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Jun Zhao

Publications and source records attributed to Jun Zhao.

At least 361 records · Page 20Linked to original sources

A Whittaker category for the Symplectic Lie algebra

For any $n\in \mathbb{Z}_{\geq 2}$, let $\mathfrak{m}_n$ be the subalgebra of $\mathfrak{sp}_{2n}$ spanned by all long negative root vectors $X_{-2ε_i}$, $i=1,\dots,n$. An $\mathfrak{sp}_{2n}$-module $M$ is called a Whittaker module with respect to the Whittaker pair $(\mathfrak{sp}_{2n},\mathfrak{m}_n)$ if the action of $\mathfrak{m}_n$ on $M$ is locally finite, according to a definition of Batra and Mazorchuk. This kind of modules are more general than the classical Whittaker modules defined by Kostant. In this paper, we show that each non-singular block $\mathcal{WH}_{\mathbf{a}}^μ$ with finite dimensional Whittaker vector subspaces is equivalent to a module category $\mathcal{W}^{\mathbf{a}}$ of the even Weyl algebra $\mathcal{D}_n^{ev}$ which is semi-simple. As a corollary, any simple module in the block $\mathcal{WH}_{\mathbf{i}}^{-\frac{1}{2}ω_n}$ for the fundamental weight $ω_n$ is equivalent to the Nilsson's module $N_{\mathbf{i}}$ up to an automorphism of $\mathfrak{sp}_{2n}$. We also characterize all possible algebra homomorphisms from $U(\mathfrak{sp}_{2n})$ to the Weyl algebra $\mathcal{D}_n$ under a natural condition.

math.RT↗

Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless com-munication systems, enhances the spectral and energy efficiency by intelligently adjusting the propaga-tion conditions between a base station (BS) and mobile equipments (MEs). An RIS consists of manylow-cost passive reflecting elements to improve the quality of the received signal. In this paper, westudy the problem of power control at the BS for the RIS aided physical-layer broadcasting. Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposedoptimization methods, we derive lower bounds to quantify the average transmit power at the BS as afunction of the number of MEs, the number of RIS elements, and the number of antennas at the BS.The simulation results demonstrated that the average transmit power at the BS is close to the lowerbound in an RIS aided system, and is significantly lower than the average transmit power in conventionalschemes without the RIS.

eess.SP↗

First implementation of full-workflow automation in radiotherapy: the All-in-One solution on rectal cancer

The aim of this work is to describe the technical characteristics of an AI-powered radiotherapy workflow that enables full-process automation (All-in-One), evaluate its performance implemented for on-couch initial treatment of rectal cancer, and provide insight into the behavior of full-workflow automation in the specialty of radiotherapy. The All-in-One workflow was developed based on a CT-integrated linear accelerator. It incorporates routine radiotherapy procedures from simulation, autosegmentation, autoplanning, image guidance, beam delivery, and in vivo quality assurance (QA) into one scheme, with critical decision points involved, while the patient is on the treatment couch during the whole process. For the enrolled ten patients with rectal cancer, minor modifications of the autosegmented target volumes were required, and the Dice similarity coefficient and 95% Hausdorff distance before and after modifications were 0.892{\pm}0.061 and 18.2{\pm}13.0 mm, respectively. The autosegmented normal tissues and automatic plans were clinically acceptable without any modifications or reoptimization. The pretreatment IGRT corrections were within 2 mm in all directions, and the EPID-based in vivo QA showed a γ passing rate better than 97{\%} (3{\%}/3 mm/10{\%} threshold). The duration of the whole process was 23.2{\pm}3.5 minutes, depending mostly on the time required for manual modification and plan evaluation. The All-in-One workflow enables full automation of the entire radiotherapy process by seamlessly integrating multiple routine procedures. The one-stop solution shortens the time scale it takes to ready the first treatment from days to minutes, significantly improving the patient experience and the efficiency of the workflow, and shows potential to facilitate the clinical application of online adaptive replanning.

physics.med-ph↗

Privacy and Robustness in Federated Learning: Attacks and Defenses

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality. Existing FL protocol design has been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this paper, we conduct the first comprehensive survey on this topic. Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions towards robust and privacy-preserving federated learning.

cs.CR↗

Neutron Scattering Studies of the Breathing Pyrochlore Antiferromagnet LiGaCr$_{4}$O$_{8}$

