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Jun Zhao

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preprint2026arXiv

DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios

Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from code-sandbox confinement, single-language creation-only tasks, and assumption of perfect intent. To bridge these gaps, we introduce DV-World, a benchmark of 260 tasks designed to evaluate DV agents across real-world professional lifecycles. DV-World spans three domains: DV-Sheet for native spreadsheet manipulation including chart and dashboard creation as well as diagnostic repair; DV-Evolution for adapting and restructuring reference visual artifacts to fit new data across diverse programming paradigms and DV-Interact for proactive intent alignment with a user simulator that mimics real-world ambiguous requirements. Our hybrid evaluation framework integrates Table-value Alignment for numerical precision and MLLM-as-a-Judge with rubrics for semantic-visual assessment. Experiments reveal that state-of-the-art models achieve less than 50% overall performance, exposing critical deficits in handling the complex challenges of real-world data visualization. DV-World provides a realistic testbed to steer development toward the versatile expertise required in enterprise workflows. Our data and code are available at \href{https://github.com/DA-Open/DV-World}{this project page}.

preprint2023arXiv

Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0-1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: 1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. 2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations on two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7\% and 4.3\% in mean reciprocal rank (MRR).

preprint2023arXiv

UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach

Metaverse is expected to create a virtual world closely connected with reality to provide users with immersive experience with the support of 5G high data rate communication technique. A huge amount of data in physical world needs to be synchronized to the virtual world to provide immersive experience for users, and there will be higher requirements on coverage to include more users into Metaverse. However, 5G signal suffers severe attenuation, which makes it more expensive to maintain the same coverage. Unmanned aerial vehicle (UAV) is a promising candidate technique for future implementation of Metaverse as a low-cost and high-mobility platform for communication devices. In this paper, we propose a proximal policy optimization (PPO) based double-agent cooperative reinforcement learning method for channel allocation and trajectory control of UAV to collect and synchronize data from the physical world to the virtual world, and expand the coverage of Metaverse services economically. Simulation results show that our proposed method is able to achieve better performance compared to the benchmark approaches.

preprint2022arXiv

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.

preprint2022arXiv

An LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks

In this paper, an LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G machine-type communication (MTC) networks, where massive MTC (mMTC) devices and ultra-reliable low latency communications (URLLC) devices coexist. In the proposed LSTMH-RA scheme, mMTC devices access the network via a timing advance (TA)-aided four-step procedure to meet massive access requirement, while the access procedure of the URLLC devices is completed in two steps coupled with the mMTC devices' access procedure to reduce latency. Furthermore, we propose an attention-based LSTM prediction model to predict the number of active URLLC devices, thereby determining the parameters of the multi-user detection algorithm to guarantee the latency and reliability access requirements of URLLC devices. We analyze the successful access probability of the LSTMH-RA scheme. Numerical results show that, compared with the benchmark schemes, the proposed LSTMH-RA scheme can significantly improve the successful access probability, and thus satisfy the diverse QoS requirements of URLLC and mMTC devices.

preprint2022arXiv

Anomalous contribution to the nematic electronic states from the structural transition in FeSe revealed by time- and angle-resolved photoemission spectroscopy

High-resolution time- and angle-resolved photoemission measurements were made on FeSe superconductors. With ultrafast photoexcitation, two critical excitation fluences that correspond to two ultrafast electronic phase transitions were found only in the $d_{yz}$-orbit-derived band near the Brillouin-zone center within our time and energy resolution. Upon comparison to the detailed temperature dependent measurements, we conclude that there are two equilibrium electronic phase transitions (at approximately 90 and 120 K) above the superconducting transition temperature, and an anomalous contribution on the scale of 10 meV to the nematic states from the structural transition is experimentally determined. Our observations strongly suggest that the electronic phase transition at 120 K must be taken into account in the energy band development of FeSe, and, furthermore, the contribution of the structural transition plays an important role in the nematic phase of iron-based high-temperature superconductors.

preprint2022arXiv

Connected Vehicles: A Privacy Analysis

Just as the world of consumer devices was forever changed by the introduction of computer controlled solutions, the introduction of the engine control unit (ECU) gave rise to the automobile's transformation from a transportation product to a technology platform. A modern car is capable of processing, analysing and transmitting data in ways that could not have been foreseen only a few years ago. These cars often incorporate telematics systems, which are used to provide navigation and internet connectivity over cellular networks, as well as data-recording devices for insurance and product development purposes. We examine the telematics system of a production vehicle, and aim to ascertain some of the associated privacy-related threats. We also consider how this analysis might underpin further research.

preprint2022arXiv

Editorial on Research Topic: High-Tc Superconductivity in Electron-Doped Iron Selenide and Related Compounds

