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Fully revealing the mathmatical structure of quantum contextuality is a significant task, while some known contextuality theories are only applicable for rank-1 projectors. That is because they adopt the observable-based definitions. This paper analyses the challenges faced by some known contextuality theories, and establishes an event-based contextuality theory with partial Boolean algebra to overcome them. The theory can handle the scenarios composed of general projectors and observables, and provides a unified mathematical structure to investigate the hierarchy of quantum contextuality. It also introduces a tool to extend some known results from rank-1 cases to general cases. For example, we get a Kochen-Specker set with 12 projectors from the Cabello-Estebaranz-Garcia set with 18 vectors.

Partial Boolean algebra underlies the quantum logic as an important tool for quantum contextuality. We propose the notion \textit{atom graphs} to reveal the graph structure of partial Boolean algebra for quantum systems by proving that (i) the partial Boolean algebras for quantum systems are determined by their atom graphs; (ii) the states on atom graphs can be extended uniquely to the partial Boolean algebras, and (iii) each exclusivity graph is an induced graph of an atom graph. (i) and (ii) show that the quantum systems are uniquely determined by their atom graphs. which proves the reasonability of graphs as the models of quantum experiments. (iii) establishes a connection between partial Boolean algebra and exclusivity graphs, and introduces a method to express the exclusivity experiments more precisely. We also present a general and parametric description for Kochen-Specker theorem based on graphs, which gives a type of non-contextuality inequality for KS contextuality., Comment: 20 pages, 13 figures

This paper focuses on training a robust RGB-D registration model without ground-truth pose supervision. Existing methods usually adopt a pairwise training strategy based on differentiable rendering, which enforces the photometric and the geometric consistency between the two registered frames as supervision. However, this frame-to-frame framework suffers from poor multi-view consistency due to factors such as lighting changes, geometry occlusion and reflective materials. In this paper, we present NeRF-UR, a novel frame-to-model optimization framework for unsupervised RGB-D registration. Instead of frame-to-frame consistency, we leverage the neural radiance field (NeRF) as a global model of the scene and use the consistency between the input and the NeRF-rerendered frames for pose optimization. This design can significantly improve the robustness in scenarios with poor multi-view consistency and provides better learning signal for the registration model. Furthermore, to bootstrap the NeRF optimization, we create a synthetic dataset, Sim-RGBD, through a photo-realistic simulator to warm up the registration model. By first training the registration model on Sim-RGBD and later unsupervisedly fine-tuning on real data, our framework enables distilling the capability of feature extraction and registration from simulation to reality. Our method outperforms the state-of-the-art counterparts on two popular indoor RGB-D datasets, ScanNet and 3DMatch. Code and models will be released for paper reproduction.

The classical belief revision framework, as proposed by Alchourron, Gardenfors, and Makinson, involves the revision of a theory based on eight postulates. In this paper, we focus on the exploration of a revision theory grounded in quantum mechanics, referred to as the natural revision theory. There are two reasoning modes in quantum systems: static intuitionistic reasoning, which incorporates contextuality, and dynamic reasoning, which is achieved through projection measurement. We combine the advantages of two intuitionistic quantum logic frameworks, as proposed by D{\"o}ring and Coecke, respectively. Our goal is to establish a truth-value assignment for intuitionistic quantum logic that not only aligns with the inherent characteristics of quantum mechanics but also supports truth-value reasoning. The natural revision theory is then investigated based on this approach. We introduce two types of revision operators that correspond to the two reasoning modes in quantum systems: static and dynamic revision. Furthermore, we highlight the distinctions between these two operators. Shifting away from classical revision paradigms, we consider the revision of consequence relations in intuitionistic quantum logic. We demonstrate how, within the natural revision theory framework, both revision operators collectively influence the consequence relations. Notably, the outcomes of revision process are impacted by the sequence in which these interweaved operators are deployed.