We report neutron scattering measurements of the spinel oxide LiGaCr$_{4}$O$_{8}$, in which magnetic ions Cr$^{3+}$ form a breathing pyrochlore lattice. Our experiments reveal the coexistence of a nearly dispersionless resonance mode and dispersive spin wave excitations in the magnetically ordered state, which can be quantitatively described by a quantum spin model of hexagonal loops and linear spin wave theory with the same set of exchange parameters, respectively. Comparison to other Cr spinel oxides reveals a linear relationship between the resonance energy and lattice constant across all these materials, which is in agreement with our hexagonal loop calculations. Our results suggest a unified picture for spin resonances in Cr spinel oxides.

cond-mat.str-el↗

Before and after GDPR: tracking in mobile apps

Third-party tracking, the collection and sharing of behavioural data about individuals, is a significant and ubiquitous privacy threat in mobile apps. The EU General Data Protection Regulation (GDPR) was introduced in 2018 to protect personal data better, but there exists, thus far, limited empirical evidence about its efficacy. This paper studies tracking in nearly two million Android apps from before and after the introduction of the GDPR. Our analysis suggests that there has been limited change in the presence of third-party tracking in apps, and that the concentration of tracking capabilities among a few large gatekeeper companies persists. However, change might be imminent.

cs.CY↗

Pressure-induced charge orders and their coupling to magnetism in hexagonal multiferroic LuFe2O4

Hexagonal LuFe2O4 is a promising charge-order (CO) driven multiferroic material with high charge and spin ordering temperatures. The coexisting charge and spin orders on Fe3+/Fe2+ sites result in novel magnetoelectric behaviors, but the coupling mechanism between the charge and spin orders remains elusive. Here, by tuning external pressure, we reveal three correlated spin-charge ordered phases in LuFe2O4: i) a centrosymmetric incommensurate three-dimensional CO with ferrimagnetism, ii) a non-centrosymmetric incommensurate quasi-two-dimensional CO with ferrimagnetism, and iii) a centrosymmetric commensurate CO with antiferromagnetism. Experimental in-situ single-crystal X-ray diffraction and X-ray magnetic circular dichroism measurements combined with density functional theory calculations suggest that the charge density redistribution caused by pressure-induced compression in the frustrated double-layer [Fe2O4] cluster is responsible for the correlated spin-charge phase transitions. The pressure-enhanced effective Coulomb interactions among Fe-Fe bonds drive the frustrated (1/3, 1/3) CO to a less frustrated (1/4, 1/4) CO, which induces the ferrimagnetic to antiferromagnetic transition. Our results not only elucidate the coupling mechanism among charge, spin and lattice degrees of freedom in LuFe2O4 but also provide a new way to tune the spin-charge orders in a highly controlled manner.

cond-mat.mtrl-sci↗

"Money makes the world go around'': Identifying Barriers to Better Privacy in Children's Apps From Developers' Perspectives

The industry for children's apps is thriving at the cost of children's privacy: these apps routinely disclose children's data to multiple data trackers and ad networks. As children spend increasing time online, such exposure accumulates to long-term privacy risks. In this paper, we used a mixed-methods approach to investigate why this is happening and how developers might change their practices. We base our analysis against 5 leading data protection frameworks that set out requirements and recommendations for data collection in children's apps. To understand developers' perspectives and constraints, we conducted 134 surveys and 20 semi-structured interviews with popular Android children's app developers. Our analysis revealed that developers largely respect children's best interests; however, they have to make compromises due to limited monetisation options, perceived harmlessness of certain third-party libraries, and lack of availability of design guidelines. We identified concrete approaches and directions for future research to help overcome these barriers.

cs.HC↗

Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices

Deep learning based image recognition systems have been widely deployed on mobile devices in today's world. In recent studies, however, deep learning models are shown vulnerable to adversarial examples. One variant of adversarial examples, called adversarial patch, draws researchers' attention due to its strong attack abilities. Though adversarial patches achieve high attack success rates, they are easily being detected because of the visual inconsistency between the patches and the original images. Besides, it usually requires a large amount of data for adversarial patch generation in the literature, which is computationally expensive and time-consuming. To tackle these challenges, we propose an approach to generate inconspicuous adversarial patches with one single image. In our approach, we first decide the patch locations basing on the perceptual sensitivity of victim models, then produce adversarial patches in a coarse-to-fine way by utilizing multiple-scale generators and discriminators. The patches are encouraged to be consistent with the background images with adversarial training while preserving strong attack abilities. Our approach shows the strong attack abilities in white-box settings and the excellent transferability in black-box settings through extensive experiments on various models with different architectures and training methods. Compared to other adversarial patches, our adversarial patches hold the most negligible risks to be detected and can evade human observations, which is supported by the illustrations of saliency maps and results of user evaluations. Lastly, we show that our adversarial patches can be applied in the physical world.

