Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks

The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users' conversions are always delayed in online commercial systems. Although new data are beneficial for continuous training, without complete feedback information, i.e., conversion labels, training algorithms may suffer from overwhelming fake negatives. Existing methods tend to use multitask learning or design data pipelines to solve the delayed feedback problem. However, these methods have a trade-off between data freshness and label accuracy. In this paper, we propose Delayed Feedback Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages, i.e., preparing a data pipeline, building a dynamic graph, and training a CVR prediction model. In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships. The proposed method achieves both data freshness and label accuracy. We conduct extensive experiments on three industry datasets, which validate the consistent superiority of our method

Snapshot Reinforcement Learning: Leveraging Prior Trajectories for Efficiency

Deep reinforcement learning (DRL) algorithms require substantial samples and computational resources to achieve higher performance, which restricts their practical application and poses challenges for further development. Given the constraint of limited resources, it is essential to leverage existing computational work (e.g., learned policies, samples) to enhance sample efficiency and reduce the computational resource consumption of DRL algorithms. Previous works to leverage existing computational work require intrusive modifications to existing algorithms and models, designed specifically for specific algorithms, lacking flexibility and universality. In this paper, we present the Snapshot Reinforcement Learning (SnapshotRL) framework, which enhances sample efficiency by simply altering environments, without making any modifications to algorithms and models. By allowing student agents to choose states in teacher trajectories as the initial state to sample, SnapshotRL can effectively utilize teacher trajectories to assist student agents in training, allowing student agents to explore a larger state space at the early training phase. We propose a simple and effective SnapshotRL baseline algorithm, S3RL, which integrates well with existing DRL algorithms. Our experiments demonstrate that integrating S3RL with TD3, SAC, and PPO algorithms on the MuJoCo benchmark significantly improves sample efficiency and average return, without extra samples and additional computational resources.Comment: Under revie

Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework

A Cell Electrofusion Microfluidic Device Integrated with 3D Thin-Film Microelectrode Arrays

A microfluidic device integrated with 3D thin film microelectrode arrays wrapped around serpentine-shaped microchannel walls has been designed, fabricated and tested for cell electrofusion. Each microelectrode array has 1015 discrete microelectrodes patterned on each side wall, and the adjacent microelectrodes are separated by coplanar dielectric channel wall. The device was tested to electrofuse K562 cells under a relatively low voltage. Under an AC electric field applied between the pair of the microelectrode arrays, cells are paired at the edge of each discrete microelectrode due to the induced positive dielectrophoresis. Subsequently, electric pulse signals are sequentially applied between the microelectrode arrays to induce electroporation and electrofusion. Compared to the design with thin film microelectrode arrays deposited at the bottom of the side walls, the 3D thin film microelectrode array could induce electroporation and electrofusion under a lower voltage. The staggered electrode arrays on opposing side walls induce inhomogeneous electric field distribution, which could avoid multi-cell fusion. The alignment and pairing efficiencies of K562 cells in this device were 99% and 70.7%, respectively. The electric pulse of low voltage (~9 V) could induce electrofusion of these cells, and the fusion efficiency was about 43.1% of total cells loaded into the device, which is much higher than that of the convectional and most existing microfluidics-based electrofusion devices. (C) 2011 American Institute of Physics

Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation

Human-object interactions (HOI) recognition and pose estimation are two closely related tasks. Human pose is an essential cue for recognizing actions and localizing the interacted objects. Meanwhile, human action and their interacted objects' localizations provide guidance for pose estimation. In this paper, we propose a turbo learning framework to perform HOI recognition and pose estimation simultaneously. First, two modules are designed to enforce message passing between the tasks, i.e. pose aware HOI recognition module and HOI guided pose estimation module. Then, these two modules form a closed loop to utilize the complementary information iteratively, which can be trained in an end-to-end manner. The proposed method achieves the state-of-the-art performance on two public benchmarks including Verbs in COCO (V-COCO) and HICO-DET datasets.Comment: AAAI201

CDA: A clustering degree based influential spreader identification algorithm in weighted complex network

Identifying the most influential spreaders in a weighted complex network is vital for optimizing utilization of the network structure and promoting the information propagation. Most existing algorithms focus on node centrality, which consider more connectivity than clustering. In this paper, a novel algorithm based on clustering degree algorithm (CDA) is proposed to identify the most influential spreaders in a weighted network. First, the weighted degree of a node is defined according to the node degree and strength. Then, based on the node weighted degree, the clustering degree of a node is calculated in respect to the network topological structure. Finally, the propagation capability of a node is achieved by accounting the clustering degree of the node and the contribution from its neighbors. In order to evaluate the performance of the proposed CDA algorithm, the susceptible-infected-recovered model is adopted to simulate the propagation process in real-world networks. The experiment results have showed that CDA is the most effective algorithm in terms of Kendall's tau coefficient and with the highest accuracy in influential spreader identification compared with other algorithms such as weighted degree centrality, weighted closeness centrality, evidential centrality, and evidential semilocal centrality

The Global Existence of Solutions in Time for a Chemotaxis Model with Two Chemicals

This paper concerns the uniform boundedness and global existence of solutions in time for the chemotaxis model with two chemicals. We prove the system has global existence of solutions in time for any dimension n

Translational progress on tumor biomarkers

There is an urgent need to apply basic research achievements to the clinic. In particular, mechanistic studies should be developed by bench researchers, depending upon clinical demands, in order to improve the survival and quality of life of cancer patients. To date, translational medicine has been addressed in cancer biology, particularly in the identification and characterization of novel tumor biomarkers. This review focuses on the recent achievements and clinical application prospects in tumor biomarkers based on translational medicine.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115962/1/tca12294.pd

Ensemble multiboost based on ripper classifier for prediction of imbalanced software defect data

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR-SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm