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Toward the beginning of the 2002 film, a domineering lawyer (played by James Spader) barges into the office of his assistant (Maggie Gyllenhaal) with evidence of a work infraction: a memo she has written that has "three typing errors." "Do you know what this makes me look like to the people who receive these letters?" Setting aside that his screed turns out to be foreplay, Spader's character was channeling a widespread cultural revulsion: Typos were the ultimate shorthand for careless work. A spelling mistake was proof that the writer hadn't bothered putting much effort into a piece of correspondence, that their instructions or advice shouldn't be taken seriously--and perhaps that the recipient shouldn't invest time in reading their note at all. More than two decades later, as AI-generated writing has flooded workplaces, social media, and dating apps, old hallmarks of sloppiness--typos chief among them--are getting a new gloss. Some job applicants are intentionally adding typos to their cover letters to prove that they, and not an AI program, wrote them.

Algorithm 1 Association Graph Learning (TRAININGTIME) Require: {Dtrt }Tt=1: Training sets of all tasks; T: Number of tasks; C: Number of all classes; E: Shared feature extractor; WT,WC: Parameters of metric functions in the association graph; L: Number of GNN layers; {Wl}Ll=1: Parameters of all GNN layers; {ft}Tt=1: Task-specific classifiers; λ: Learning rate. For clarity, we provide the algorithms during training and test in Algorithm 1 and Algorithm 2, respectively. Algorithm 2 Association Graph Learning (TESTTIME) Require: xt: one test instance from the t-th task; E: Trained the feature extractor; GT,GC: Trained task and class graph; L: Number of GNN layers; {Wl}Ll=1: Trained parameters of all GNN layers; ft: The trained task-specific classifier. In this section, we provide the class assignment of all datasets under different missing rates. Table B.1, B.2, B.3 shows the class assignment for Office-Home, Office-Caltechand ImageCLEF, respectively.

We build our dataset from triangu-lated meshes derived from Objaverse-XL and Thingi10K datasets.

In this section, we provide the class assignment of all datasets under different missing rates. The proposed setting is anew multi-task learning scenario. Its practical applications could not be limited by the mentioned assumption in the testing space. Table B.2: The observed classes of each task onOffice-Caltech with different missing rates. Office-Home [9] contains images from four domains/tasks: Artistic, Clipart, Product and Realworld. Skin-Lesion contains three skin lesion classification tasks: HAM10000 [8], Dermofit [2] and Derm7pt[5].

The world's first 3D-printed microscope costs only $60 to build

Technology Engineering How does 3D printing work? Rapid prototyping is a relatively simple process that can be scaled up or down. Breakthroughs, discoveries, and DIY tips sent every weekday. Since 3D printers debuted in the 1980s, the devices have been used to build meat, chocolate, human organs, clothing, cars, and houses . It's more mainstream than ever, and you can buy a machine for less than $200.

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Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures. This contribution presents a noise-aware decision-making algorithm that quantifies and models device-specific noise profiles to manage variability adap-tively. It uses distributional analysis and pairwise divergence metrics with clustering to choose between single-device and robust multi-device Bayesian optimization strategies. Unlike conventional methods that assume homogeneous devices or generic robustness, this framework explicitly leverages inter-device differences to enhance performance, reproducibility, and efficiency. An experimental case study involving three nominally identical 3D printers (same brand, model, and close serial numbers) demonstrates reduced redundancy, lower resource usage, and improved reliability. Overall, this framework establishes a paradigm for precision-and resource-aware optimization in scalable, automated experimental platforms. Introduction Recent advances in automation technologies have revolutionized scientific research, particularly in fields that rely on high-throughput experimentation.

Tactile sensing plays a key role in enabling dexterous and reliable robotic manipulation, but realizing this capability requires substantial calibration to convert raw sensor readings into physically meaningful quantities. Despite its near-universal necessity, the calibration process remains ad hoc and labor-intensive. Here, we introduce 3D Cal, an open-source library that transforms a low-cost 3D printer into an automated probing device capable of generating large volumes of labeled training data for tactile sensor calibration. We demonstrate the utility of 3D Cal by calibrating two commercially available vision-based tactile sensors, DIGIT and GelSight Mini, to reconstruct high-quality depth maps using the collected data and a custom convolutional neural network. In addition, we perform a data ablation study to determine how much data is needed for accurate calibration, providing practical guidelines for researchers working with these specific sensors, and we benchmark the trained models on previously unseen objects to evaluate calibration accuracy and generalization performance. By automating tactile sensor calibration, 3D Cal can accelerate tactile sensing research, simplify sensor deployment, and promote the practical integration of tactile sensing in robotic platforms.