Short-term origin-destination (OD) demand prediction is critical in managing the multimodal transportation system. The joint short-term OD demand prediction for multimodal systems faces three challenges: (1) data availability: real-time OD demand is not available for prediction; (2) sparsity and high-dimensionality of OD demand: the OD demand is spatiotemporal sparse and usually high dimension; (3) impact of different transportation modes: the future OD demand for one mode is affected by others, and extensive studies primarily focus on a single transportation mode, overlooking the influence between different modes. To tackle these challenges, we propose a multitask learning and Partial-Differential-based model to predict the short-term Multimodal Transport Systems OD demand (PD-MTSOD), which includes (1) an OD demand learner to estimate real-time OD demand, (2) data aggregation with hypergraph attention to capture spatiotemporal features, and (3) OD demand decomposition into self-generated increment, other-modes-generated increment, and real-time OD demand, and use partial-differential-based methods to model intermodal correlations. Extensive tests on Beijing and New York city's multimodal systems show that PD-MTSOD surpasses baseline models. In addition, we prove the benefits of joint considering multiple transportation and explore the correlations of different transportation modes. This paper offers a reliable method for understanding multimodal transportation systems.

Achieving accurate traffic prediction is a fundamental but crucial task in the development of current intelligent transportation systems. These limitations pose significant challenges for the efficient deployment and operation of deep learning models on large-scale datasets. To address these challenges, we propose a cost-effective graph-free Multilayer Perceptron (MLP) based model M3-Net for traffic prediction. Extensive experiments conducted on multiple real datasets demonstrate the superiority of the proposed model in terms of prediction performance and lightweight deployment. Our code is available at https://github.com/jinguangyin/M3_NET

Originally proposed as a conceptual metaphor, the Waddington landscape was used to illustrate the directional nature of embryonic development and the relative stability of distinct developmental states. While the Waddington landscape offers a valuable conceptual framework for understanding cellular dynamics, its quantitative reconstruction remains a significant challenge in systems biology and biophysics. Recent methodological advances in single-cell omics technologies, computational modeling approaches, and nonlinear dynamical systems theory have facilitated progress toward quantitative reconstruction of the Waddington landscape, thereby transforming this heuristic metaphor into a predictive theoretical framework. In this review, we summarize the theoretical foundations of the Waddington landscape, categorize current computational and mathematical approaches for the Waddington landscape reconstruction. Meanwhile, we highlight the potential applications and inherent limitations of these approaches in characterizing cellular behaviors, predicting cell fate decisions, and modulating developmental trajectories.

Due to the rapid development of urban rail transit and the expansion of new lines, accurately predicting the passenger flow of newly expanding subway lines has become increasingly important. The lack of historical data and long forecasting times have led to insufficient accuracy in previous studies when directly predicting inflow at new line stations. To address these challenges, this study proposes a method to decompose inflow features into trend features and scale features, and introduces a model named Semi-Conv-Transformer, based on semi-supervised learning and deep learning neural networks, for prediction of newly expanding subway lines. This innovative approach divides the inflow prediction of newly expanding subway lines into three parts: 1) enhancing the dataset using semi-supervised learning for data augmentation, 2) predicting trend features and scale features with Conv-Transformer deep learning model, and 3) combining the trend features and scale features of station passenger flow to obtain the required inflow data. The proposed method was tested on a new subway line operated in Nanning, China. In this experiment, the model achieved better experimental performance than previous methods and achieved higher accuracy.

Breast carcinoma (BRCA) is a leading cause of mortality in women worldwide. Understanding the driving factors behind BRCA initiation, progression, and evolution is crucial. This study proposes a novel method to identify stage-related driving factors in BRCA. By utilizing stage-specific functional interaction networks, the multi-omics features and PPI were integrated. A novel rumor-mongering model is introduced to smooth the stage-specific networks and the gravity model is used to balance gene interactions. The top 100 gravity interactions are identified as driving factors. Through biomolecular and enrichment analyses, these driving factors are shown to play a crucial role in BRCA progression. Furthermore, a hybrid hierarchical evolution network illustrates the stage-evolutionary role of driving factors, while a biological functional evolution network demonstrates functional changes in BRCA progression. The proposed method exhibits superior enrichment performance, particularly within targeted pathways, providing valuable insights into the underlying mechanisms of BRCA.

