This paper is concerned with the matching stability problem across different decoder layers in DEtection TRansformers (DETR). We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR. To address this problem, we show that the most important design is to use and only use positional metrics (like IOU) to supervise classification scores of positive examples. Under the principle, we propose two simple yet effective modifications by integrating positional metrics to DETR’s classification loss and matching cost, named position-supervised loss and position-modulated cost. We verify our methods on several DETR variants. Our methods show consistent improvements over baselines. By integrating our methods with DINO, we achieve 50.4 and 51.5 AP on the COCO detection benchmark using ResNet-50 backbones under 1× (12 epochs) and 2× (24 epochs) training settings, achieving a new record under the same setting. We achieve 63.8 AP on COCO detection test-dev with a Swin-Large backbone. Our code will be made available at https://github.com/IDEA-Research/Stable-DINO.

A continuous attractor of a recurrent neural network (RNN) is a set of connected stable equilibrium points. Continuous attractors have been used to describe the encoding of continuous stimuli in neural networks. Dynamic behaviors of continuous attractors of RNNs exhibit interesting properties. This brief desires to derive explicit representations of continuous attractors of RNNs. Representations of continuous attractors of linear RNNs as well as linear-threshold (LT) RNNs are obtained under some conditions. These representations could be looked at as solutions of continuous attractors of the networks. Such results provide clear and complete descriptions to the continuous attractors.

Collaborative filtering recommendation with implicit feedbacks (i.e., clicks, views, check-ins) has been gaining increasing attention in various real applications. Tagging information is the common resource to complement implicit feedbacks to assist

Traffic prediction plays a significant part in creating intelligent cities such as traffic management, urban computing, and public safety. Nevertheless, the complex spatio-temporal linkages and dynamically shifting patterns make it somewhat challenging. Existing mainstream traffic prediction approaches heavily rely on graph convolutional networks and sequence prediction methods to extract complicated spatio-temporal patterns statically. However, they neglect to account for dynamic underlying correlations and thus fail to produce satisfactory prediction results. Therefore, we propose a novel Self-Adaptive Spatio-Temporal Graph Convolutional Network (SASTGCN) for traffic prediction. A self-adaptive calibrator, a spatio-temporal feature extractor, and a predictor comprise the bulk of the framework. To extract the distribution bias of the input in the self-adaptive calibrator, we employ a self-supervisor made of an encoder–decoder structure. The concatenation of the bias and the original characteristics are provided as input to the spatio-temporal feature extractor, which leverages a transformer and graph convolution structures to learn the spatio-temporal pattern, and then applies a predictor to produce the final prediction. Extensive trials on two public traffic prediction datasets (METR-LA and PEMS-BAY) demonstrate that SASTGCN surpasses the most recent techniques in several metrics.

With the widespread application of machine learning techniques in malware detection, researchers have proposed various adversarial attack methods to generate adversarial examples (AEs) of malware, thereby evading detection. Previous studies have shown that the reinforcement learning (RL) framework can enable black-box attacks by performing a sequence of function-preserving operations, which produces functional evasive malware samples. However, it is difficult to obtain the useful guidance and feedbacks from the environment for agent training in the black-box scenario, which results in the RL framework being unable to learn the effective evasion policy. In this paper, we propose the Shapley prior and establish a prior-guidance-based RL framework, namely PSP-Mal, to generate AEs against Portable Executable (PE) malware detectors. Our framework improves on existing methods in three aspects: 1) We explore feature effects of the black-box model by computing Shapley values and further propose the Shapley prior to represent the expected impact of operations. 2) A novel prioritized experience utilization mechanism is established regarding the Shapley prior guidance in the RL framework. 3) The actions are expanded into item-content pairs and we use the Thompson sampling to choose effective content, which helps to reduce randomness and ensure repeatability. We compare the attack performance of our framework with other methods, and experimental results demonstrate that our algorithm is more effective. The evasion rates of PSP-Mal against the LightGBM models trained on EMBER and SOREL-20M reach 76.88% and 72.03%, respectively.