Visit tech startup Skydio's headquarters on the San Francisco peninsula in California and you're likely to find flying robots buzzing on the roof overhead. Docking stations with motorised covers open to allow small drones that resemble the TIE fighters from Star Wars films to take off; when each drone lands back again, they close. The drones can fly completely autonomously and without GPS, taking in data from onboard cameras and using AI to execute programmed missions and avoid obstacles. Skydio, with more than 740m in venture capital funding and a valuation of about 2.5bn, makes drones for the military along with civilian organisations such as police forces and utility companies. The company moved away from the consumer market in 2020 and is now the largest US drone maker.

Foundation models have revolutionized artificial intelligence (AI), offering remarkable capabilities across multi-modal domains. Their ability to precisely locate objects in complex aerial and satellite images, using rich contextual information and detailed object descriptions, is essential for remote sensing (RS). These models can associate textual descriptions with object positions through the Visual Grounding (VG) task, but due to domain-specific challenges, their direct application to RS produces sub-optimal results. To address this, we applied Parameter Efficient Fine Tuning (PEFT) techniques to adapt these models for RS-specific VG tasks. Specifically, we evaluated LoRA placement across different modules in Grounding DINO and used BitFit and adapters to fine-tune the OFA foundation model pre-trained on general-purpose VG datasets. This approach achieved performance comparable to or surpassing current State Of The Art (SOTA) models while significantly reducing computational costs. This study highlights the potential of PEFT techniques to advance efficient and precise multi-modal analysis in RS, offering a practical and cost-effective alternative to full model training.

Reinforcement learning (RL) can transform power grid operations by providing adaptive and scalable controllers essential for grid decarbonization. However, existing methods struggle with the complex dynamics, aleatoric uncertainty, long-horizon goals, and hard physical constraints that occur in real-world systems. This paper presents RL2Grid, a benchmark designed in collaboration with power system operators to accelerate progress in grid control and foster RL maturity. Built on a power simulation framework developed by RTE France, RL2Grid standardizes tasks, state and action spaces, and reward structures within a unified interface for a systematic evaluation and comparison of RL approaches. Moreover, we integrate real control heuristics and safety constraints informed by the operators' expertise to ensure RL2Grid aligns with grid operation requirements. We benchmark popular RL baselines on the grid control tasks represented within RL2Grid, establishing reference performance metrics. Our results and discussion highlight the challenges that power grids pose for RL methods, emphasizing the need for novel algorithms capable of handling real-world physical systems.

The traditional deep learning paradigm that solely relies on labeled data has limitations in representing the spatial relationships between farmland elements and the surrounding environment.It struggles to effectively model the dynamic temporal evolution and spatial heterogeneity of farmland. Language,as a structured knowledge carrier,can explicitly express the spatiotemporal characteristics of farmland, such as its shape, distribution,and surrounding environmental information.Therefore,a language-driven learning paradigm can effectively alleviate the challenges posed by the spatiotemporal heterogeneity of farmland.However,in the field of remote sensing imagery of farmland,there is currently no comprehensive benchmark dataset to support this research direction.To fill this gap,we introduced language based descriptions of farmland and developed FarmSeg-VL dataset,the first fine-grained image-text dataset designed for spatiotemporal farmland segmentation.Firstly, this article proposed a semi-automatic annotation method that can accurately assign caption to each image, ensuring high data quality and semantic richness while improving the efficiency of dataset construction.Secondly,the FarmSeg-VL exhibits significant spatiotemporal characteristics.In terms of the temporal dimension,it covers all four seasons.In terms of the spatial dimension,it covers eight typical agricultural regions across China.In addition, in terms of captions,FarmSeg-VL covers rich spatiotemporal characteristics of farmland,including its inherent properties,phenological characteristics, spatial distribution,topographic and geomorphic features,and the distribution of surrounding environments.Finally,we present a performance analysis of VLMs and the deep learning models that rely solely on labels trained on the FarmSeg-VL,demonstrating its potential as a standard benchmark for farmland segmentation.

