Trending Now Safety Net Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Mashable Selects Getting Technical with Mashable Gift Ideas For Everyone On Your List Versus In My Bag Mashable @Home All Series The cost of living may be rising, but you can still have a great budget Halloween costume. Amanda Yeo is an Assistant Editor at Mashable, covering entertainment, culture, tech, science, and social good. Based in Australia, she writes about everything from video games and K-pop to movies and gadgets. The cost of living is punting us all in the unmentionables right now. Spending your hard-earned cash on an expensive Halloween costume can feel unwise, especially since you'll only wear it for one night.

The data generation process includes situation sampling, long-form text generation, query generation for the long-form text, and QA generation. It is based on human observations of changes, object attributes, and allocentric object relationships in 3DSSG [9], as well as egocentric relationships between the human and the objects. A.1 Situation Sampling We follow the situation categories of MSQA [4], namely sitting, interacting, and standing, but with more detailed geometric analysis: Sitting. The 28seat categories in 3RScan [8] are grouped into four types: 3large seats with backrests (e.g., sofa), 16 small seats with backrests (e.g., armchair), 1 large seat without a backrest (bed), and 8small seats without backrests (e.g., beanbag). Seatable and backrest areas are classified by surface normals, or by nearby walls within 0.5 m if no backrest exists. For small seats, the seating point is the bounding box center, oriented away from the backrest. For large seats, we select a point with a backrest behind and open space (0.5-1 m) in front.

Physical environments and circumstances are fundamentally dynamic, yet current 3D datasets and evaluation benchmarks tend to concentrate on either dynamic scenarios or dynamic situations in isolation, resulting in incomplete comprehension. To overcome these constraints, we introduce Situat3DChange, an extensive dataset supporting three situation-aware change understanding tasks following the perception-action model: 121K question-answer pairs, 36K change descriptions for perception tasks, and 17K rearrangement instructions for the action task. To construct this large-scale dataset, Situat3DChange leverages 11K human observations of environmental changes to establish shared mental models and shared situational awareness for human-AI collaboration. These observations, enriched with egocentric and allocentric perspectives as well as categorical and coordinate spatial relations, are integrated using an LLM to support understanding of situated changes. To address the challenge of comparing pairs of point clouds from the same scene with minor changes, we propose SCReasoner, an efficient 3DMLLM approach that enables effective point cloud comparison with minimal parameter overhead and no additional tokens required for the language decoder. Comprehensive evaluation on Situat3DChange tasks highlights both the progress and limitations of MLLMs in dynamic scene and situation understanding. Additional experiments on data scaling and cross-domain transfer demonstrate the task-agnostic effectiveness of using Situat3DChange as a training dataset for MLLMs.

My friend walks into the village hall, scene of my son's third birthday party, a mixture of panic and incredulity creeping across his face. "I didn't realise we were dressing up," he says, taking in my outfit. I'm wearing a mint-green tulle midi dress with sheer sleeves that balloon precociously and a tiered skirt that puffs out in such a way as to give me the appearance of either a Quality Street or a three-year-old at her own birthday party. It's not, if I'm entirely honest, the most practical of outfits for serving chocolate cake to 18 sticky-handed toddlers but, as I blurt out to my friend, keen to dispel any confusion, the avant-garde look wasn't actually my choice: it was AI's. My wardrobe is my identity, my refuge, my hobby, my happy place. Or, at least, it was.

Interior design involves the careful selection and arrangement of objects to create an aesthetically pleasing, functional, and harmonized space that aligns with the client's design brief. This task is particularly challenging, as a successful design must not only incorporate all the necessary objects in a cohesive style, but also ensure they are arranged in a way that maximizes accessibility, while adhering to a variety of affordability and usage considerations. Data-driven solutions have been proposed, but these are typically room- or domain-specific and lack explainability in their design design considerations used in producing the final layout. In this paper, we investigate if large language models (LLMs) can be directly utilized for interior design. While we find that LLMs are not yet capable of generating complete layouts, they can be effectively leveraged in a structured manner, inspired by the workflow of interior designers. By systematically probing LLMs, we can reliably generate a list of objects along with relevant constraints that guide their placement. We translate this information into a design layout graph, which is then solved using an off-the-shelf constrained optimization setup to generate the final layouts. We benchmark our algorithm in various design configurations against existing LLM-based methods and human designs, and evaluate the results using a variety of quantitative and qualitative metrics along with user studies. In summary, we demonstrate that LLMs, when used in a structured manner, can effectively generate diverse high-quality layouts, making them a viable solution for creating large-scale virtual scenes. Project webpage at https://flairgpt.github.io/

Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning. A key challenge for agents attempting to solve such tasks is to generalize across multiple games and demonstrate good performance on both seen and unseen objects. Purely deep-RL-based approaches may perform well on seen objects; however, they fail to showcase the same performance on unseen objects. Commonsense-infused deep-RL agents may work better on unseen data; unfortunately, their policies are often not interpretable or easily transferable. To tackle these issues, in this paper, we present EXPLORER which is an exploration-guided reasoning agent for textual reinforcement learning. EXPLORER is neurosymbolic in nature, as it relies on a neural module for exploration and a symbolic module for exploitation. It can also learn generalized symbolic policies and perform well over unseen data. Our experiments show that EXPLORER outperforms the baseline agents on Text-World cooking (TW-Cooking) and Text-World Commonsense (TWC) games.

More than 12,000 people are turning 65 each day in the US. And with that, individuals and families are starting to make considerations on what might be entailed to better manage the aging process. There is a strong desire from seniors to age in place, meaning staying in their home instead of moving to a dedicated facility. Marc Glickman, CEO of long-term care planning experts BuddyIns, estimated that today, around 75% of seniors are using home care services to age in place instead of moving to an assisted living or nursing homes. An AARP survey showed 90% of individuals 65 and over would prefer to age in place.

'Make meetings more fun and personal with Decorate your background,' Microsoft said The fancy option adds sparkling fairy lights and glasses of champagne to your background. Meanwhile, the celebration option adds a festive Christmas tree and presents behind you. The feature will launch in early 2024 for Teams Premium users. The news comes shortly after a study revealed how your background on video calls can influence the first impression you make. Researchers from Durham University say that people who sit in front of houseplants or bookcases are deemed the most trustworthy.

Andreas Kronthaler and iconic British fashion designer Dame Vivienne, who died last year, were a fashion powerhouse designing together under the Westwood brand. The pair married in 1992.

The average person sees up to 10,000 ads every day. No wonder people just throw money at whatever -- they'll buy something they don't need, something that doesn't fit them or won't help them. It can be a perpetual need to spend. Then the guilt hits, and more than half of all Americans say that they regret spending on things they didn't need or can't use. A single Black Friday sale generates $74 billion worth of regretted spending in the U.S. alone.