Sequential recommendation systems have become essential for personalized services in e-commerce and content platforms. While recent research has extended these systems with multi-modal features, existing approaches face three major challenges. First, they inadequately model fine-grained temporal interval distributions, failing to discriminate between high-frequency short intervals and low-frequency long intervals. Second, uniform fusion in the time domain leads to semantic misalignment across modalities because it ignores their inherent differences in the frequency domain. Third, rigid fusion strategies without self-supervised constraints lead to limited representation quality and semantic drift from pretrained embeddings. To address these issues, we propose Adaptive Temporal Expert Routing with Hierarchical Wavelet Enhancement (ATHWE) framework. ATHWE employs exponential saturation time mapping to generate temporally adaptive embeddings. These embeddings guide a sparse mixture of experts to model multi-scale user behavior dynamics. A hierarchical wavelet decomposition with band-specific gating selectively fuses complementary frequency components across modalities. Furthermore, contrastive learning and cluster-preserving objectives preserve semantic information during multi-modal fusion. Extensive experiments on multiple datasets validate the effectiveness of our framework. Our code is available at https://github.com/lulusiyuyu/ATHWE .

Semantic communication is emerging as an effective means of facilitating intelligent and context-aware communication for next-generation communication systems. In this paper, we propose a novel metric called Age of Incorrect Semantics (AoIS) for the transmission of video frames over multiple-input multiple-output (MIMO) channels in a monitoring system. Different from the conventional age-based approaches, we jointly consider the information freshness and the semantic importance, and then formulate a time-averaged AoIS minimization problem by jointly optimizing the semantic actuation indicator, transceiver beamformer, and the semantic symbol design. We first transform the original problem into a low-complexity problem via the Lyapunov optimization. Then, we decompose the transformed problem into multiple subproblems and adopt the alternative optimization (AO) method to solve each subproblem. Specifically, we propose two efficient algorithms, i.e., the successive convex approximation (SCA) algorithm and the low-complexity zero-forcing (ZF) algorithm for optimizing transceiver beamformer. We adopt exhaustive search methods to solve the semantic actuation policy indicator optimization problem and the transmitted semantic symbol design problem. Experimental results demonstrate that our scheme can preserve more than 50% of the original information under the same AoIS compared to the constrained baselines.

Risk scoring systems have been widely deployed in many applications, which assign risk scores to users according to their behavior sequences. Though many deep learning methods with sophisticated designs have achieved promising results, the black-box nature hinders their applications due to fairness, explainability, and compliance consideration. Rule-based systems are considered reliable in these sensitive scenarios. However, building a rule system is labor-intensive. Experts need to find informative statistics from user behavior sequences, design rules based on statistics and assign weights to each rule. In this paper, we bridge the gap between effective but black-box models and transparent rule models. We propose a two-stage framework, CauseRuDi, that distills the knowledge of black-box teacher models into rule-based student models. We design a Monte Carlo tree search-based statistics generation method that maximizes the correlation or dependence between the generated statistics and the teacher model's outputs. We formulate a sequential move game and a simultaneous move coalitional game to generate multiple statistics. Then statistics are composed into logical rules with our proposed neural logical networks by mimicking the outputs of teacher models. We evaluate CauseRuDi on three real-world public datasets and an industrial dataset to demonstrate its effectiveness.

High-resolution Earth observation, particularly from geostationary orbits (GEOs), requires the deployment of optical telescopes with apertures exceeding 10 m or more; however, a universally accepted solution to achieve this goal has yet to be formulated. This article proposes a high-resolution imaging method of checkerboard constellation based on optical pupil plane interferometry (PPI) and phase retrieval algorithms. An innovative solution is provided to address the issue of inadequate spatial frequency sampling in conventional sparse optical PPI: incorporating several checkerboard imagers and a monolithic telescope to create a checkerboard constellation that achieves an ultra-Nyquist sampling rate. Based on this sampling approach, the challenge of phase measurement can be resolved with phase recovery algorithms, which make it possible to generate high-resolution images comparable to that of a super-large-aperture traditional monolithic telescope based on modulus-only measurements. A checkerboard constellation is designed comprising four checkerboard imagers with a maximum baseline of 18 m and one conventional monolithic telescope with an aperture of 3.5 m, which achieves a twice Nyquist sampling rate and provides a ground resolution of 0.5 m at visible wavelengths in GEO. Simulations demonstrate that this setup can produce relatively optimal imaging quality when the signal-to-noise ratio (SNR) is higher than 40. An experiment conducted in the lab confirms the feasibility of this approach. The results show that: 1) high-resolution images can be produced by fusing the high-frequency data from the long-baseline checkerboard imagers with low-resolution data from the monolithic telescope and 2) using optical fibers as core components allows the equivalent aperture of telescopes to be extended to 10 m or even greater, demonstrating the potential scalability of this approach.

Because of the imaging mechanism complexity of long-linear-array and wide-swath whisk-broom thermal infrared spectrometer (TIS) of the first Sustainable Development Goals Satellite (SDGSAT-1), how to achieve a high geometric positioning accuracy (GPA) becomes the core factor in subsequent geometric quantitative applications. Here, in this article, a three-step in-orbit geometric calibration (GC) strategy comprising the estimations of exterior orientation parameters (EOPs), interior orientation parameters (IOPs), and scanning compensation parameters (SCPs) is proposed to correct the geo-location displacements for whisk-broom TIS. First, in accordance with the optical-mechanical structure and pinhole imaging theory, we establish the rigorous geometric positioning model (RGPM) of TIS and analyze the error resources term-by-term along the error propagation link elaborately. Second, the corresponding rigorous geometric calibration model (RGCM) is constructed in detail based on the 2-D look-angle model and the generalized bias correction matrix. Especially for eliminating the systematic nonlinear errors in the scanning direction, a fifth-degree polynomial is put forward to be employed to fit and compensate for the angular measurement errors of the scanning mirror. Finally, a three-step estimation method is presented to estimate the calibration parameters with ground control points (GCPs). Experimental results based on the spatial references of Landsat 8 panchromatic images and version 2 of advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM2) show that the GPA of the proposed method in along-track and cross-track directions can be better than 1.0 pixels for all three bands, which makes a great sense for associated geometric measurements.