Cycle-level simulators such as gem5 are widely used in microarchitecture design, but they are prohibitively slow for large-scale design space explorations. We present Concorde, a new methodology for learning fast and accurate performance models of microarchitectures. Unlike existing simulators and learning approaches that emulate each instruction, Concorde predicts the behavior of a program based on compact performance distributions that capture the impact of different microarchitectural components. It derives these performance distributions using simple analytical models that estimate bounds on performance induced by each microarchitectural component, providing a simple yet rich representation of a program's performance characteristics across a large space of microarchitectural parameters. Experiments show that Concorde is more than five orders of magnitude faster than a reference cycle-level simulator, with about 2% average Cycles-Per-Instruction (CPI) prediction error across a range of SPEC, open-source, and proprietary benchmarks. This enables rapid design-space exploration and performance sensitivity analyses that are currently infeasible, e.g., in about an hour, we conducted a first-of-its-kind fine-grained performance attribution to different microarchitectural components across a diverse set of programs, requiring nearly 150 million CPI evaluations.

The colonization of Mars presents extraordinary challenges, including radiation exposure, low atmospheric pressure, and toxic regolith. Recent advancements in synthetic biology and genetic engineering offer unprecedented opportunities to address these obstacles by utilizing terrestrial extremophiles and engineered organisms. This paper examines the potential for creating symbiotic relationships between terrestrial microbes and hypothetical Martian life forms, should they exist, to support a sustainable human presence on Mars. Inspired by natural examples of endosymbiosis, such as mitochondria and chloroplasts, we propose methods to engineer life forms capable of enduring Martian conditions. Key components include experimental designs, laboratory simulations, and bioengineering approaches essential to this endeavor. The ethical, political, and technological challenges of introducing engineered life to Mars are critically evaluated, with an emphasis on international collaboration and robust planetary protection policies. This research underscores engineered symbiosis as a transformative strategy for enabling life to adapt and thrive on Mars while advancing humanity's aspirations for interplanetary habitation and exploration. By addressing these challenges, this work highlights a path toward sustainable life on Mars, reflecting both scientific ingenuity and ethical stewardship.

The accurate forecasting of geomagnetic activity is important. In this work, we present a novel multimodal Transformer based framework for predicting the 3 days and 5 days planetary Kp index by integrating heterogeneous data sources, including satellite measurements, solar images, and KP time series. A key innovation is the incorporation of the Wasserstein distance into the transformer and the loss function to align the probability distributions across modalities. Comparative experiments with the NOAA model demonstrate performance, accurately capturing both the quiet and storm phases of geomagnetic activity. This study underscores the potential of integrating machine learning techniques with traditional models for improved real time forecasting.

Digital twin technologies help practitioners simulate, monitor, and predict undesirable outcomes in-silico, while avoiding the cost and risks of conducting live simulation exercises. Virtual reality (VR) based digital twin technologies are especially useful when monitoring human Patterns of Life (POL) in secure nuclear facilities, where live simulation exercises are too dangerous and costly to ever perform. However, the high-security status of such facilities may restrict modelers from deploying human activity sensors for data collection. This problem was encountered when deploying MetaPOL, a digital twin system to prevent insider threat or sabotage of secure facilities, at a secure nuclear reactor facility at Oak Ridge National Laboratory (ORNL). This challenge was addressed using an agent-based model (ABM), driven by anecdotal evidence of facility personnel POL, to generate synthetic movement trajectories. These synthetic trajectories were then used to train deep neural network surrogates for next location and stay duration prediction to drive NPCs in the VR environment. In this study, we evaluate the efficacy of this technique for establishing NPC movement within MetaPOL and the ability to distinguish NPC movement during normal operations from that during a simulated emergency response. Our results demonstrate the success of using a multi-layer perceptron for next location prediction and mixture density network for stay duration prediction to predict the ABM generated trajectories. We also find that NPC movement in the VR environment driven by the deep neural networks under normal operations remain significantly different to that seen when simulating responses to a simulated emergency scenario.

