arXiv subjects

Kang Li

Publications and source records attributed to Kang Li.

At least 145 records · Page 8Linked to original sources

arXiv2022

The Haagerup property for twisted groupoid dynamical systems

We introduce the Haagerup property for twisted groupoid $C^*$-dynamical systems in terms of naturally defined positive-definite operator-valued multipliers. By developing a version of `the Haagerup trick' we prove that this property is equivalent to the Haagerup property of the reduced crossed product $C^*$-algebra with respect to the canonical conditional expectation $E$. This extends a theorem of Dong and Ruan for discrete group actions, and implies that a given Cartan inclusion of separable $C^*$-algebras has the Haagerup property if and only if the associated Weyl groupoid has the Haagerup property in the sense of Tu. We use the latter statement to prove that every separable $C^*$-algebra which has the Haagerup property with respect to some Cartan subalgebra satisfies the Universal Coefficient Theorem. This generalises a recent result of Barlak and Li on the UCT for nuclear Cartan pairs.

math.OA↗

arXiv2022

Unsupervised Segmentation for Terracotta Warrior Point Cloud (SRG-Net)

The repairing work of terracotta warriors in Emperor Qinshihuang Mausoleum Site Museum is handcrafted by experts, and the increasing amounts of unearthed pieces of terracotta warriors make the archaeologists too challenging to conduct the restoration of terracotta warriors efficiently. We hope to segment the 3D point cloud data of the terracotta warriors automatically and store the fragment data in the database to assist the archaeologists in matching the actual fragments with the ones in the database, which could result in higher repairing efficiency of terracotta warriors. Moreover, the existing 3D neural network research is mainly focusing on supervised classification, clustering, unsupervised representation, and reconstruction. There are few pieces of researches concentrating on unsupervised point cloud part segmentation. In this paper, we present SRG-Net for 3D point clouds of terracotta warriors to address these problems. Firstly, we adopt a customized seed-region-growing algorithm to segment the point cloud coarsely. Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds. Finally, we combine the SRG algorithm with our improved CNN(convolution neural network) using a refinement method. This pipeline is called SRG-Net, which aims at conducting segmentation tasks on the terracotta warriors. Our proposed SRG-Net is evaluated on the terracotta warrior data and ShapeNet dataset by measuring the accuracy and the latency. The experimental results show that our SRG-Net outperforms the state-of-the-art methods. Our code is available at https://github.com/hyoau/SRG-Net.

cs.CV↗

arXiv2022

Recursive 3D Segmentation of Shoulder Joint with Coarse-scanned MR Image

For diagnosis of shoulder illness, it is essential to look at the morphology deviation of scapula and humerus from the medical images that are acquired from Magnetic Resonance (MR) imaging. However, taking high-resolution MR images is time-consuming and costly because the reduction of the physical distance between image slices causes prolonged scanning time. Moreover, due to the lack of training images, images from various sources must be utilized, which creates the issue of high variance across the dataset. Also, there are human errors among the images due to the fact that it is hard to take the spatial relationship into consideration when labeling the 3D image in low resolution. In order to combat all obstacles stated above, we develop a fully automated algorithm for segmenting the humerus and scapula bone from coarsely scanned and low-resolution MR images and a recursive learning framework that iterative utilize the generated labels for reducing the errors among segmentations and increase our dataset set for training the next round network. In this study, 50 MR images are collected from several institutions and divided into five mutually exclusive sets for carrying five-fold cross-validation. Contours that are generated by the proposed method demonstrated a high level of accuracy when compared with ground truth and the traditional method. The proposed neural network and the recursive learning scheme improve the overall quality of the segmentation on humerus and scapula on the low-resolution dataset and reduced incorrect segmentation in the ground truth, which could have a positive impact on finding the cause of shoulder pain and patient's early relief.

eess.IV↗

arXiv2022

Counterfactual Regret Minimization for Anti-jamming Game of Frequency Agile Radar

The competition between radar and jammer is one emerging issue in modern electronic warfare, which in principle can be viewed as a non-cooperative game with two players. In this work, the competition between a frequency agile (FA) radar and a noise-modulated jammer is considered. As modern FA radar adopts coherent processing with several pulses, the competition is hence in a multiple-round way where each pulse can be modeled as one round interaction between the radar and jammer. To capture such multiple-round property as well as imperfect information inside the game, i.e., radar and jammer are unable to know the upcoming signal, we propose an extensive-form game formulation for such competition. Since the number of game information states grows exponentially with respect to number of pulses, finding Nash Equilibrium (NE) strategies may be a computationally intractable task. To effectively solve the game, a learning-based algorithm called deep Counterfactual Regret Minimization (CFR) is utilized. Numerical simulations demonstrates the effectiveness of deep CFR algorithm for approximately finding NE and obtaining the best response strategy.