cs.CV↗

Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method

Intelligent Internet of Things (IoT) systems based on deep neural networks (DNNs) have been widely deployed in the real world. However, DNNs are found to be vulnerable to adversarial examples, which raises people's concerns about intelligent IoT systems' reliability and security. Testing and evaluating the robustness of IoT systems becomes necessary and essential. Recently various attacks and strategies have been proposed, but the efficiency problem remains unsolved properly. Existing methods are either computationally extensive or time-consuming, which is not applicable in practice. In this paper, we propose a novel framework called Attack-Inspired GAN (AI-GAN) to generate adversarial examples conditionally. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. We apply AI-GAN on different datasets in white-box settings, black-box settings and targeted models protected by state-of-the-art defenses. Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly. Moreover, for the first time, AI-GAN successfully scales to complex datasets e.g. CIFAR-100 and ImageNet, with about $90\%$ success rates among all classes.

cs.LG↗

Intelligent Reflecting Surface-Assisted Bistatic Backscatter Networks: Joint Beamforming and Reflection Design

Bistatic backscatter communication (BackCom) allows passive tags to transmit over extended ranges, but at the cost of having carrier emitters either transmitting at high powers or being deployed very close to tags. In this paper, we examine how the presence of an intelligent reflecting surface (IRS) could benefit the bistatic BackCom system. We study the transmit power minimization problem at the carrier emitter, where its transmit beamforming vector is jointly optimized with the IRS phase shifts, whilst guaranteeing a required BackCom performance. A unique feature in this system setup is the multiple IRS reflections experienced by signals traveling from the carrier emitter to the reader, which renders the optimization problem highly nonconvex. Therefore, we propose algorithms based on the minorization-maximization and alternating optimization techniques to obtain approximate solutions for the joint design. We also propose low-complexity algorithms based on successive optimization of individual phase shifts. Our results reveal considerable transmit power savings in both single-tag and multi-tag systems, even with moderate IRS sizes, which may be translated to significant range improvements using the original transmit power or a reduction of the reliance of tags on carrier emitters located at close range.

cs.IT↗

Adversarial Purification through Representation Disentanglement

Deep learning models are vulnerable to adversarial examples and make incomprehensible mistakes, which puts a threat on their real-world deployment. Combined with the idea of adversarial training, preprocessing-based defenses are popular and convenient to use because of their task independence and good generalizability. Current defense methods, especially purification, tend to remove ``noise" by learning and recovering the natural images. However, different from random noise, the adversarial patterns are much easier to be overfitted during model training due to their strong correlation to the images. In this work, we propose a novel adversarial purification scheme by presenting disentanglement of natural images and adversarial perturbations as a preprocessing defense. With extensive experiments, our defense is shown to be generalizable and make significant protection against unseen strong adversarial attacks. It reduces the success rates of state-of-the-art \textbf{ensemble} attacks from \textbf{61.7\%} to \textbf{14.9\%} on average, superior to a number of existing methods. Notably, our defense restores the perturbed images perfectly and does not hurt the clean accuracy of backbone models, which is highly desirable in practice.

cs.CV↗

Blockchain for Edge of Things: Applications, Opportunities, and Challenges

In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, called BEoT that has been regarded as a promising enabler for future services and applications. In this paper, we present a state-of-the-art review of recent developments in BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home and smart grid. Security challenges in BEoT paradigm are also discussed and analyzed, with some key services such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.

cs.CR↗

A Relation-Oriented Clustering Method for Open Relation Extraction

The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE). However, high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters cannot explicitly align with the relational semantic classes. In this work, we propose a relation-oriented clustering model and use it to identify the novel relations in the unlabeled data. Specifically, to enable the model to learn to cluster relational data, our method leverages the readily available labeled data of pre-defined relations to learn a relation-oriented representation. We minimize distance between the instance with same relation by gathering the instances towards their corresponding relation centroids to form a cluster structure, so that the learned representation is cluster-friendly. To reduce the clustering bias on predefined classes, we optimize the model by minimizing a joint objective on both labeled and unlabeled data. Experimental results show that our method reduces the error rate by 29.2% and 15.7%, on two datasets respectively, compared with current SOTA methods.

