In this paper, we investigate task offloading and computing in an urban mobile edge computing (MEC) system assisted by an aerial intelligent reflecting surface (AIRS). To enhance information freshness while saving energy, we formulate a joint optimization problem to minimize the weighted sum of average age of information (AoI) and total energy consumption by jointly optimizing task offloading decisions, resource allocation, and AIRS configuration including its deployment position, phase shifts, and panel size, subject to offloading quality and computing deadline constraints. To tackle the resulting mixed-integer and nonconvex problem, we develop a hierarchical optimization framework based on the objective priority and variable coupling relations. Under this framework, the problem is solved in two stages using quadratic penalty, numerical analysis, and convex optimization techniques. Specifically, an AoI-aware task offloading policy is first designed to maximize information freshness; subsequently, given the obtained offloading policy, an AIRS configuration and resource allocation scheme is proposed to minimize energy consumption. Simulation results demonstrate that the proposed approach significantly outperforms benchmark schemes in reducing both AoI and energy consumption, while exhibiting superior convergence performance.

Timely and energy-efficient task offloading and computing can be challenging in mobile edge computing (MEC) networks when the communication links between devices and edge servers are unreliable. In this paper, we apply multiple aerial intelligent reflective surfaces (AIRSs) to assist devices in offloading computing tasks to the edge server in a timely and reliable manner in the MEC network with poor offloading environments. To evaluate the timeliness of offloading and computing, we derive the evolution process of age-of-information (AoI) under the random arrival of the computing tasks. The association between devices and AIRSs, offloading order of computing tasks, design of IRS phase shift, and allocation of communication and computing resources are jointly optimized to minimize the average AoI and system energy consumption given computing requirements. To solve the formulated minimization problem, we propose an efficient problem-solving framework to cope with the challenge of variable coupling. Firstly, we derive a closed-form optimal IRS phase shift to provide a reliable offloading environment. Then, we optimize the association between devices and AIRSs while reducing the offloading complexity and balancing the number of devices associated with each AIRS. Finally, we develop a low-complexity task offloading and resource allocation algorithm based on convex optimization to attain a good enough solution. Simulation results indicate the proposed solution outperforms benchmarks in timeliness and energy saving.

In this paper, we study a robust beamforming design in a downlink reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) communication system with consideration of bounded channel uncertainty. The goal of the design is to minimize the transmit power by employing the symbol-level precoding (SLP) at the base station (BS), while satisfying the quality-of-service requirements of the primary users (PU) and secondary user (SU). Unlike most existing works where RIS is only a signal reflector, in this paper, RIS also operates as a transmitter and delivers the secondary information to SU by switching the reflecting beamformers. In single-PU scenario, the secondary information recovery (SIR) scheme is proposed and a power minimization problem is formulated. To tackle the non-convex problem, we decompose the problem into two sub-problems to alternately optimize the transmit and reflecting beamformers. Then, two algorithms namely the penalty-based algorithm and the monotone accelerated projected gradient-based algorithm are proposed to address the unit-modulus constraint on the reflecting beamformer. The problem is further extended to the multi-PU scenario and the extended SIR scheme is provided. Finally, the simulation results demonstrate the effectiveness of our proposed algorithms and exhibit the advantages of the SIR scheme.

The millimeter-wave (mmWave) train-to-ground (T2G) communications is an essential enabling technology for future intelligent railways, where the acquisition of beam alignment information is one of the most challenging and significant issues. Hence, in this paper, we investigate the optimal beamformers and performance bounds for the mmWave T2G beam alignment with the aid of train location information. We first propose a mmWave T2G system model, which can be used by arbitrary array geometry and identifies a clear relationship between the T2G scenario and the wireless channel. Then, based on the Cramer Rao bound (CRB), we provide two bounds characterizing the average and worst minimum mean square error (MMSE) of beam alignment with the bounded error model of train location. Next, two non-convex optimization problems are formulated aiming to find the transmitting beamformers that can minimize the bounds, which is solved optimally by relaxation and recovery techniques. Finally, numerical simulations are conducted to validate the proposed beamformers and bounds for the mmWave T2G beam alignment. In particular, the results show that the MSE performance of optimal beamformers converges to the bounds by applying the maximum likelihood estimator (MLE) in the high SNR regime.

Aerial intelligent reconfigurable surface (IRS) is a promising technology to enhance channel quality in data delivery. In this article, we study an aerial IRS deployment problem to enable timely and reliable data delivery in a remote Internet of Things (IoT) scenario, in which an IRS mounted on an unmanned aerial vehicle (UAV) is adopted as a mobile relay to assist devices in uploading data to the base station (BS). The objective is to minimize the average Age of Information (AoI) of the data received by the BS over time by jointly determining the aerial IRS deployment position and phase shift, transmit power of devices, and data uploading time. Under the requirements of peak AoI (PAoI) and communication reliability, we formulate an average AoI minimization problem. Since the nonlinear relations among optimization variables make the formulated problem nonconvex and intractable to solve, we propose a block coordinate descent (BCD)-based iterative algorithm which decomposes the formulated problem into several subproblems. The variables are optimized in each subproblem individually in an alternately iterative manner to attain a near-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm in improving the information freshness compared with the benchmark schemes.

Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems-e.g., retrieving a fashion item given a reference image plus text feedback from a user.Prior works on multimodal fashion tasks have either been limited by the data in individual benchmarks, or have leveraged generic vision-and-language pre-training but have not taken advantage of the characteristics of fashion data.Additionally, these works have mainly been restricted to multimodal understanding tasks.To address these gaps, we make two key contributions.First, we propose a novel fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs.We show the tripletbased tasks are an effective addition to standard multimodal pre-training tasks.Second, we propose a flexible decoder-based model architecture capable of both fashion retrieval and captioning tasks.Together, our model design and pre-training approach are competitive on a diverse set of fashion tasks, including crossmodal retrieval, image retrieval with text feedback, image captioning, relative image captioning, and multimodal categorization.

In order to adapt to user requirement changes at runtime, software provides adaptable operations through user interfaces to change software functionality. We suggest the FleXible Point (FXP), flexible changes, flexible degree, flexible force, and flexible distance to evaluate the effect of such user interfaces. An approach and a case are given to illustrate the evaluation and setting of the FXPs. Quantitative relationship between flexible point on user interface and software flexibility is discussed and expressed. The approach can be used as a guide to adjust, improve, and to compare the FXPs on user interfaces. It is a direction for managers to arrange different levels of manipulators to increase the FXP efficiency and bring user interface flexibility into play.