E-commerce platforms usually present an ordered list, mixed with several organic items and an advertisement, in response to each user's page view request. This list, the outcome of ad auction and allocation processes, directly impacts the platform's ad revenue and gross merchandise volume (GMV). Specifically, the ad auction determines which ad is displayed and the corresponding payment, while the ad allocation decides the display positions of the advertisement and organic items. The prevalent methods of segregating the ad auction and allocation into two distinct stages face two problems: 1) Ad auction does not consider externalities, such as the influence of actual display position and context on ad Click-Through Rate (CTR); 2) The ad allocation, which utilizes the auction-winning ad's payment to determine the display position dynamically, fails to maintain incentive compatibility (IC) for the advertisement. For instance, in the auction stage employing the traditional Generalized Second Price (GSP), even if the winning ad increases its bid, its payment remains unchanged. This implies that the advertisement cannot secure a better position and thus loses the opportunity to achieve higher utility in the subsequent ad allocation stage. Previous research often focused on one of the two stages, neglecting the two-stage problem, which may result in suboptimal outcomes.

Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot. Notice that, platforms (e.g., e-commerce platforms, video platforms, food delivery platforms and so on) usually have multiple entrances for different categories and some entrances have few visits. Data from these entrances has low coverage, which makes it difficult for the agent to learn. To address this challenge, we propose Similarity-based Hybrid Transfer for Ads Allocation (SHTAA), which effectively transfers samples as well as knowledge from data-rich entrance to data-poor entrance. Specifically, we define an uncertainty-aware similarity for MDP to estimate the similarity of MDP for different entrances. Based on this similarity, we design a hybrid transfer method, including instance transfer and strategy transfer, to efficiently transfer samples and knowledge from one entrance to another. Both offline and online experiments on Meituan food delivery platform demonstrate that the proposed method could achieve better performance for data-poor entrance and increase the revenue for the platform.

Hyperspectral (HS) and multispectral (MS) image fusion has attracted great attention during the past decades. Numerous of fusion methods have been developed and shown their effectiveness particularly on simulated data. Nonetheless, for real remote sensing data, the different acquisition times or conditions result in a serious spectral distortion and severely affect the fusion quality. Yet very few works have considered this issue. In this paper, a spectral modulation (SM) method is proposed to better maintain the spectral information of the HS data when fusing with MS data. The goal is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Experiments on two HS and MS data sets acquired by different platforms demonstrate that the proposed method is beneficial to the spectral fidelity and spatial enhancement of the fused image compared with some state-of-the-art fusion techniques.

A computational method for suppressing clutter and generating clear microwave images of targets is proposed in this paper. It is suitable of SAR for special applications. The nonlinear recursive model is introduced into the SAR operation principle, and the cubature Kalman filter (CKF) algorithm is used to estimate target and clutter responses in each azimuth position based on their states before, which are both assumed to be Gaussian distributions. Neyman-Pearson criteria based optimal waveforms are designed repeatedly as the sensor flight along its azimuth path and used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. Thus, with fewer disturbances from the clutter response, we can generate the SAR image with traditional azimuth matched filters. Simulations show that the clutter suppression filter significantly reduces the clutter response, and our algorithm greatly improves the signal-to-clutter plus noise ratio (SCNR) of the SAR image based on different clutter suppression filter parameters. As such, this new algorithm may be preferable for special target imaging when prior information on the target is available.

A method to solve multi-target classification problems with unknown clutter parameters is proposed in this paper. The unknown parameter is estimated and synthesized at each observation, and probability of each hypothesis is updated. Subsequently, the optimal waveform for the next illumination is designed based on NP criteria, and the final decision is made based on the sequential probability ratio testing. Simulated results are presented based on our method and show that the optimal waveform-based sequential testing can be decided through reduction of the average illumination number. Furthermore, results indicate a significant improvement over the non-optimal waveforms.

Radar performance improvement through waveform optimization has been an ongoing topic of research recent years. In this paper, we use the optimal waveform design method to deal with the moving target in the clutter and noise. Neyman-Pearson detector criterion is used to maximize the probability of target detection. The optimal waveform is then designed theoretically corresponding to the velocity of target and clutter/noise power spectrum density. Simple CW signals can produce maximum detectability based on different noise PSD situations. Simulated results are presented based on our method and improvement in image is approached. Finally, the conclusions are drawn based on our analysis and simulations.

In this paper, we combine the waveform design method with the synthetic aperture algorithm to suppress clutter and generate clear microwave images of targets. The linear recursive model is introduced into the SAR operation principle and Kalman filter algorithm is used to estimate target and clutter responses in each azimuth direction based on their states before, which both are assumed to be Gaussian distributions. Optimal waveforms based on NP criteria are designed repeatedly and used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. The simulations show that our algorithm can significantly reduce the clutter response while targets are imaged.

An innovative algorithm to reconstruct complex target-induced azimuth envelope in synthetic aperture radar (SAR) system is proposed. Unlike the assumption in conventional SAR imaging algorithms, target's backscattering coefficient can hardly remain constant when synthetic aperture time is long enough. In order to fully understand the target feature, we extract both amplitude and phase information of target-induced azimuth envelope from SAR raw data. In this paper, we formulate range migration curve (RMC) with a parametric model and implement curve fitting to estimate these parameters. The input pixels of curve fitting process is extracted from range compression result of raw data. Applying the idea of random sample consensus (RANSAC), this algorithm classifies pixels according to the target's RMC they belongs to, and extracts target's feature information at the same time. Experimental results are conducted to validate this algorithm.

In this paper, we proposed a clutter suppression algorithm for SAR imaging due to different target power spectrum density (PSD) and clutter PSD in azimuth direction. The optimal waveform of the SAR system is designed according to the prior-knowledge of the target and clutter, an amplitude limiter set in frequency domain is used to suppress the clutter response and 2-dimentional pulse compression is then used to the SAR imaging. Simulated results are presented based on our method which is shown great improvement in target image over clutter image. Then conclusions are drawn based on our analysis and simulations.

In this paper, we apply the optimum waveform design method to meet the application of elastic targets detection and aim at enhance the target detection rate. The target is assumed to be Gaussian elastic targets and the clutter is assumed to be a stationary Gaussian random process. The detection problem then is described using Neyman-Pearson Detector. The waveform design solutions for elastic targets are given theoretically, and numerical simulated results are presented based on different transmit energy and comparison results between optimum signal and LFM signal are shown. Finally, the conclusions are drawn based on our analysis and simulations.