This work focuses on the dual-arm object rearrangement problem abstracted from a realistic industrial scenario of Cartesian robots. The goal of this problem is to transfer all the objects from sources to targets with the minimum total completion time. To achieve the goal, the core idea is to develop an effective object-to-arm task assignment strategy for minimizing the cumulative task execution time and maximizing the dual-arm cooperation efficiency. One of the difficulties in the task assignment is the scalability problem. As the number of objects increases, the computation time of traditional offline-search-based methods grows strongly for computational complexity. Encouraged by the adaptability of reinforcement learning (RL) in long-sequence task decisions, we propose an online task assignment decision method based on RL, and the computation time of our method only increases linearly with the number of objects. Further, we design an attention-based network to model the dependencies between the input states during the whole task execution process to help find the most reasonable object-to-arm correspondence in each task assignment round. In the experimental part, we adapt some search-based methods to this specific setting and compare our method with them. Experimental result shows that our approach achieves outperformance over search-based methods in total execution time and computational efficiency, and also verifies the generalization of our method to different numbers of objects. In addition, we show the effectiveness of our method deployed on the real robot in the supplementary video., Comment: 7 pages, 9 figures, conference

The quasiparticle scattering interference (QSI) is intimately related to the nature of the quasiparticle and of its interplay with a variety of electronic orders and superconductivity. Here starting from the microscopic octet scattering model, the nature of QSI in cuprate superconductors is studied in the $T$-matrix approach. In particular, a new method of the inversion of matrix is developed to accurately derive the $T$-matrix for various kinds of impurities, and then the obtained $T$-matrix is employed to calculate the local density of states (LDOS). It is shown that the overall features of the LDOS modulation can be described qualitatively by taking into account the quasiparticle scattering from a single impurity on the kinetic-energy-driven homogeneous superconducting-state, where the QSI scattering wave vectors ${\bf q}_{i}$ and the related QSI peak dispersions are internally consistent within the octet scattering model. However, the pronounced QSI peaks in the momentum-space LDOS modulation pattern for a single impurity are smeared heavily in the case for multiple impurities, and then the momentum-space LDOS modulation for multiple impurities exhibits a speckle pattern. Moreover, the momentum-space LDOS modulation for Gaussian-random-distribution of on-site impurity at a relatively large deviation displays a similar behavior of the LDOS modulation for multiple impurities. The theory also indicates that the impurity weight linearly increases with the increase of the impurity-scattering strength for the weak scattering strength and tends to saturate in the strong scattering strength, while it decreases with the increase of the impurity-scattering screening length for the short screening-length and saturates in the long screening-length., Comment: 21 pages, 12 figures, added figures, discussions, and references. Accepted for publication in Physical Review B

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises \textit{contamination-resilient continuous supervisory signals}. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies. The source code is available at https://github.com/xuhongzuo/rosas/., Comment: Accepted by Information Processing and Management (IP&M)

The microwave surface impedance measurements on cuprate superconductors provide the crucial information of the effect of the impurity scattering on the quasiparticle transport, however, the full understanding of the effect of the impurity scattering on the quasiparticle transport is still a challenging issue. Here based on the microscopic octet scattering model, the effect of the impurity scattering on the low-temperature microwave conductivity in cuprate superconductors is investigated in the self-consistent $T$-matrix approach. The impurity-dressed electron propagator obtained in the Fermi-arc-tip approximation of the quasiparticle excitations and scattering processes is employed to derive the electron current-current correlation function by taking into account the impurity-induced vertex correction. It is shown that the microwave conductivity spectrum is a non-Drude-like, with a sharp cusp-like peak extending to zero-energy and a high-energy tail falling slowly with energy. Moreover, the microwave conductivity decreases with the increase of the impurity concentration or with the increase of the strength of the impurity scattering potential. In a striking contrast to the dome-like shape of the doping dependence of the superconducting transition temperature, the microwave conductivity exhibits a reverse dome-like shape of the doping dependence. The theory also show that the highly unconventional features of the microwave conductivity are generated by both the strong electron correlation and impurity-scattering effects., Comment: 15 pages, 6 figures. arXiv admin note: text overlap with arXiv:2206.06884