Prime Day is months away. Black Friday is nearly a year off. Amazon has spied a gap in the calendar and plans to cram it full of deals. Amazon's Big Spring Sale runs from through March 31. With no other big sale events in view, this could be a good time to snag that mesh router, set of headphones, or robo vac you've had your eye on. As usual, Amazon has discounts on all sorts of stuff, but many deals are exclusive to Amazon Prime members. Now, we're not suggesting you harvest this spring deal crop indiscriminately; we are here to help you sort the wheat from the chaff. The WIRED Gear team has run its many eyes over the list to tease out deals that are for gadgets worth owning and actually deals. Everything we highlight here has been hand-tested by one of us and deemed worthy of a spot in your home. Updated March 28: We've checked prices and added a few fresh deals on things like slippers, t-shirts, and an extreme alarm clock. Get best-in-class reporting that's too important to ignore for just 2.50 1 per month for 1 year. Includes unlimited digital access and exclusive subscriber-only content. The Eero Pro 6E (7/10, WIRED Recommends) mesh system is one of the easiest to set up and will deliver speedy, stable Wi-Fi across your home. Amazon's Eero makes some of our favorite mesh systems, ideal for busy families seeking a set-and-forget mesh. The Pro 6E is a tri-band system with a 6-GHz band for fast Wi-Fi at close range, and with the jump to Wi-Fi 7 systems still costly, this system is worth considering right now. But you need an Eero Plus subscription at 10 per month or 100 per year to unlock the best features, including parental controls, advanced security, and ad blocking. There are discounts on other Eero systems, so check our Eero buying guide to decide which is best for your home. DJI's debut portable power station can put out 2,200 watts steadily (2,600 watts surge), has two USB-C PD 3.1 ports (140 watts), and boasts DJI's proprietary SDC ports for fast-charging drone batteries. It can juice up phones, run microwaves or small tools, and meet most of your portable power needs, but it's an especially great choice for folks with DJI drones because it can fast charge most models. It gets a little noisy with several gadgets charging, and cable and bag accessories cost extra, but it still claims a place in our best portable power stations guide. Built to last, this braided nylon cable's exterior is 100 percent recycled plastic that Anker promises will last a century.

Accurate segmentation of sea ice types is essential for mapping and operational forecasting of sea ice conditions for safe navigation and resource extraction in ice-covered waters, as well as for understanding polar climate processes. While deep learning methods have shown promise in automating sea ice segmentation, they often rely on extensive labeled datasets which require expert knowledge and are time-consuming to create. Recently, foundation models (FMs) have shown excellent results for segmenting remote sensing images by utilizing pre-training on large datasets using self-supervised techniques. However, their effectiveness for sea ice segmentation remains unexplored, especially given sea ice's complex structures, seasonal changes, and unique spectral signatures, as well as peculiar Synthetic Aperture Radar (SAR) imagery characteristics including banding and scalloping noise, and varying ice backscatter characteristics, which are often missing in standard remote sensing pre-training datasets. In particular, SAR images over polar regions are acquired using different modes than used to capture the images at lower latitudes by the same sensors that form training datasets for FMs. This study evaluates ten remote sensing FMs for sea ice type segmentation using Sentinel-1 SAR imagery, focusing on their seasonal and spatial generalization. Among the selected models, Prithvi-600M outperforms the baseline models, while CROMA achieves a very similar performance in F1-score. Our contributions include offering a systematic methodology for selecting FMs for sea ice data analysis, a comprehensive benchmarking study on performances of FMs for sea ice segmentation with tailored performance metrics, and insights into existing gaps and future directions for improving domain-specific models in polar applications using SAR data.