When you purchase through links in our articles, we may earn a small commission. Here's how to move your calendar and contacts If you're moving your data to the latest version of Microsoft Outlook, it's important to know that your calendar and contacts may need to be handled separately from your email. Classic Outlook can export your data in a PST file, but New Outlook does not restore calendar and contact information in the same way. Instead, you need to first export your calendar as an ICS file and then your contacts as a CSV file, then you need to import those files to the new version. To restore the calendar, open New Outlook and click on Add Calendar underneath the monthly view.

Fake calendar invites can infect your system, and they're surging - how to protect yourself These invites sneak past your security software to embed themselves in your calendar. But you can thwart them before they do any damage. Lance Whitney is a technology journalist with an IT background. He's written for TechRepublic, PCMag, Macworld, and Time, among others, He also teaches classes in AI, cybersecurity, and social media. Fake meeting invites can infect your system with malware.

Creator Playbook Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Voices Look Up Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series Get up to 64% off full-sized toys from Womanizer, We-Vibe, and Arcwave. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School. You can find more of her work in various online publications, including,, and .

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.

Scammers are using Apple iPhone Calendar subscriptions to send phishing alerts directly to users' devices, Kurt "CyberGuy" Knutsson writes.

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].

Apple Shortcuts, which lets users write custom automations, recently earned some new capabilities thanks to Apple Intelligence. Here's how to make the most of this upgrade. As sentences go, "Apple Intelligence now works in Apple Shortcuts" isn't the most likely to inspire a lot of people to click a link. And that's too bad: This change, one of the more overlooked new features in macOS 26, means you can use Apple's on-board AI to do all kinds of things while designing shortcuts. Look, I get it: Apple Intelligence makes AI a feature, not a product, and features are generally less interesting to read about than full-blown products.

Temporal reasoning and knowledge are essential capabilities for language models (LMs). While much prior work has analyzed and improved temporal reasoning in LMs, most studies have focused solely on the Gregorian calendar. However, many non-Gregorian systems, such as the Japanese, Hijri, and Hebrew calendars, are in active use and reflect culturally grounded conceptions of time. If and how well current LMs can accurately handle such non-Gregorian calendars has not been evaluated so far. Here, we present a systematic evaluation of how well language models handle one such non-Gregorian system: the Japanese wareki. We create datasets that require temporal knowledge and reasoning in using wareki dates. Evaluating open and closed LMs, we find that some models can perform calendar conversions, but GPT-4o, Deepseek V3, and even Japanese-centric models struggle with Japanese calendar arithmetic and knowledge involving wareki dates. Error analysis suggests corpus frequency of Japanese calendar expressions and a Gregorian bias in the model's knowledge as possible explanations. Our results show the importance of developing LMs that are better equipped for culture-specific tasks such as calendar understanding.

API calls for each position identified in a piece of text. Question Answering We use the Atlas model of Izacard et al. (2022) finetuned on Natural Questions Calculator Our calculator is based on a simple Python script and only supports the operators " It does not return any result for syntactically invalid equations. "=", "equals", "equal to", "total of", "average of" followed by a number, or (iii) contain at least three English text before generating API calls. Below, we list the prompts used to sample API calls for each tool considered. Your task is to add calls to a Question Answering API to a piece of text. Input: Joe Biden was born in Scranton, Pennsylvania. Output: Joe Biden was born in [QA("Where was Joe Biden born?")] Scranton, [QA("In Output: Coca-Cola, or [QA("What other name is Coca-Cola known by?")] Coke, is Your task is to add calls to a Calculator API to a piece of text.