eess.SP↗

arXiv2022

SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching

Lung nodule detection from 3D Computed Tomography scans plays a vital role in efficient lung cancer screening. Despite the SOTA performance obtained by recent anchor-based detectors using CNNs for this task, they require predetermined anchor parameters such as the size, number, and aspect ratio of anchors, and have limited robustness when dealing with lung nodules with a massive variety of sizes. To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network that is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters. The SCPM-Net consists of two novel components: sphere representation and center points matching. First, to match the nodule annotation in clinical practice, we replace the commonly used bounding box with our proposed bounding sphere to represent nodules with the centroid, radius, and local offset in 3D space. A compatible sphere-based intersection over-union loss function is introduced to train the lung nodule detection network stably and efficiently. Second, we empower the network anchor-free by designing a positive center-points selection and matching process, which naturally discards pre-determined anchor boxes. An online hard example mining and re-focal loss subsequently enable the CPM process to be more robust, resulting in more accurate point assignment and mitigation of class imbalance. In addition, to better capture spatial information and 3D context for the detection, we propose to fuse multi-level spatial coordinate maps with the feature extractor and combine them with 3D squeeze-and-excitation attention modules. Experimental results on the LUNA16 dataset showed that our proposed framework achieves superior performance compared with existing anchor-based and anchor-free methods for lung nodule detection.

cs.CV↗

arXiv2021

Measured expanders

By measured graphs we mean graphs endowed with a measure on the set of vertices. In this context, we explore the relations between the appropriate Cheeger constant and Poincaré inequalities. We prove that the so-called Cheeger inequality holds in two cases: when the measure comes from a random walk, or when the measure has a bounded measure ratio. Moreover, we also prove that our measured (asymptotic) expanders are generalised expanders introduced by Tessera. Finally, we present some examples to demonstrate relations and differences between classical expander graphs and the measured ones. The current paper is motivated primarily by our previous work on the rigidity problem for Roe algebras.

math.MG↗

arXiv2021

Purely leptonic decays of the ground charged vector mesons

The study of the purely leptonic decays of the ground charged vector mesons is very interesting and significant in determining the CKM matrix elements, obtaining the decay constant of vector mesons, examining the lepton flavor universality, and searching for new physics beyond the standard model. These purely leptonic decays of the ground charged vector mesons are induced by the weak interactions within the standard model, and usually have very small branching ratios, ${\cal B}(ρ^{-}{\to}{\ell}^{-}ν_{\ell})$ ${\sim}$ ${\cal O}(10^{-13})$, ${\cal B}(K^{{\ast}-}{\to}{\ell}^{-}ν_{\ell})$ ${\sim}$ ${\cal O}(10^{-13})$, ${\cal B}(D_{d}^{{\ast}-}{\to}{\ell}^{-}ν_{\ell})$ ${\sim}$ ${\cal O}(10^{-10})$, ${\cal B}(B_{u}^{{\ast}-}{\to}{\ell}^{-}ν_{\ell})$ ${\sim}$ ${\cal O}(10^{-10})$, ${\cal B}(D_{s}^{{\ast}-}{\to}{\ell}^{-}ν_{\ell})$ ${\sim}$ ${\cal O}(10^{-6})$ and ${\cal B}(B_{c}^{{\ast}-}{\to}{\ell}^{-}ν_{\ell})$ ${\sim}$ ${\cal O}(10^{-6})$. Inspired by the potential prospects of LHCb, Belle-II, STCF, CEPC and FCC-ee experiments, we discussed the probabilities of experimental investigation on these purely leptonic decays. It is found that the measurements of these decays might be possible and feasible with the improvement of data statistics, analytical technique, and measurement precision in the future. (1) With the hadron-hadron collisions, the purely leptonic decays of $ρ^{-}$, $K^{{\ast}-}$, $D_{d,s}^{{\ast}-}$ and $B_{u,c}^{{\ast}-}$ mesons might be accessible at LHC experiments. (2) With the $e^{+}e^{-}$ collisions, the purely leptonic decays of $D_{d,s}^{{\ast}-}$ and $B_{u,c}^{{\ast}-}$ mesons might be measurable with over $10^{12}$ $Z^{0}$ bosons available at CEPC and FCC-ee experiments. In addition, the $D_{d,s}^{{\ast}-}$ ${\to}$ ${\ell}^{-}ν_{\ell}$ decays could also be studied at Belle-II and SCTF experiments.

hep-ph↗

arXiv2021

SoftTRR: Protect Page Tables Against RowHammer Attacks using Software-only Target Row Refresh

