GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy

Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes have dissimilar features and different class labels, and the semantically related nodes might be multi-hop away. To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs. An innovative node relative entropy, which considers node features and structural similarity, is used to measure mutual information between node pairs. In addition, to avoid the sub-optimal solutions caused by mixing useful information and noises of remote nodes, a deep reinforcement learning-based algorithm is developed to optimize the graph topology. This algorithm selects informative nodes and discards noisy nodes based on the defined node relative entropy. Extensive experiments are conducted on seven real-world datasets. The experimental results demonstrate the superiority of GraphRARE in node classification and its capability to optimize the original graph topology.

Can LSH (Locality-Sensitive Hashing) Be Replaced by Neural Network?

With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms. Recent progress shows that neural networks can partly replace traditional data structures. In this paper, we proposed a novel DNN (Deep Neural Network)-based learned locality-sensitive hashing, called LLSH, to efficiently and flexibly map high-dimensional data to low-dimensional space. LLSH replaces the traditional LSH (Locality-sensitive Hashing) function families with parallel multi-layer neural networks, which reduces the time and memory consumption and guarantees query accuracy simultaneously. The proposed LLSH demonstrate the feasibility of replacing the hash index with learning-based neural networks and open a new door for developers to design and configure data organization more accurately to improve information-searching performance. Extensive experiments on different types of datasets show the superiority of the proposed method in query accuracy, time consumption, and memory usage.

MixBCT: Towards Self-Adapting Backward-Compatible Training

The exponential growth of data, alongside advancements in model structures and loss functions, has necessitated the enhancement of image retrieval systems through the utilization of new models with superior feature embeddings. However, the expensive process of updating the old retrieval database by replacing embeddings poses a challenge. As a solution, backward-compatible training can be employed to avoid the necessity of updating old retrieval datasets. While previous methods achieved backward compatibility by aligning prototypes of the old model, they often overlooked the distribution of the old features, thus limiting their effectiveness when the old model's low quality leads to a weakly discriminative feature distribution. On the other hand, instance-based methods like L2 regression take into account the distribution of old features but impose strong constraints on the performance of the new model itself. In this paper, we propose MixBCT, a simple yet highly effective backward-compatible training method that serves as a unified framework for old models of varying qualities. Specifically, we summarize four constraints that are essential for ensuring backward compatibility in an ideal scenario, and we construct a single loss function to facilitate backward-compatible training. Our approach adaptively adjusts the constraint domain for new features based on the distribution of the old embeddings. We conducted extensive experiments on the large-scale face recognition datasets MS1Mv3 and IJB-C to verify the effectiveness of our method. The experimental results clearly demonstrate its superiority over previous methods. Code is available at https://github.com/yuleung/MixBCT

CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided.

Key frames assisted hybrid encoding for photorealistic compressive video sensing

Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition. Numerous algorithms, ranging from regularization-based optimization and deep learning, are being investigated to improve reconstruction quality, but they are still limited by the ill-posed and information-deficient nature of the standard SCI paradigm. To overcome these drawbacks, we propose a new key frames assisted hybrid encoding paradigm for compressive video sensing, termed KH-CVS, that alternatively captures short-exposure key frames without coding and long-exposure encoded compressive frames to jointly reconstruct photorealistic video. With the use of optical flow and spatial warping, a deep convolutional neural network framework is constructed to integrate the benefits of these two types of frames. Extensive experiments on both simulations and real data from the prototype we developed verify the superiority of the proposed method.

Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data

Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases in data science. In this paper, we propose using a generalized sigmoid function to model the distance similarity in both high- and low-dimensional spaces. In particular, the parameter b is introduced to the generalized sigmoid function in low-dimensional space, so that we can adjust the heaviness of the function tail by changing the value of b. Using both simulated and real-world data sets, we show that our proposed method can generate visualization results comparable to those of uniform manifold approximation and projection (UMAP), which is a newly developed manifold learning technique with fast running speed, better global structure, and scalability to massive data sets. In addition, according to the purpose of the study and the data structure, we can decrease or increase the value of b to either reveal the finer cluster structure of the data or maintain the neighborhood continuity of the embedding for better visualization. Finally, we use domain knowledge to demonstrate that the finer subclusters revealed with small values of b are meaningful.

Adaptive Generation of Phantom Limbs Using Visible Hierarchical Autoencoders

This paper proposed a hierarchical visible autoencoder in the adaptive phantom limbs generation according to the kinetic behavior of functional body-parts, which are measured by heterogeneous kinetic sensors. The proposed visible hierarchical autoencoder consists of interpretable and multi-correlated autoencoder pipelines, which is directly derived from the hierarchical network described in forest data-structure. According to specified kinetic script (e.g., dancing, running, etc.) and users' physical conditions, hierarchical network is extracted from human musculoskeletal network, which is fabricated by multiple body components (e.g., muscle, bone, and joints, etc.) that are bio-mechanically, functionally, or nervously correlated with each other and exhibit mostly non-divergent kinetic behaviors. Multi-layer perceptron (MLP) regressor models, as well as several variations of autoencoder models, are investigated for the sequential generation of missing or dysfunctional limbs. The resulting kinematic behavior of phantom limbs will be constructed using virtual reality and augmented reality (VR/AR), actuators, and potentially controller for a prosthesis (an artificial device that replaces a missing body part). The addressed work aims to develop practical innovative exercise methods that (1) engage individuals at all ages, including those with a chronic health condition(s) and/or disability, in regular physical activities, (2) accelerate the rehabilitation of patients, and (3) release users' phantom limb pain. The physiological and psychological impact of the addressed work will critically be assessed in future work.