In e-commerce, recommender systems help users find suitable products by leveraging diverse behaviors, e.g., view, cart and buy. In recent years, multi-behavior recommender systems have made strides by integrating auxiliary behaviors with purchase histories to deliver high-quality recommendations. However, most existing methods often fail to identify spurious correlation intents within auxiliary behaviors that conflict with users' target intents. Indiscriminately incorporating such correlations into the prediction of target intents may lead to performance degradation. Toward this end, we propose a Multi-Behavior Intent Disentanglement (MBID) framework based on Information Bottleneck (IB) principle, which focuses on disentangling spurious correlation intents in multi-behavior recommendations. In particular, we design a projection-based intent extraction method to decompose the genuine and spurious correlation intents in auxiliary behaviors. Building on this, we conceive an IB-based multi-intent learning task to disentangle the spurious correlation intents and transfer the genuine correlation intents from auxiliary behaviors into the target behavior, yielding high-quality target intent representations. Experiments on three real-world datasets show MBID significantly outperforms the state-of-the-art baselines by effectively disentangling the spurious correlation intents.

As technology nodes shrink, static timing analysis (STA) must balance accuracy and efficiency to ensure circuit functionality. Graph-based analysis (GBA) is fast but pessimistic, while path-based analysis (PBA) offers higher accuracy with an expensive runtime cost. However, GBA and PBA rely on lookup table (LUT)-based standard cell libraries, introducing accuracy losses compared to accurate SPICE simulations at advanced technology nodes. This work presents GTN-Path, an efficient post-layout path timing prediction method based on waveform propagation and graph transformer network (GTN). GTN-Path captures structural information to accurately predict waveforms by modeling standard cells and interconnects as graphs. Compared to HSPICE simulations, GTN-Path predicts waveforms with $2.98 \%$ error and delay with $2.96 \%$ error, achieving a speedup of $3510 \times$. Additionally, compared with the sign-off STA tool, the GTN-Path achieves a speedup of $12 \times$.

Lookup table (LUT)-based libraries of standard cell characterization is crucial to accurate static timing analysis (STA). However, with the continuous scaling of technology nodes and the increasing complexity of circuit designs, the traditional nonlinear delay model (NLDM) is progressively unable to meet the required accuracy for cell modeling. The current source model (CSM) offers a more precise characterization of cells at advanced nodes and is able to handle arbitrary electrical waveforms. However, the CSM is highly time-consuming because it requires extensive transistor-level simulations, posing severe challenges to efficient standard cell library design. This work presents GTN-Cell, an efficient graph transformer network (GTN)-based method for library-compatible LUT-based CSM waveform prediction of standard cell characterization. GTN-Cell represents the transistor-level structures of standard cells as graphs, learning the local structural information of each cell. By incorporating the transformer encoder into the model and embedding path-related positional encodings, GTN -Cell captures the global relationships between distant nodes within each cell. Compared with HSPICE, the GTN-Cell achieves an average error of 2.27% on predicted voltage waveforms among different standard cells and timing arcs while reducing the number of simulations by 70%.

Abstract Character segmentation is a challenging task for optical character recognition systems. Traditional methods usually utilize rule‐based algorithms but most of them are not applicable in modern intelligent recognition applications that require high accuracy. It is especially the case for text containing Eastern Asian language characters with complex pictograph structures, such as Chinese. To alleviate this problem, this study proposes an encoder–decoder structure‐based multiscale fully convolutional network (MSFCN) model for optical character segmentation. Comparing with other methods, MSFCN can not only effectively extract semantic details from images but also exploit boundary information of intervals between characters, thereby distinguishing characters from a background in pixel level. Extensive experiments have been conducted on two benchmark data sets of ICDAR2013 and MLCS. Obtained results prove that MSFCN achieves state‐of‐the‐art segmentation performance and indicated its practical application value.

The methodology, formulating a reasonable task assignment to find the most suitable workers for a task and achieving the desired objectives, is the most fundamental challenge in spatial crowdsourcing. Many task assignment approaches have been proposed to improve the quality of crowdsourcing results and the number of task assignment and to limit the budget and the travel cost. However, these approaches have two shortcomings: (1) these approaches are commonly based on the attributes influencing the result of task assignment. However, different tasks may have different preferences for individual attributes; (2) the performance and efficiency of these approaches are expected to be improved further. To address the above issues, we proposed a task assignment approach in spatial crowdsourcing based on multiattribute decision-making (TASC-MADM), with the dual objectives of improving the performance as well as the efficiency. Specifically, the proposed approach jointly considers the attributes on the quality of the worker and the distance between the worker and the task, as well as the influence differences caused by the task’s attribute preference. Furthermore, it can be extended flexibly to scenarios with more attributes. We tested the proposed approach in a real-world dataset and a synthetic dataset. The proposed TASC-MADM approach was compared with the RB-TPSC and the Budget-TASC algorithm using the real dataset and the synthetic dataset; the TASC-MADM approach yields better performance than the other two algorithms in the task assignment rate and the CPU cost.

Public transportation agencies call for an intuitive, interactive, and reusable visualization tool to detect patterns of crime (i.e. pickpockets and gangs) or missing commuters on public transportation systems. Few existing visualization techniques have visually explored mobility correlations of targets and their companions, who are characterized in diverse mobility types, by using discrete travel hints extracted from a massive amount of data. To fill this gap, a visual analytical system is provided to conduct a group-based and individual-based exploration of mobility correlations of passengers of interest, based on an auto integration of multiple queries. How passengers differ from or correlate with each other are further examined based on their spatiotemporal distributions in trajectories and ODs. Real-world case studies, as well as user feedback made by 30 participants, demonstrate the effectiveness of the system in detecting specific targets and their companions featured in diverse mobility types, or in characterizing their spatiotemporal aggregation patterns for a further tracking on public transportation systems.

Robust scene recognition serves as an essential task for robots to work within a complex dynamic environment. Considering vision device's limited adaptability in the dark environment, a 3D-laser-based scene recognition approach that extracts and matches SIFT features from Bearing Angle images is proposed, which makes it possible to make full use of both global metric information and local scale-invariant features. This approach can not only cope with irregular disturbances of dynamic objects, but also tackle obvious changes of observation location robustly in a semi-structured environment. An large-scale indoor environment with more than 30 offices is selected as the real-world scenes to test the performance of the proposed approach.