Express pickup and delivery systems play crucial roles in contemporary urban areas. Couriers within these systems retrieve packages from designated Areas of Interest (AOI) that the express company assigns to them during specific time intervals. The express company traditionally employs historical pickup request data for executing AOI assignments (or pickup request assignments) for couriers, and these assignments are conventionally static and do not evolve over time However, future pickup requests display significant temporal variations. Employing historical data for future assignments is, therefore, somewhat impractical. Furthermore, even if we were to predict future pickup requests beforehand and subsequently employ these predictions for assignments, this two-stage approach proves to be both impractical and trivial, potentially harboring drawbacks. For example, the better prediction results may not necessarily guarantee better clustering outcomes. To address these challenges, we introduce an intelligent end-to-end predict-then-optimize clustering method that simultaneously forecasts future pickup requests for AOIs and dynamically allocates AOIs to couriers through clustering. Initially, we propose a deep learning-based prediction model for predicting order quantities within AOIs. Subsequently, we present a differential constrainedK-means clustering method for AOI clustering based on the prediction results. Finally, we introduce a one-stage end-to-end predict-then-optimize clustering approach for the rational, dynamic, and intelligent allocation of AOIs to couriers. Our results demonstrate that this one-stage predict-then-optimize method significantly enhances optimization outcomes, namely the quality of clustering results. This study offers valuable insights that are relevant to predict-then-optimize-related tasks, particularly when addressing stochastic assignment problems within all types of express systems.

Accurate passenger flow prediction of urban rail transit systems (URT) is essential for improving the performance of intelligent transportation systems, especially during the epidemic. How to dynamically model the complex spatiotemporal dependencies of passenger flow is the main issue in achieving accurate passenger flow prediction during the epidemic. To solve this issue, this paper proposes a brand-new transformer-based architecture called COVID-19 Spatial-Temporal Transformer Network (COV-STFormer) under the encoder-decoder framework specifically for COVID-19. Concretely, a modified self-attention mechanism named Causal-Convolution ProbSparse Self-Attention (CPSA) is developed to model the complex temporal dependencies of passenger flow. A novel Adaptive Multi-Graph Convolution Network (AMGCN) is introduced to capture the complex and dynamic spatial dependencies by leveraging multiple graphs in a self-adaptive manner. Additionally, the Multi-source Data Fusion block fuses the passenger flow data, COVID-19 confirmed case data, and the relevant social media data to study the impact of COVID-19 to passenger flow. Experiments on real-world passenger flow datasets demonstrate the superiority of COV-STFormer over the other thirteen state-of-the-art methods. Several ablation studies are carried out to verify the effectiveness and reliability of our model structure. Results can provide critical insights for the operation of URT systems.

Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existing works utilize sophisticated temporal modeling approaches to incorporate with graph convolution networks (GCNs) for capturing short-term and long-term spatio-temporal dependencies. However, these separated modules with complicated designs could restrict effectiveness and efficiency of spatio-temporal representation learning. Furthermore, most previous works adopt the fixed graph construction methods to characterize the global spatio-temporal relations, which limits the learning capability of the model for different time periods and even different data scenarios. To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction. Specifically, we design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG) module to capture the short-term and long-term spatio-temporal correlations by stacking deeper layers with dilation factors in an increasing order. Further, we propose a graph structure search approach to automatically construct the spatio-temporal synchronous graph that can adapt to different data scenarios. Extensive experiments on four real-world datasets demonstrate that our model can achieve about 10% improvements compared with the state-of-art methods. Source codes are available athttps://github.com/jinguangyin/Auto-DSTSGN.

Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for public agencies to better understand the performance of the URT system. This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understanding the system-wide real-time operation state. The emerging data-driven techniques, such as the computational graph (CG) method in the machine learning field, provide a new solution for solving this problem. In this study, we first formulate a data-driven estimation optimization framework to estimate the link travel time and station waiting time. Then, we cast the estimation optimization model into a CG-based framework to solve the optimization problem and obtain the estimation results. The methodology is verified on a synthetic URT network and applied to a real-world URT network using the synthetic and real-world AFC data, respectively. Results show the robustness and effectiveness of the CG-based framework. To the best of our knowledge, this is the first time that the CG is applied to the URT. This study can provide critical insights to better understand the operational state of URT.

Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep learning models provide good insight into improving prediction precision. Therefore, we propose a deep learning architecture combining the residual network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) (called “ResLSTM”) to forecast short-term passenger flow in urban rail transit on a network scale. First, improved methodologies of the ResNet, GCN, and attention LSTM models are presented. Then, the model architecture is proposed, wherein ResNet is used to capture deep abstract spatial correlations between subway stations, GCN is applied to extract network topology information, and attention LSTM is used to extract temporal correlations. The model architecture includes four branches for inflow, outflow, graph-network topology, as well as weather conditions and air quality. To the best of our knowledge, this is the first time that air-quality indicators have been taken into account, and their influences on prediction precision quantified. Finally, ResLSTM is applied to the Beijing subway using three time granularities (10, 15, and 30 min) to conduct short-term passenger flow forecasting. A comparison of the prediction performance of ResLSTM with those of many state-of-the-art models illustrates the advantages and robustness of ResLSTM. Moreover, a comparison of the prediction precisions obtained for time granularities of 10, 15, and 30 min indicates that prediction precision increases with increasing time granularity. This study can provide subway operators with insight into short-term passenger flow forecasting by leveraging deep learning models.

The emerging High Efficiency Video Coding (HEVC) standard adopts many advanced techniques with flexible combinations, which enables HEVC to achieve about 50% bit-rate reduction for similar perceptual video quality relative to the prior video coding standard H.264/Advanced Video Coding. However, the enormously increased encoding complexity of HEVC inevitably becomes one of the greatest challenges for real-time applications. Among all the factors resulting in the increase in encoding complexity of HEVC, the quad-tree structure for coding units (CUs) with different sizes and accordingly a large number of prediction modes is one critical reason. Thus, it is greatly desired to develop a fast mode decision method for HEVC to reduce the computational complexity. In this paper, considering that HEVC employs the quad-tree structure, and the distortion of each sub-CU can indicate whether the current mode is suitable for current CU, we explore the relationship between the impossible modes and the distribution of the distortions to help the encoder skip checking the unnecessary modes. Besides, since the residual values can reflect the prediction result directly, we propose a method to skip some motion estimation operations according to the distribution of the residuals. Experimental results show that the proposed method can save about 77% of encoding time with only about a 4.1% bit-rate increase compared with HM16.4 anchor, while compared with the fast mode decision method adopted in HM16.4, the proposed algorithm can save about 48% of encoding time with only about a 2.9% bit-rate increase.

The High Efficiency Video Coding (HEVC) with the transform bypass mode is simple but inefficient for lossless coding. For this reason, we propose a novel transform to further eliminate the redundancy between residues of different blocks in intra prediction. Dependent on intra prediction modes, the proposed transform is adaptable to exploit correlations of residues formed by different modes. In order to accurately obtain parameters of the transform matrix, an approach similar to the Wiener filtering method is adopted. Experimental results show that on top of the lossless coding mode in HEVC, our method offers the performance with a 7.4% bit-rate reduction on average for All Intra Main configuration. Compared with other representative algorithms, our proposal still shows an improvement in the compression ratio, without substantial increases of computational complexity in the encoder or decoder.

Rate control is a useful tool for video coding, especially in real-time communication applications. Most of existing rate control algorithms are based on the R-Q model, which characterizes the relationship between bitrate R and quantization Q , under the assumption that Q is the critical factor on rate control. However, with the video coding schemes becoming more and more flexible, it is very difficult to accurately model the R-Q relationship. In fact, we find that there exists a more robust correspondence between R and the Lagrange multiplier λ . Therefore, in this paper, we propose a novel λ -domain rate control algorithm based on the R-λ model, and implement it in the newest video coding standard high efficiency video coding (HEVC). Experimental results show that the proposed λ -domain rate control can achieve the target bitrates more accurately than the original rate control algorithm in the HEVC reference software as well as obtain significant R-D performance gain. Thanks to the high accurate rate control algorithm, hierarchical bit allocation can be enabled in the implemented video coding scheme, which can bring additional R-D performance gain. Experimental results demonstrate that the proposed λ -domain rate control algorithm is effective for HEVC, which outperforms the R-Q model based rate control in HM-8.0 (HEVC reference software) by 0.55 dB on average and up to 1.81 dB for low delay coding structure, and 1.08 dB on average and up to 3.77 dB for random access coding structure. The proposed λ -domain rate control algorithm has already been adopted by Joint Collaborative Team on Video Coding and integrated into the HEVC reference software.

On board compression coding of remote sensing images requires simple encoder and small storage, etc. However, the existing scheme such as JPEG2000 which is based on DWT has a complex encoder, and many algorithms based on DWT with a lower storage requirement have been researched. In this paper, we propose a novel line-based distributed lossless compression scheme for remote sensing stereo images with low storage costs and light complexity at the encoder. All the lines are encoded independently so that the required storage of the encoder is only one line. In order to achieve low encoding complexity, distributed coding techniques are used to exploit the spatial correlation and inter-view correlation of the stereo images. At the encoder, sub-sampled lines are successively encoded and transmitted. At the decoder, side information is generated with the knowledge of decoded sub-sampled lines and other previously decoded lines for the first view, and the second view will use the previous lines of the first view to remove the inter-view redundancy. In addition, line-based adaptive filter is performed to capture the spatial characteristics and template matching is used to remove the inter-view redundancy. Experimental results on high-resolution remote sensing stereo images demonstrate that the proposed scheme is comparable to JPEG2000 with respect to the compression performance, but with much lower encoding complexity and storage requirement.