control and the ability to record yourself with the DJI Mic 2. Video specs look promising as well, but not everything is perfect -- it lacks obstacle detection and uses small propellers that are likely to be noisy. I wasn't able to give it a full look as some features were missing, but I was still astonished by what DJI got a small, cheap drone to do. The Neo is DJI's lightest drone by a long way at 135 grams and is nearly small enough to fit into a pocket.