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration. Specifically, our approach adaptively penalizes uncertain predictions to restrain irregular samples in anomaly contamination during optimization, while simultaneously encouraging confident predictions on regular samples to ensure effective normality learning. This largely alleviates the negative impact of anomaly contamination. Our approach also creates native anomaly examples via perturbation to simulate time series abnormal behaviors. Through discriminating these dummy anomalies, our one-class learning is further calibrated to form a more precise normality boundary. Extensive experiments on ten real-world datasets show that our model achieves substantial improvement over sixteen state-of-the-art contenders., Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)

The impurity is inherently manifest in cuprate superconductors, as cation substitution or intercalation is necessary for the introduction of charge carriers, and its influence on the electronic state is at the heart of a great debate in physics. Here based on the microscopic octet scattering model, the influence of the impurity scattering on the electronic structure of cuprate superconductors is investigated in terms of the self-consistent T-matrix approach. The impurity scattering self-energy is evaluated firstly in the Fermi-arc-tip approximation of the quasiparticle excitations and scattering processes, and the obtained results show that the decisive role played by the impurity scattering self-energy in the particle-hole channel is the further renormalization of the quasiparticle band structure with a reduced quasiparticle lifetime, while the impurity scattering self-energy in the particle-particle channel induces a strong deviation from the d-wave behaviour of the superconducting gap, leading to the existence of a finite gap over the entire electron Fermi surface. Moreover, these impurity scattering self-energies are employed to study the exotic features of the line-shape in the quasiparticle excitation spectrum and the autocorrelation of the quasiparticle excitation spectra, and the obtained results are then compared with the corresponding experimental data. The theory therefore also indicates that the unconventional features of the electronic structure in cuprate superconductors is generated by both the strong electron correlation and impurity scattering., Comment: 17 pages, 8 figures, replotted figure 3 and added discussions. Accepted for publication in Physical Review B

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation forest. We introduce a new representation scheme that utilises casually initialised neural networks to map original data into random representation ensembles, where random axis-parallel cuts are subsequently applied to perform the data partition. This representation scheme facilitates high freedom of the partition in the original data space (equivalent to non-linear partition on subspaces of varying sizes), encouraging a unique synergy between random representations and random partition-based isolation. Extensive experiments show that our model achieves significant improvement over state-of-the-art isolation-based methods and deep detectors on tabular, graph and time series datasets; our model also inherits desired scalability from iForest., Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)

Website fingerprinting attack (WFA) aims to deanonymize the website a user is visiting through anonymous networks channels (e.g., Tor). Despite of remarkable progress in the past years, most existing methods make implicitly a couple of artificial assumptions that (1) only a single website (i.e., single-tab) is visited each time, and (2) website fingerprinting data are pre-trimmed into a single trace per website manually. In reality, a user often open multiple tabs for multiple websites spontaneously. Indeed, this multi-tab WFA (MT-WFA) setting has been studied in a few recent works, but all of them still fail to fully respect the real-world situations. In particular, the overlapping challenge between website fingerprinting has never been investigated in depth. In this work, we redefine the problem of MT-WFA as detecting multiple monitored traces, given a natural untrimmed traffic data including monitored traces, unmonitored traces, and potentially unconstrained overlapping between them. This eliminates the above assumptions, going beyond the conventional single website fingerprint classification perspective taken by all previous WFA methods. To tackle this realistic MT-WFA problem, we formulate a novel Website Fingerprint Detection (WFD) model capable of detecting accurately the start and end points of all the monitored traces and classifying them jointly, given long, untrimmed raw traffic data. WFD is end-to-end, with the trace localization and website classification integrated in a single unified pipeline. To enable quantitative evaluation in our MT-WFA setting, we introduce new performance metrics. Extensive experiments on several newly constructed benchmarks show that our WFD outperforms the state-of-the-art alternative methods in both accuracy and efficiency by a large margin, even with a very small training set. Code is available at https://github.com/WFDetector/WFDetection, Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible