While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users' more complex implicit intents.In this work, we highlight Hierarchical Implicit Intent Alignment for Personalized GUI Agent (PersonalAlign), a new agent task that requires agents to leverage long-term user records as persistent context to resolve omitted preferences in vague instructions and anticipate latent routines by user state for proactive assistance.To facilitate this study, we introduce AndroidIntent, a benchmark designed to evaluate agents' ability in resolving vague instructions and providing proactive suggestions through reasoning over long-term user records.We annotated 775 user-specific preferences and 215 routines from 20k long-term records across different users for evaluation.Furthermore, we introduce Hierarchical Intent Memory Agent (HIM-Agent), which maintains a continuously updating personal memory and hierarchically organizes user preferences and routines for personalization.Finally, we evaluate a range of GUI agents on AndroidIntent, including GPT-5, Qwen3-VL, and UI-TARS, further results show that HIM-Agent significantly improves both execution and proactive performance by 15.7% and 7.3%.

The research focus of GUI agents is shifting from text-dependent to pure-vision-based approaches, which, though promising, prioritize comprehensive pre-training data collection while neglecting contextual modeling challenges. We probe the characteristics of element and history contextual modeling in GUI agent and summarize: 1) the high-density and loose-relation of element context highlight the existence of many unrelated elements and their negative influence; 2) the high redundancy of history context reveals the inefficient history modeling in current GUI agents. In this work, we propose a context-aware simplification framework for building an efficient and effective GUI Agent, termed SimpAgent. To mitigate potential interference from numerous unrelated elements, we introduce a masking-based element pruning method that circumvents the intractable relation modeling through an efficient masking mechanism. To reduce the redundancy in historical information, we devise a consistency-guided history compression module, which enhances implicit LLM-based compression through innovative explicit guidance, achieving an optimal balance between performance and efficiency. With the above components, SimpAgent reduces 27% FLOPs and achieves superior GUI navigation performances. Comprehensive navigation experiments across diverse web and mobile environments demonstrate the effectiveness and potential of our agent.

Current affective computing paradigms often treat emotional understanding and generation as separate tasks, yet they inherently possess symbiotic potential for mutual enhancement. In this paper, we aim to bridge the gap by developing a unified framework. The primary challenge lies in the extraction of precise and semantically rich representations of abstract emotions, which are crucial for both tasks. To address this, we harness the Chain-of-Thought reasoning at the latent space of multimodal large language models and propose EmoSym, a unified framework built upon this advanced foundation. Our framework is executed through three key steps: 1) Emotional reasoning knowledge compression. To enable efficient transfer of emotional reasoning priors, we design specialized reasoning tokens to compact emotion-aware contexts from external reasoning knowledge bases into latent representations. 2) Verifiable reinforcement reasoning optimization. To ensure more reliable and consistent emotional reasoning, we develop a verifiable reinforcement learning paradigm to further enhance the reasoning token by emotion-specific verifiable reward signals. Processed through the above two steps, the reasoning token simultaneously enhances emotional understanding while enriching semantic representations, benefiting subsequent emotional generation tasks. 3) Reasoning-augmented generation and online feedback. We then fuse it with emotional representations and feed them into a diffusion model to generate emotion-evoking images. Additionally, to create a generative-to-understanding enhancement feedback, we propose an Online Emotional Memory Bank (OEMB). It leverages newly generated images to progressively update the training dataset in the training process to reinforce understanding. Extensive experiments demonstrate the superior capabilities of our framework in both emotional understanding and generation tasks.

Intensity modulation and direct detection (IM-DD) based optical wireless communication (OWC), requires the modulated signal to be real and non-negative. To satisfy the requirements, this paper proposes two types of mixed orthogonal frequency division multiplexing (X-OFDM) waveforms. The Hermitian symmetry (HS) characteristic of the sub-carriers in the frequency domain, guarantees the signal in the time domain to be real, which reduces the spectral efficiency to 1/2. For the odd sub-carriers in the frequency domain, the signal in the time domain after the inverse fast fourier transform (IFFT) is antisymmetric. For the even sub-carriers in the frequency domain, the signal in the time domain after the IFFT is symmetric. Based on the antisymmetric and symmetric characteristics, the two types of X-OFDM waveforms are designed to guarantee the signal in the time domain to be non-negative. With$M$sub-carriers in the frequency domain, the generated signal in the time domain has$3M/2$points, which further reduces the spectral efficiency to 1/3. The numerical simulations show that, the two types of X-OFDM waveforms greatly enhance the power efficiency considering the OWC channel with the signal-dependent noise and/or the signal-independent noise.

The wavelength division multiplexing radio over fiber (WDM-RoF) system is designed to provide broadband and high throughput services, but sensitive to the nonlinear distortion. This paper studies the nonlinear model with rate equations and multiple dimension behavior, and the corresponding digital linearization algorithms. The multi dimension parallel Hammer-stain (MD-PH) algorithm considers not only the nonlinearity of each path but also the intermodulation and interchannel crosstalk, which provides better performance than crosstalk memory polynomial (CO-MPM) algorithm. By means of VPI photonics design suite, a 4-WDM-RoF system and corresponding algorithms are verified. The simulation results demonstrate that the MD-PH algorithm outperforms the CO-MPM one in terms of the adjacent channel power ratio (ACPR) and the normalized mean square error (NMSE).

With the increasing commercial prospect of personal Unmanned Aerial Vehicle (UAV), human and UAV interaction has been a compelling and challenging task. In this paper, we present the RFHUI, a human and UAV interaction system based on passive radio-frequency identification (RFID) technology which provides a remote control function. Three or more Ultra high frequency (UHF) RFID tags are attached on a board to create a hand-held controller. A COTS (Commercial Off-The-Shelf) RFID reader with multiple antennas is deployed to collect the observations of the tags. According to the phase measurement from the RFID reader, we leverage a Bayesian filter based method to localize the position of all tags in a global coordinate. From the estimated position of the attached tags, a 6 DOF (Degrees of Freedom) pose of the controller can be obtained. Therefore, when the user moves the controller, its pose will be precisely tracked in a real-time manner. Then, the flying commands, which are generated from the estimated pose of the controller, are sent to the UAV for navigation. We implemented a prototype of the RFHUI, and the experiment results show that it provides precise poses with 0.045 m error in position and 2.5° error in orientation for the controller. It therefore enables the controller to precisely and intuitively instruct the UAV's navigation in an indoor environment.

In order to improve the error performance of M-ary differential chaotic shift keying (DCSK) systems over multi-path fading channel, two types of iterative structures have been employed in receiver design. One is joint source-channel decoder (JSCD), the other is iterative receiver (IR). Although several works have separately addressed the advantages of IR and JSCD, it is not clear which design provides more iterative gain for M-ary DCSK systems over multi-path fading channels. In this work, we employ the extrinsic information transfer (EXIT) chart technique to analyze the iteration behavior of JSCD and IR. Simulation results suggest that with enough source redundancy, JSCD outperforms IR under M-ary DCSK systems over multi-paths fading channels.

Although the performance of a differential chaos shift keying (DCSK) system can be improved by designing appropriate channel codes, it is still a great challenge to do so for various channel models through transmitter and receiver designs, particularly with short frame length transmissions. Alternatively, to bypass the difficult task of designing a good channel code, an iterative receiver (IR) DCSK system is proposed in this paper. A soft demapper suitable for non-coherent M-ary DCSK system is presented, which can work either with or without fading amplitude information. Bit error rate (BER) performance shows its advantages over the non-IR DCSK system in different channel models. The achievable rate curves also indicate that gains relative to non-iterative DCSK system can be achieved with the iterative receiver.