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.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Flying taxis are taking off in Texas. Can tech really get you off your phone and into real life?

Documents say customs officers in the US Virgin Islands had friendly relationships with Epstein years after his 2008 conviction, showing how the infamous sex offender tried to cultivate allies. United States prosecutors and federal law enforcement spent over a year examining ties between Jeffrey Epstein and Customs and Border Protection officers stationed in the US Virgin Islands (USVI), according to documents recently released by the Department of Justice. As The Guardian and New York Times have reported, emails, text messages, and investigative records show that Epstein cultivated friendships with several officers, entertaining them on his island and offering to take them for whale-watching trips in his helicopter. He even brought one cannolis for Christmas Eve. In turn, Epstein would bring certain officers his complaints about his treatment at the hands of other CBP and federal agents.

Academia heavily relies on trust. This trust-based system, however, creates a significant vulnerability: identity theft. In this Opinion column, we describe newly uncovered cases of identity theft within the scientific peer-review process within the research area of artificial intelligence (AI), involving modus operandi that could also disrupt other academic procedures. We begin by outlining the peer-review process, focusing on scientific conferences since they are the most prominent venues of publication in computer science. Peer review is foundational to scientific inquiry, relying on researchers to voluntarily apply their expertise in evaluating scientific papers.

Task-oriented dexterous grasping holds broad application prospects in robotic manipulation and human-object interaction. However, most existing methods still struggle to generalize across diverse objects and task instructions, as they heavily rely on costly labeled data to ensure task-specific semantic alignment. In this study, we propose \textbf{ZeroDexGrasp}, a zero-shot task-oriented dexterous grasp synthesis framework integrating Multimodal Large Language Models with grasp refinement to generate human-like grasp poses that are well aligned with specific task objectives and object affordances. Specifically, ZeroDexGrasp employs prompt-based multi-stage semantic reasoning to infer initial grasp configurations and object contact information from task and object semantics, then exploits contact-guided grasp optimization to refine these poses for physical feasibility and task alignment. Experimental results demonstrate that ZeroDexGrasp enables high-quality zero-shot dexterous grasping on diverse unseen object categories and complex task requirements, advancing toward more generalizable and intelligent robotic grasping.

Abstract: We discuss newly uncovered cases of identity theft in the scientific peer-review process within artificial intelligence (AI) research, with broader implications for other academic procedures. We detail how dishonest researchers exploit the peer-review system by creating fraudulent reviewer profiles to manipulate paper evaluations, leveraging weaknesses in reviewer recruitment workflows and identity verification processes. The findings highlight the critical need for stronger safeguards against identity theft in peer review and academia at large, and to this end, we also propose mitigating strategies. Academia heavily relies on trust. This trust-based system, however, creates a significant vulnerability: identity theft.

The bottom line, says William Agnew, a postdoctoral fellow in AI ethics at Carnegie Mellon University and one of the coauthors, is that "anything you put online can [be] and probably has been scraped." The researchers found thousands of instances of validated identity documents--including images of credit cards, driver's licenses, passports, and birth certificates--as well as over 800 validated job application documents (including résumés and cover letters), which were confirmed through LinkedIn and other web searches as being associated with real people. A number of the résumés disclosed sensitive information including disability status, the results of background checks, birth dates and birthplaces of dependents, and race. When résumés were linked to people with online presences, researchers also found contact information, government identifiers, sociodemographic information, face photographs, home addresses, and the contact information of other people (like references). When it was released in 2023, DataComp CommonPool, with its 12.8 billion data samples, was the largest existing data set of publicly available image-text pairs, which are often used to train generative text-to-image models.

Visuotactile sensing offers rich contact information that can help mitigate performance bottlenecks in imitation learning, particularly under vision-limited conditions, such as ambiguous visual cues or occlusions. Effectively fusing visual and visuotactile modalities, however, presents ongoing challenges. We introduce GelFusion, a framework designed to enhance policies by integrating visuotactile feedback, specifically from high-resolution GelSight sensors. GelFusion using a vision-dominated cross-attention fusion mechanism incorporates visuotactile information into policy learning. To better provide rich contact information, the framework's core component is our dual-channel visuotactile feature representation, simultaneously leveraging both texture-geometric and dynamic interaction features. We evaluated GelFusion on three contact-rich tasks: surface wiping, peg insertion, and fragile object pick-and-place. Outperforming baselines, GelFusion shows the value of its structure in improving the success rate of policy learning.

Robust and precise robotic assembly entails insertion of constituent components. Insertion success is hindered when noise in scene understanding exceeds tolerance limits, especially when fabricated with tight tolerances. In this work, we propose ContactFusion which combines global mapping with local contact information, fusing point clouds with force sensing. Our method entails a Rejection Sampling based contact occupancy sensing procedure which estimates contact locations on the end-effector from Force/Torque sensing at the wrist. We demonstrate how to fuse contact with visual information into a Stochastic Poisson Surface Map (SPSMap) - a map representation that can be updated with the Stochastic Poisson Surface Reconstruction (SPSR) algorithm. We first validate the contact occupancy sensor in simulation and show its ability to detect the contact location on the robot from force sensing information. Then, we evaluate our method in a peg-in-hole task, demonstrating an improvement in the hole pose estimate with the fusion of the contact information with the SPSMap.

Multi-hand semantic grasp generation aims to generate feasible and semantically appropriate grasp poses for different robotic hands based on natural language instructions. Although the task is highly valuable, due to the lack of multi-hand grasp datasets with fine-grained contact description between robotic hands and objects, it is still a long-standing difficult task. In this paper, we present Multi-GraspSet, the first large-scale multi-hand grasp dataset with automatically contact annotations. Based on Multi-GraspSet, we propose Multi-GraspLLM, a unified language-guided grasp generation framework. It leverages large language models (LLM) to handle variable-length sequences, generating grasp poses for diverse robotic hands in a single unified architecture. Multi-GraspLLM first aligns the encoded point cloud features and text features into a unified semantic space. It then generates grasp bin tokens which are subsequently converted into grasp pose for each robotic hand via hand-aware linear mapping. The experimental results demonstrate that our approach significantly outperforms existing methods on Multi-GraspSet. More information can be found on our project page https://multi-graspllm.github.io.