Rowhammer attacks that corrupt level-1 page tables to gain kernel privilege are the most detrimental to system security and hard to mitigate. However, recently proposed software-only mitigations are not effective against such kernel privilege escalation attacks. In this paper, we propose an effective and practical software-only defense, called SoftTRR, to protect page tables from all existing rowhammer attacks on x86. The key idea of SoftTRR is to refresh the rows occupied by page tables when a suspicious rowhammer activity is detected. SoftTRR is motivated by DRAM-chip-based target row refresh (ChipTRR) but eliminates its main security limitation (i.e., ChipTRR tracks a limited number of rows and thus can be bypassed by many-sided hammer). Specifically, SoftTRR protects an unlimited number of page tables by tracking memory accesses to the rows that are in close proximity to page-table rows and refreshing the page-table rows once the tracked access count exceeds a pre-defined threshold. We implement a prototype of SoftTRR as a loadable kernel module, and evaluate its security effectiveness, performance overhead, and memory consumption. The experimental results show that SoftTRR protects page tables from real-world rowhammer attacks and incurs small performance overhead as well as memory cost.

cs.CR↗

arXiv2021

Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach

In the context of transition towards cleaner and sustainable energy production, microgrids have become an effective way for tackling environmental pollution and energy crisis issues. With the increasing penetration of renewables, how to coordinate demand response and renewable generations is a critical and challenging issue in the field of microgrid scheduling. To this end, a bi-level scheduling model is put forward for isolated microgrids with consideration of multi-stakeholders in this paper, where the lower- and upper-level models respectively aim to the minimization of user cost and microgrid operational cost under real-time electricity pricing environments. In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper- levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels. After that, the real-time prices updated by the upper-level model and the electricity plans determined by the lower-level model will be alternately iterated between the upper- and lower- levels through the real-time pricing mechanism to obtain an optimal scheduling plan. The test results show that the proposed method can coordinate the uncertainty of renewable generations with demand response strategies, thereby achieving a balance between the interests of microgrid and users; and that by leveraging demand response, the flexibility of the load side can be fully exploited to achieve peak load shaving while maintaining the balance of supply and demand. In addition, the Jaya-IPM algorithm is proven to be superior to the traditional hybrid intelligent algorithm (HIA) and the CPLEX solver in terms of optimization results and calculation efficiency.

eess.SY↗

arXiv2021

Self-paced Resistance Learning against Overfitting on Noisy Labels

Noisy labels composed of correct and corrupted ones are pervasive in practice. They might significantly deteriorate the performance of convolutional neural networks (CNNs), because CNNs are easily overfitted on corrupted labels. To address this issue, inspired by an observation, deep neural networks might first memorize the probably correct-label data and then corrupt-label samples, we propose a novel yet simple self-paced resistance framework to resist corrupted labels, without using any clean validation data. The proposed framework first utilizes the memorization effect of CNNs to learn a curriculum, which contains confident samples and provides meaningful supervision for other training samples. Then it adopts selected confident samples and a proposed resistance loss to update model parameters; the resistance loss tends to smooth model parameters' update or attain equivalent prediction over each class, thereby resisting model overfitting on corrupted labels. Finally, we unify these two modules into a single loss function and optimize it in an alternative learning. Extensive experiments demonstrate the significantly superior performance of the proposed framework over recent state-of-the-art methods on noisy-label data. Source codes of the proposed method are available on https://github.com/xsshi2015/Self-paced-Resistance-Learning.

cs.CV↗

arXiv2021

On the structure of asymptotic expanders

In this paper, we use geometric tools to study the structure of asymptotic expanders and show that a sequence of asymptotic expanders always admits a "uniform exhaustion by expanders". It follows that asymptotic expanders cannot be coarsely embedded into any $L^p$-space, and that asymptotic expanders can be characterised in terms of their uniform Roe algebra. Moreover, we provide uncountably many new counterexamples to the coarse Baum--Connes conjecture. These appear to be the first counterexamples that are not directly constructed by means of spectral gaps. Finally, we show that vertex-transitive asymptotic expanders are actually expanders. In particular, this gives a $C^*$-algebraic characterisation of expanders for vertex-transitive graphs.

math.MG↗

arXiv2021

The type semigroup, comparison and almost finiteness for ample groupoids

We prove that a minimal second countable ample groupoid has dynamical comparison if and only if its type semigroup is almost unperforated. Moreover, we investigate to what extent a not necessarily minimal almost finite groupoid has an almost unperforated type semigroup. Finally, we build a bridge between coarse geometry and topological dynamics by characterizing almost finiteness of the coarse groupoid in terms of a new coarsely invariant property for metric spaces, which might be of independent interest in coarse geometry. As a consequence, we are able to construct new examples of almost finite principal groupoids lacking other desirable properties, such as amenability or even a-T-menability. This behaviour is in stark contrast to the case of principal transformation groupoids associated to group actions.