The maximum clique problem (MCP) has various applications to reveal the structure and function of graphs. Graphs are constantly updated in the real life. However, no algorithm is specifically designed for dynamic graph. AlthoughMCPin dynamic graphs can be solved by simply invoking a state-of-the-art static approach, such asPMC, when the graph is updated, such an approach of simply re-calculating from scratch is inefficient. The key issue withMCPalgorithm is to find a large clique, namely aseed, as fast as possible. Thus, search space can be pruned based on the seed. Size of the seed greedily found byPMCcannot be guaranteed, as it fluctuates considerably. Moreover, the time required to find a seed underPMCis up to$O(| E| \cdot \Delta (G))$, where$\Delta (G)$is the highest degree inG. In this article, we intend to find a sizable seed by updating the previous maximum clique with the incident vertices of the inserted/deleted edge. Size of the seed now is guaranteed to be no less than$\omega (G^{\prime})\; - \;1$, where$\omega (G^{\prime})$is the size of the maximum clique on the updated graph. Moreover, the seed can be found in a time complexity of$O(\Delta (G)^{2})$. Two other crucial issues related to theMCPin dynamic graphs are refreshing rate and refreshing overhead. After a tight upper bound is imposed on$\omega (G^{\prime})$, the necessity of refreshing is evaluated by comparing the seed with its largest challenger, then unnecessary refreshing is wiped out effectively. The size of the largest challenger is judiciously estimated using a lazy growth strategy. Subsequently, the search space in refreshing is confined on a much smaller subgraph using a local refreshing strategy. Extensive experiments indicate that the proposed approach outperforms the baseline algorithm by approximately one order of magnitude.

Shengli Sun, Qingfeng Sun, Kevin Zhou, Tengchao Lv. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

In order to detect space weak targets in low signal-to-clutter ratio (SCR) environment, this letter presents an effective detection model. At the first stage, two-dimensional least-mean-square preprocessing part is applied to the original image, after which, the suspicious area can be obtained. At the second stage, a novel method based on the contrast mechanism of human visual system called neighborhood saliency map is applied, which improves the local contrast map algorithm and greatly increases the accuracy of neighborhood saliency estimation, meanwhile, effectively enhances targets and suppresses background. Then, a simple threshold segmentation is used to get real targets. Compared with other state-of-the-art algorithms, the proposed algorithm obtains the superior performance in terms of SCR gain, background suppression factor, and detection results (detection rate and false alarm rate). The model proposed in this letter can effectively detect spatial weak point targets in the case of average SCR ≈ 1 or even SCR <; 1 (with minimum SCR is about 0.55).

This letter presents an extended compressive tracking algorithm to increase the stabilization and robustness in scale variation, rotation, and illumination variation. The features are extracted from the Gaussian differential graphs and taken as input signals of compressive sensing. In order to reduce the computational cost, the operating area is narrowed down to the region of interest. The algorithm utilizes a naïve Bayes classifier to get N candidate target locations with N highest classifying scores. Their weighted multiframe cosine similarities with the ground truth object from the initial frame and the most similar object in some adjacent frame are calculated to find the target location in current frame. Experimental results demonstrate the superiority of the proposed algorithm over some state-of-the-art tracking algorithms. It also can efficiently meet the needs of real-time tracking.

Maximal Clique Enumeration (MCE) is a long standing problem in database community. Though it is extensively studied, almost all researches focus on calculating maximal cliques as a one-time effort. MCE on dynamic graph has been rarely discussed so far, the only work on this topic is to maintain maximal cliques with graph evolving. The key within this problem is to find maximal cliques that contains vertices incident to the inserted edge when edge insertion happens. Up to O(W2) candidates are generated in prior method based on Cartesian product, the overall complexity is O(W2n2) where n, W represents the number of vertices and maximal cliques on the graph. Besides, maximality verification of candidate is conducted frequently by global search. Change of maximal clique induced by graph's updating presents some localities. We propose novel local construction strategy to generate candidates based on linear scan, number of candidates is reduced to O(W), the overall complexity is then reduced to O(Wn2). Furthermore, we present heuristics to reduce the cost incurred by maximality verification. Theoretical analysis and experiments on real graphs indicate that our proposals are effective and efficient.

A novel dual-graph-based matching method is proposed in this letter particularly for the multispectral/multidate images with low overlapping areas, similar patterns, or large transformations. First, scale invariant feature transform based matching is improved by normalizing gradient orientations and maximizing the scale ratio similarity of all corresponding points. Next, Delaunay graphs are generated for outlier removal, and the candidate outliers are selected by comparing the distinction of Delaunay graph structures. In order to bring back the inliers removed in Delaunay triangulation matching iterations and to exclude the remaining outliers, the recovery strategy equipped with the dual graph of Delaunay is explored. Inliers located in the corresponding Voronoi cells are recovered to the residual sets. The experimental results demonstrate the accuracy and robustness of the proposed algorithm for various representative remote sensing images.