Submodular maximization is an important building block for developing algorithms in many areas such as machine learning and data mining. Due to the NP-hardness of the problem, analysis of submodular maximization algorithms typically provides pessimistic worst-case approximation factors only. It is not easy to evaluate how close a produced solution is to an optimal one for a given problem instance. In this paper, we develop new data-dependent upper bounds for submodular maximization with a knapsack constraint. We theoretically prove that they dominate the optimal solution and empirically demonstrate their advantages in certifying how close to optimal a solution is through experiments with real-world datasets.

Next Basket Recommendation (NBR) predicts unordered item sets for users' next purchases, crucial for grocery shopping and online retail scenarios. However, existing NBR methods face two critical challenges: (1) They neglect the threshold-meeting phenomenon, such as common add-to-cart behaviors where users intentionally include seemingly irrelevant items to meet promotional or free shipping price thresholds, which is a widespread phenomenon in reality but has not been a dedicated research focus in the recommendation domain; (2) Even though many works study repetitive and exploratory recommendations, they still lack a theoretically analyzable mechanism to directionally decouple these patterns. This may limit the model's performance. To address these issues, we propose DiffNBR, the first diffusion-model-based framework for NBR. DiffNBR employs two denoising diffusion probabilistic models (DDPMs) to jointly learn users' purchasing behaviors in in spatial and temporal dimensions, modeling the latent compositional strategies the dynamic evolution behind phenomena like threshold-meeting. Moreover, we integrate the information bottleneck theory to enforce directional decoupling between repetition and exploration by explicitly regulating information flows. Specifically, our framework constrains the model's generative representation to focus on learning the exploratory patterns, while the habitual repurchase representation is responsible for the repetitive patterns. Extensive experiments on four real-world datasets show that DiffNBR outperforms the state-of-the-art (SOTA) methods.

Accurate, timely and wide-ranging radar Quantitative Precipitation Estimation (QPE) is crucial for effective water resource management and climate research. However, the insufficient consideration of surface meteorological conditions such as moisture information leads to significant biases for existing QPE methods. This paper proposes a novel quantitative precipitation estimation network based on hierarchical multi-branch convolutional blocks (MCB-Net). The network incorporates relative humidity data and radar data into a multimodal-fusion architecture, effectively extracting information from diverse data sources and correcting precipitation loss due to evaporation. Additionally, a series of MCB blocks are designed to replace the traditional convolutional operation with multi-branch convolutional units, enabling MCB-Net to capture complicated spatial and temporal features related to precipitation. The indices including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Ratio (MBR) and Correlation Coefficient (CC) are adopted to evaluate the performance of the proposed method. Experimental results demonstrate that MCB-Net outperforms conventional method and other deep learning based QPE networks including Volume-to-Point CNN network, U-Net and ResNet. The visualized results illustrate that the proposed network can effectively mitigate precipitation overestimation and enhance the accuracy of precipitation estimation.

Video anomaly detection plays a crucial role in the field of autonomous driving to ensure driving safety. Most existing video anomaly detection methods exhibit mediocre performance when analyzing video frames captured by dynamic cameras. To enhance their performance on dynamic cameras, we propose a video anomaly detection method based on Swin-Unet to separately predict optical flow and images cropped with ROI in the form of dual GAN. By integrating the predicted results of optical flow and ROI-cropped images, the model’s ability to learn dynamic information is significantly improved. The experimental results indicate that our proposed method outperforms state-of-the-art methods in terms of AUC on the Car Crash dataset and RetroTrucks dataset.

Internet of Things (IoT) software is used to control the distributed hardware of the underlying network and provide a reliable operating platform for various services. In production system, diversity IoT software provides multiple services, flows of different software run in parallel on the same IoT platform. Thus, system-level network parameter configurations may not be suitable for all service needs. In this paper, we focus on the challenge of the personally parameterizing transmission unit size and congestion windows (CWND) in flow control. We propose deeper flow control software model (DeepFC) for finer-grained flow control than traditional algorithms. DeepFC consist of two parts: (i) Since system-level transmission unit size may degrade network performance due to frequent fragmentation, we combine path MTU (PMTU) and deep reinforcement learning (DRL) to predict fine-grained flow-level transmission unit size. (ii) Transmission unit size is related to CWND in flow control. The fine-grained transmission unit size needs fine-grained congestion control solution. In DeepFC, we consider the mutual coupling between transmission unit size and CWND parameter configuration to further improve network performance. Experimental results show that DeepFC can reduce fragmentation by 67.8% compared to the protocols with system-level transmission unit size, flow completion time can be reduced by 20.93%, and throughput can be increased by 19.96% compared to the average of benchmark algorithms.

The correct distinction between precipitation echo (PE) and non-precipitation echo (NPE) of weather radar is one of the key steps in the quantitative precipitation estimation (QPE). Based on the high spatiotemporal resolution data of the Himawari-8 satellite and the precipitation data of the rain gauge, this paper proposes a fuzzy logic algorithm (FLA) to remove NPE in weather radar data through statistically analyzing the probability density distribution of the satellite temperature of black body (TBB) according to different precipitation scenarios. Two methods, i.e. the FLA and the threshold method (TM), are compared and analyzed by using the data in August 2019. The critical success index (CSI) score of the FLA is 0.056 higher than that of the TM (0.796 vs. 0.74), and the probability of detection (POD) score is 0.058 higher than that of the TM (0.802 vs. 0.744). The results show that the proposed method can effectively improve the quality of weather radar data.

Radar quantitative precipitation estimation (QPE) is one of important applications of Doppler weather radar. The use of the rain gauge data to correct and improve the accuracy of radar QPE is important for weather forecasting. Based on observation data of rain gauges, this study proposes two machine learning methods to correct radar QPE product. The Support Vector Regression (SVR) and Random Forest (RF) models are used as the regression model. The rain gauge data is used as the ground truth. The radar QPE data is divided into three intervals: 0–10 mm/h, 10–30 mm/h, and> 30 mm/h. One traditional method, i.e., the PDF method, is selected for comparison. The experimental results show that the machine learning methods achieve better performance than the PDF method. The scatter plot shows that SVR effectively mitigate the overestimation problem which is a main problem of the original radar QPE product.

Fine-grained sketch-based image retrieval is considered as an ideal alternative to keyword-based image retrieval and image search by image due to the rich and easily accessible characteristics of sketches. Previous works always follow a paradigm that first extracting image global feature with convolution neural network and then optimizing the model with triplet loss. Many efforts on narrowing the domain gap and extracting discriminating features are made by these works. However, they ignored that the global feature is not good at capturing fine-grained details. In this paper, we emphasize the local features are more discriminating than global feature in FG-SBIR and explore an effective way to utilize local features. Specifically, Local Aligned Network (LA-Net) is proposed first, which solves FG-SBIR by directly aligning the mid-level local features. Experiment manifests it can beat all previous baselines and is easy to implement. LA-Net is hoped to be a new strong baseline for FG-SBIR. Next, Dynamic Local Aligned Network (DLA-Net) is proposed to enhance LA-Net. The question of spatial misalignment caused by the abstraction of the sketch is not considered by LA-Net. To solve this question, a dynamic alignment mechanism is introduced into LA-Net. This new mechanism makes the sketch interact with the photo and dynamically decide where to align according to the different photos. The Experiment indicates DLA-Net successfully addresses the question of spatial misalignment. It gains a significant performance boost over LA-Net and outperforms the state-of-the-art in FG-SBIR. To the best of our knowledge, DLA-Net is the first model that beats humans on all datasets---QMUL FG-SBIR, QMUL Handbag, and Sketchy.