math.DS↗

arXiv2021

Tracial approximate divisibility and stable rank one

We show that every separable simple tracially approximately divisible $C^*$-algebra has strict comparison, is either purely infinite, or has stable rank one. As a consequence, we show that every (non-unital) finite simple ${\cal Z}$-stable $C^*$-algebra has stable rank one.

math.OA↗

arXiv2021

Unsupervised Segmentation for Terracotta Warrior with Seed-Region-Growing CNN (SRG-Net)

The repairing work of terracotta warriors in Emperor Qinshihuang Mausoleum Site Museum is handcrafted by experts, and the increasing amounts of unearthed pieces of terracotta warriors make the archaeologists too challenging to conduct the restoration of terracotta warriors efficiently. We hope to segment the 3D point cloud data of the terracotta warriors automatically and store the fragment data in the database to assist the archaeologists in matching the actual fragments with the ones in the database, which could result in higher repairing efficiency of terracotta warriors. Moreover, the existing 3D neural network research is mainly focusing on supervised classification, clustering, unsupervised representation, and reconstruction. There are few pieces of researches concentrating on unsupervised point cloud part segmentation. In this paper, we present SRG-Net for 3D point clouds of terracotta warriors to address these problems. Firstly, we adopt a customized seed-region-growing algorithm to segment the point cloud coarsely. Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds. Finally, we combine the SRG algorithm with our improved CNN using a refinement method. This pipeline is called SRG-Net, which aims at conducting segmentation tasks on the terracotta warriors. Our proposed SRG-Net is evaluated on the terracotta warriors data and ShapeNet dataset by measuring the accuracy and the latency. The experimental results show that our SRG-Net outperforms the state-of-the-art methods. Our code is shown in Code File 1~\cite{Srgnet_2021}.

cs.CV↗

arXiv2021

Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing

Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems. Testers need to handcraft the virtual driving scenes and configure various environmental settings like surrounding traffic, weather conditions, etc. Due to the huge amount of configuration possibilities, the human efforts are subject to the inefficiency in detecting flaws in industry-class autonomous driving system. This paper proposes a coverage-driven fuzzing technique to automatically generate diverse configuration parameters to form new driving scenes. Experimental results show that our fuzzing method can significantly reduce the cost in deriving new risky scenes from the initial setup designed by testers. We expect automated fuzzing will become a common practice in virtual testing for autonomous driving systems.

cs.AI↗

arXiv2021

On Explainability of Graph Neural Networks via Subgraph Explorations

We consider the problem of explaining the predictions of graph neural networks (GNNs), which otherwise are considered as black boxes. Existing methods invariably focus on explaining the importance of graph nodes or edges but ignore the substructures of graphs, which are more intuitive and human-intelligible. In this work, we propose a novel method, known as SubgraphX, to explain GNNs by identifying important subgraphs. Given a trained GNN model and an input graph, our SubgraphX explains its predictions by efficiently exploring different subgraphs with Monte Carlo tree search. To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs. To expedite computations, we propose efficient approximation schemes to compute Shapley values for graph data. Our work represents the first attempt to explain GNNs via identifying subgraphs explicitly and directly. Experimental results show that our SubgraphX achieves significantly improved explanations, while keeping computations at a reasonable level.

cs.LG↗

arXiv2021

Dynamic asymptotic dimension for actions of virtually cyclic groups

We show that the dynamic asymptotic dimension of a minimal free action of an infinite virtually cyclic group on a compact Hausdorff space is always one. This extends a well-known result of Guentner, Willett, and Yu for minimal free actions of infinite cyclic groups. Furthermore, the minimality assumption can be replaced by the marker property, and we prove the marker property for all free actions of countable groups on finite dimensional compact Hausdorff spaces, generalising a result of Szabo in the metrisable setting.

math.DS↗

arXiv2021

Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis

Deep neural networks (DNNs) have been applied in a wide range of applications,e.g.,face recognition and image classification; however,they are vulnerable to adversarial examples. By adding a small amount of imperceptible perturbations,an attacker can easily manipulate the outputs of a DNN. Particularly,the localized adversarial examples only perturb a small and contiguous region of the target object,so that they are robust and effective in both digital and physical worlds. Although the localized adversarial examples have more severe real-world impacts than traditional pixel attacks,they have not been well addressed in the literature. In this paper,we propose a generic defense system called TaintRadar to accurately detect localized adversarial examples via analyzing critical regions that have been manipulated by attackers. The main idea is that when removing critical regions from input images,the ranking changes of adversarial labels will be larger than those of benign labels. Compared with existing defense solutions,TaintRadar can effectively capture sophisticated localized partial attacks, e.g.,the eye-glasses attack,while not requiring additional training or fine-tuning of the original model's structure. Comprehensive experiments have been conducted in both digital and physical worlds to verify the effectiveness and robustness of our defense.

cs.CV↗