With the rapid development of mobile communication technologies, future mobile networks will offer vast services and resources for commuting, production, daily life, and entertainment. Accurate and efficient forecasting of mobile data (e.g., cell traffic, user behavior, channel quality) helps operators monitor network state changes, orchestrate wireless resources, and schedule infrastructure and users, thereby improving supply efficiency and service quality. However, current forecasting paradigms rely on customized designs with tailored models for exclusive data types. Such approaches increase complexity and deployment costs under large-scale, heterogeneous networks involving base stations, users, and channels. In this paper, we design a foundation model for mobile data forecasting, MobiGPT, with a unified structure capable of forecasting three data types: base station traffic, user app usage, and channel quality. We propose a soft-prompt learning method to help the model understand features of different data types, and introduce a temporal masking mechanism to guide the model through three forecasting tasks: short-term prediction, long-term prediction, and distribution generation, supporting diverse optimization scenarios. Evaluations on real-world datasets with over 100,000 samples show that MobiGPT achieves accurate multi-type forecasting. Compared to existing models, it improves forecasting accuracy by 27.37%, 20.08%, and 7.27%, reflecting strong generalization. Moreover, MobiGPT exhibits superior zero/few-shot performance in unseen scenarios, with over 21.51% improvement, validating its strong transferability as a foundation model.

Existing benchmarks for conversational AI agents simulate single-control environments, where only the AI agent can use tools to interact with the world, while the user remains a passive information provider. This differs from real-world scenarios like technical support, where users need to actively participate in modifying the state of the (shared) world. In order to address this gap, we introduce $τ^2$-bench, with four key contributions: 1) A novel Telecom dual-control domain modeled as a Dec-POMDP, where both agent and user make use of tools to act in a shared, dynamic environment that tests both agent coordination and communication, 2) A compositional task generator that programmatically creates diverse, verifiable tasks from atomic components, ensuring domain coverage and controlled complexity, 3) A reliable user simulator tightly coupled with the environment, whose behavior is constrained by tools and observable states, improving simulation fidelity, 4) Fine-grained analysis of agent performance through multiple ablations including separating errors arising from reasoning vs communication/coordination. In particular, our experiments show significant performance drops when agents shift from no-user to dual-control, highlighting the challenges of guiding users. Overall, $τ^2$-bench provides a controlled testbed for agents that must both reason effectively and guide user actions.

Mobile and ubiquitous sensing of urban air quality (AQ) has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. A necessary and value-added step towards data-driven sustainable urban management is fine-granular AQ inference, which estimates grid-level pollutant concentrations at every instance of time using AQ data collected from fixed-location and mobile sensors. We present the Mobile Air Pollution Sensing (MAPS) framework, which consists of data preprocessing, urban feature extraction, and AQ inference. This is applied to a case study in Beijing (3,025 square km, 19 June - 16 July 2018), where PM2.5 concentrations measured by 28 fixed monitoring stations and 15 vehicles are fused to infer hourly PM2.5 concentrations in 3,025 1km-by-1km grids. Two machine learning structures, namely Deep Feature Spatial-Temporal Tree (DFeaST-Tree) and Deep Feature Spatial-Temporal Network (DFeaST-Net), are proposed to infer PM2.5 concentrations supported by 62 types of urban data that encompass geography, land use, traffic, public, and meteorology. This allows us to infer fine-granular PM2.5 concentrations based on sparse AQ measurements (less than 5% coverage) with good accuracy (SMAPE<15%, R-square>0.9), while accounting for the regional transport of air pollutants outside the study area. In-depth discussions are provided on the heterogeneity of fixed and mobile data sources, spatial coverage of mobile sensing, and importance of urban features for inferring PM2.5 concentrations.

When T-Mobile's chief executive went before Senate lawmakers this year to make the case for his company's merger with Sprint, he argued that the deal could help preserve U.S. dominance in high-tech wireless networks for smartphones and other devices. "We'll make sure America wins the global 5G race," John Legere vowed. "5G will unlock capabilities that will fuel job creation and innovation well beyond what we have seen so far." The entire industry has spent much of the year marketing a dazzling future to consumers, one in which the successor to 4G LTE enables entirely new technologies, such as self-driving cars and remote medicine. But despite the hype, 5G is still a long way from becoming a reality for the majority of everyday Americans.

This article is part of CMO.com's February series about mobile. There's no arguing that smartphones are firmly entrenched in our everyday lives. In fact, recent Adobe research found that consumers spend the same amount of time sleeping as they do consuming content on their smartphone devices. And while the phone provides a means of connecting with consumers wherever they are, customers have always been one step ahead of brands. But as the adoption of artificial intelligence accelerates, brands may be able to catch up, experts say.

If you are fortunate to work as a corporate slave, then you're probably familiar with the acronym BYOD which stands for "Bring Your Own Device". The idea was that since everyone was carrying around their personal smartphone and their work issued smartphone, why not just combine the two so that employees have a better experience? That's the reason HR gives, but the reality is that it's just a big scam to get you to actually pay for a device that your company uses. Plus, since you're now checking work emails on your personal device, guess what? You're now working more (in some cases) to improve those shareholder returns.

Apple's voice assistant, Siri, lets other people access your iPhone, surpass the passcode protection and turn off cellular data, a user said Friday. The user, Anton31Kah, stated Siri can be manipulated into turning off cellular data by simply asking it about the current status of cellular data on the smartphone, upon which the voice assistant will not only tell the user whether the data is currently on or not, but also prompt the user by asking "let me know if you want it turned on/off." This basically lets any user switch mobile data on your iPhone on or off, which is against general smartphone security protocols. Due to the fact access to the iPhone, however minor, is being provided to a third person without the owner's permission who had already protected the device with a security cover like as a passcode. This is a loophole in Siri's functioning due to the fact had the user directly asked Siri to turn the data on or off, he/she would need to put in the passcode first.