Pharmaceutical companies have been educating healthcare providers (HCPs) about new medicines and treatments for decades, shaping patterns of care and influencing treatment decisions. Traditionally, these educational conversations happened in person. But as hospitals and clinics have limited in-person visits in recent years, companies have increasingly turned to digital communication [1]. Today, pharmaceutical companies connect with HCPs using many online tools: e-mail, digital advertisements, virtual meetings, and even professional social media platforms [2, 3, 4, 5, 6, 7]. And now, short message service (SMS) text messaging has emerged as a powerful digital tool.

In text messages from 2017, Jeffrey Epstein seemingly represented himself as positioned to pass information from the Trump White House to Bill Gates through an intermediary. In text messages sent in 2017, disgraced financier and registered sex offender Jeffrey Epstein appears to position himself as a middleman between president Donald Trump's administration and Microsoft cofounder Bill Gates, even seemingly representing himself as passing on information directly from Trump to Gates through an intermediary. The messages, which the House Committee on Oversight and Government Reform released on Wednesday and originated with the Epstein estate, begin on January 27, 2017, years after Epstein had already pleaded guilty to state prostitution solicitation charges. In them, Epstein purports to show intimate awareness of Trump's plans for domestic and global public health policy, and to be directly familiar with the president's thinking. Trump has continued to claim, as recently as this summer, that he stopped speaking with Epstein around 2004.

Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analysis of social networks using the time series of people's mobile connectivity data has not been extensively investigated. In the present study, we investigate four snapshot - based temporal GNNs in predicting the phone call and SMS activity between users of a mobile communication network. In addition, we develop a simple non - GNN baseline model using recently proposed EdgeBank method. Our analysis shows that the ROLAND temporal GNN outperforms the baseline model in most cases, whereas the other three GNNs perform on average worse than the baseline. The results show that GNN based approaches hold promise in the analysis of temporal social networks through mobile connectivity data. However, due to the relatively small performance margin between ROLAND and the baseline model, further research is required on specialized GNN architectures for temporal social network analysis.

Vision language model (VLM)-based mobile agents show great potential for assisting users in performing instruction-driven tasks. However, these agents typically struggle with personalized instructions -- those containing ambiguous, user-specific context -- a challenge that has been largely overlooked in previous research. In this paper, we define personalized instructions and introduce PerInstruct, a novel human-annotated dataset covering diverse personalized instructions across various mobile scenarios. Furthermore, given the limited personalization capabilities of existing mobile agents, we propose PerPilot, a plug-and-play framework powered by large language models (LLMs) that enables mobile agents to autonomously perceive, understand, and execute personalized user instructions. PerPilot identifies personalized elements and autonomously completes instructions via two complementary approaches: memory-based retrieval and reasoning-based exploration. Experimental results demonstrate that PerPilot effectively handles personalized tasks with minimal user intervention and progressively improves its performance with continued use, underscoring the importance of personalization-aware reasoning for next-generation mobile agents. The dataset and code are available at: https://github.com/xinwang-nwpu/PerPilot

This work presents an encryption model based on Generative Adversarial Networks (GANs). Encryption of RTF-8 data is realized by dynamically generating decimal numbers that lead to the encryption and decryption of alphabetic strings in integer representation by simple addition rules, the modulus of the dimension of the considered alphabet. The binary numbers for the private dynamic keys correspond to the binary numbers of public reference keys, as defined by a specific GAN configuration. For reversible encryption with a bijective mapping between dynamic and reference keys, as defined by the GAN encryptor, secure text encryption can be achieved by transferring a GAN-encrypted public key along with the encrypted text from a sender to a receiver. Using the technique described above, secure text mail transfer can be realized through component-wise encryption and decryption of text mail strings, with total key sizes of up to $10^{8}$ bits that define random decimal numbers generated by the GAN. From the present model, we assert that encrypted texts can be transmitted more efficiently and securely than from RSA encryption, as long as users of the specific configuration of the GAN encryption model are unaware of the GAN encryptor circuit and configuration, respectively.

Like many of you, I receive a variety of notifications by various means. Postal letters, email reminders, pop-ups on my laptop, audio signals on my mobile, highlighted chat application entries, text messages, phone calls, taps on the shoulder--the list is long! Thinking a bit more about this, one of the purposes of notification is to resynchronize otherwise asynchronous processes. You tell Google Assistant to set a timer for 15 minutes and go off to do something else. After 15 minutes, you get an audible reminder that the 15 minutes are up, and you should turn off the spaghetti before it turns to mush.

Spokesperson Tammy Bruce said the State Department is "aware" of an incident in which someone used AI to try to pose as Secretary of State Marco Rubio. The State Department is investigating an impostor who reportedly pretended to be Secretary of State Marco Rubio with the help of AI. The mystery individual posing as one of President Donald Trump's Cabinet members reached out to foreign ministers, a U.S. governor and a member of Congress with AI-assisted voice and text messages that mimicked Rubio's voice and writing style, the Washington Post reported, citing a senior U.S. official and State Department cable. "The State Department, of course, is aware of this incident and is currently monitoring and addressing the matter. The department takes seriously its responsibility to safeguard its information and continuously take steps to improve the department's cybersecurity posture to prevent future incidents. For security reasons, we do not have any further details to provide at this time," State Department spokesperson Tammy Bruce said Tuesday.

Breakthroughs, discoveries, and DIY tips sent every weekday. If you recently received a voice message from an unusual number claiming to be your local congressperson, it's probably a scam. The FBI's crime division issued a warning this week about a new scheme in which bad actors use text messages and AI-generated voice clones to impersonate government officials. The scammers try to build a sense of connection with their target and eventually convince them to click on a malicious link that steals valuable login credentials. This scam is just the latest in a series of evolving attacks using convincing generative AI technology to trick people.

I feel a troubling kind of opacity in my brain lately--as if reality were becoming illegible, as if language were a vessel with holes in the bottom and meaning was leaking all over the floor. I sometimes look up words after I write them: does "illegible" still mean too messy to read? The day after Donald Trump's second Inauguration, my verbal cognition kept glitching: I got an e-mail from the children's-clothing company Hanna Andersson and read the name as "Hamas"; on the street, I thought "hot yoga" was "hot dogs"; on the subway, a theatre poster advertising "Jan. Ticketing" said "Jia Tolentino" to me. Even the words that I might use to more precisely describe the sensation of "losing it" elude me.

In at least one crucial way, AI has already won its campaign for global dominance. An unbelievable volume of synthetic prose is published every moment of every day--heaping piles of machine-written news articles, text messages, emails, search results, customer-service chats, even scientific research. Chatbots learned from human writing. Now the influence may run in the other direction. Some people have hypothesized that the proliferation of generative-AI tools such as ChatGPT will seep into human communication, that the terse language we use when prompting a chatbot may lead us to dispose of any niceties or writerly flourishes when corresponding with friends and colleagues.