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1. Domain Reasoning in TopKAT

   https://doi.org/10.4230/LIPICS.ICALP.2024.157  

   Zhang, Cheng; Azevedo_de_Amorim, Arthur; Gaboardi, Marco (July 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Bringmann, Karl; Grohe, Martin; Puppis, Gabriele; Svensson, Ola (Ed.)

   TopKAT is the algebraic theory of Kleene algebra with tests (KAT) extended with a top element. Compared to KAT, one pleasant feature of TopKAT is that, in relational models, the top element allows us to express the domain and codomain of a relation. This enables several applications in program logics, such as proving under-approximate specifications or reachability properties of imperative programs. However, while TopKAT inherits many pleasant features of KATs, such as having a decidable equational theory, it is incomplete with respect to relational models. In other words, there are properties that hold true of all relational TopKATs but cannot be proved with the axioms of TopKAT. This issue is potentially worrisome for program-logic applications, in which relational models play a key role. In this paper, we further investigate the completeness properties of TopKAT with respect to relational models. We show that TopKAT is complete with respect to (co)domain comparison of KAT terms, but incomplete when comparing the (co)domain of arbitrary TopKAT terms. Since the encoding of under-approximate specifications in TopKAT hinges on this type of formula, the aforementioned incompleteness results have a limited impact when using TopKAT to reason about such specifications. 

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2. Ghost Domain Reloaded: Vulnerable Links in Domain Name Delegation and Revocation

   https://doi.org/10.14722/ndss.2023.23005  

   Li, Xiang; Liu, Baojun; Bai, Xuesong; Zhang, Mingming; Zhang, Qifan; Li, Zhou; Duan, Haixin; Li, Qi (March 2023, Annual Network and Distributed System Security Symposium)
3. Domain-Specific and Domain-General Individual Differences in Visual Object Recognition

   https://doi.org/10.1177/0963721417737151  

   Gauthier, Isabel (April 2018, Current Directions in Psychological Science)
4. Domain-indexing variational Bayes: Interpretable domain index for domain adaptation

   Xu, Zihao; Hao, Guang-Yuan; He, Hao; Wang, Hao (April 2023, Eleventh International Conference on Learning Representations (ICLR))
5. Learning in the Frequency Domain

   https://doi.org/10.1109/CVPR42600.2020.00181  

   Xu, Kai; Qin, Minghai; Sun, Fei; Wang, Yuhao; Chen, Yen-Kuang; Ren, Fengbo (January 2020, IEEE Conference on Computer Vision and Pattern Recognition)

   null (Ed.)

   Deep neural networks have achieved remarkable success in computer vision tasks. Existing neural networks mainly operate in the spatial domain with fixed input sizes. For practical applications, images are usually large and have to be downsampled to the predetermined input size of neural networks. Even though the downsampling operations reduce computation and the required communication bandwidth, it removes both redundant and salient information obliviously, which results in accuracy degradation. Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed without accuracy loss. The proposed method of learning in the frequency domain leverages identical structures of the well-known neural networks, such as ResNet-50, MobileNetV2, and Mask R-CNN, while accepting the frequency-domain information as the input. Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size. Specifically for ImageNet classification with the same input size, the proposed method achieves 1.60% and 0.63% top-1 accuracy improvements on ResNet-50 and MobileNetV2, respectively. Even with half input size, the proposed method still improves the top-1 accuracy on ResNet-50 by 1.42%. In addition, we observe a 0.8% average precision improvement on Mask R-CNN for instance segmentation on the COCO dataset. 

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