cs.CL↗

Field-Tuned Quantum Effects in a Triangular-Lattice Ising Magnet

We report thermodynamic and neutron scattering measurements of the triangular-lattice quantum Ising magnet TmMgGaO 4 in longitudinal magnetic fields. Our experiments reveal a quasi-plateau state induced by quantum fluctuations. This state exhibits an unconventional non-monotonic field and temperature dependence of the magnetic order and excitation gap. In the high field regime where the quantum fluctuations are largely suppressed, we observed a disordered state with coherent magnon-like excitations despite the suppression of the spin excitation intensity. Through detailed semi-classical calculations, we are able to understand these behaviors quantitatively from the subtle competition between quantum fluctuations and frustrated Ising interactions.

cond-mat.str-el↗

Protection or punishment? relating the design space of parental control apps and perceptions about them to support parenting for online safety

Parental control apps, which are mobile apps that allow parents to monitor and restrict their children's activities online, are becoming increasingly adopted by parents as a means of safeguarding their children's online safety. However, it is not clear whether these apps are always beneficial or effective in what they aim to do; for instance, the overuse of restriction and surveillance has been found to undermine parent-child relationship and children's sense of autonomy. In this work, we investigate this gap, asking specifically: how might children's and parents' perceptions be related to how parental control features were designed? To investigate this question, we conducted an analysis of 58 top Android parental control apps designed for the purpose of promoting children's online safety, finding three major axes of variation in how key restriction and monitoring features were realised: granularity, feedback/transparency, and parent-child communications support. To relate these axes to perceived benefits and problems, we then analysed 3264 app reviews to identify references to aspects of the each of the axes above, to understand children's and parents' views of how such dimensions related to their experiences with these apps. Our findings led towards 1) an understanding of how parental control apps realise their functionalities differently along three axes of variation, 2) an analysis of exactly the ways that such variation influences children's and parents' perceptions, respectively of the usefulness or effectiveness of these apps, and finally 3) an identification of design recommendations and opportunities for future apps by contextualising our findings within existing digital parenting theories.

cs.HC↗

Polarized neutron scattering studies of magnetic excitations in iron-selenide superconductor (Li$_{0.8}$Fe$_{0.2}$)ODFeSe ($T_c$ = 41 K)

We report polarized neutron scattering measurements of the low energy spin fluctuations of the iron-selenide superconductor Li$_{0.8}$Fe$_{0.2}$ODFeSe below and above its superconducting transition temperature $T_c=41$ K. Our experiments confirmed that the resonance mode near 21 meV is magnetic. Moreover, the spin excitations are essentially isotropic in spin space at 5$\leq E\leq$ 29 meV in the superconducting and normal states. Our results suggest that the resonance mode in iron-based superconductors becomes isotropic when the influence of spin-orbit coupling and magnetic/nematic order is minimized, similar to those observed in cuprate superconductors.

cond-mat.supr-con↗

Lifelong Intent Detection via Multi-Strategy Rebalancing

Conventional Intent Detection (ID) models are usually trained offline, which relies on a fixed dataset and a predefined set of intent classes. However, in real-world applications, online systems usually involve continually emerging new user intents, which pose a great challenge to the offline training paradigm. Recently, lifelong learning has received increasing attention and is considered to be the most promising solution to this challenge. In this paper, we propose Lifelong Intent Detection (LID), which continually trains an ID model on new data to learn newly emerging intents while avoiding catastrophically forgetting old data. Nevertheless, we find that existing lifelong learning methods usually suffer from a serious imbalance between old and new data in the LID task. Therefore, we propose a novel lifelong learning method, Multi-Strategy Rebalancing (MSR), which consists of cosine normalization, hierarchical knowledge distillation, and inter-class margin loss to alleviate the multiple negative effects of the imbalance problem. Experimental results demonstrate the effectiveness of our method, which significantly outperforms previous state-of-the-art lifelong learning methods on the ATIS, SNIPS, HWU64, and CLINC150 benchmarks.

cs.CL↗