In this editorial, we first give a brief survey of the field of iron-selenide superconductivity, both in the case of bulk FeSe characterized by the co-existence of superconductivity and nematic order, and in the case of electron-doped FeSe characterized by high-temperature superconductivity. We next review the 8 contributions to the Frontiers in Physics research topic "High-Tc Superconductivity in Electron-Doped Iron Selenide and Related Compounds".

preprint2022arXiv

Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning

With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could hinder the success of digital transformation. As such, Privacy-Preserving Machine Learning (PPML) has received much attention from both academia and industry. However, organizations are faced with the dilemma that, on the one hand, they are encouraged to share data to enhance ML performance, but on the other hand, they could potentially be breaching the relevant data privacy regulations. Practical PPML typically allows multiple participants to individually train their ML models, which are then aggregated to construct a global model in a privacy-preserving manner, e.g., based on multi-party computation or homomorphic encryption. Nevertheless, in most important applications of large-scale PPML, e.g., by aggregating clients' gradients to update a global model for federated learning, such as consumer behavior modeling of mobile application services, some participants are inevitably resource-constrained mobile devices, which may drop out of the PPML system due to their mobility nature. Therefore, the resilience of privacy-preserving aggregation has become an important problem to be tackled. In this paper, we propose a scalable privacy-preserving aggregation scheme that can tolerate dropout by participants at any time, and is secure against both semi-honest and active malicious adversaries by setting proper system parameters. By replacing communication-intensive building blocks with a seed homomorphic pseudo-random generator, and relying on the additive homomorphic property of Shamir secret sharing scheme, our scheme outperforms state-of-the-art schemes by up to 6.37$\times$ in runtime and provides a stronger dropout-resilience. The simplicity of our scheme makes it attractive both for implementation and for further improvements.

preprint2022arXiv

Ferroelectricity driven by magnetism in quasi-one-dimensional Ba9Fe3Se15

The spin-induced ferroelectricity in quasi-1D spin chain system is little known, which could be fundamentally different from those in three-dimensional (3D) system. Here, we report the ferroelectricity driven by a tilted screw spin order and its exotic dynamic in the spin-chain compound Ba9Fe3Se15. It is found that the spin-induced polarization has already occurred and exhibits magnetoelectric coupling behavior far above the long-range spin order (LRSO) at TN = 14 K. The polarized entities grow and their dynamic responses slow down gradually with decreasing temperature and permeate the whole lattice to form 3D ferroelectricity at TN. Our results reveal that the short-range spin orders (SRSOs) in the decoupled chains play a key role for the exotic dynamic in this dimension reduced system. Ba9Fe3Se15 is the only example so far which exhibits electric polarization above LRSO temperature because of the formation of SRSOs.

preprint2022arXiv

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.

preprint2022arXiv

Frustrated magnetic interactions in FeSe

The structurally simplest high-temperature superconductor FeSe exhibits an intriguing superconducting nematic paramagnetic phase with unusual spin excitation spectra that are different from typical spin waves; thus, determining its effective magnetic exchange interactions is challenging. Here we report neutron scattering measurements of spin fluctuations of FeSe in the tetragonal paramagnetic phase. We show that the equal-time magnetic structure factor, $\mathcal{S}(\textbf{Q})$, can be effectively modeled using the self-consistent Gaussian approximation calculation with highly frustrated nearest-neighbor ($J_{1}$) and next-nearest-neighbor ($J_{2}$) exchange couplings, and very weak further neighbor exchange interaction. Our results elucidate the frustrated magnetism in FeSe, which provides a natural explanation for the highly tunable superconductivity and nematicity in FeSe and related materials.

preprint2022arXiv

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.

preprint2022arXiv

Privacy-Preserving Aggregation in Federated Learning: A Survey

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. Practical PPFL typically allows multiple participants to individually train their machine learning models, which are then aggregated to construct a global model in a privacy-preserving manner. As such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has received substantial research interest. This survey aims to fill the gap between a large number of studies on PPFL, where PPAgg is adopted to provide a privacy guarantee, and the lack of a comprehensive survey on the PPAgg protocols applied in FL systems. In this survey, we review the PPAgg protocols proposed to address privacy and security issues in FL systems. The focus is placed on the construction of PPAgg protocols with an extensive analysis of the advantages and disadvantages of these selected PPAgg protocols and solutions. Additionally, we discuss the open-source FL frameworks that support PPAgg. Finally, we highlight important challenges and future research directions for applying PPAgg to FL systems and the combination of PPAgg with other technologies for further security improvement.

preprint2022arXiv

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.

preprint2022arXiv

Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography

Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved. Therefore, we propose a novel robust PCA unrolling network with sparse feature selection for super-resolution XCA vessel imaging. Being embedded within a patch-wise spatiotemporal super-resolution framework that is built upon a pooling layer and a convolutional long short-term memory network, the proposed network can not only gradually prune complex vessel-like artefacts and noisy backgrounds in XCA during network training but also iteratively learn and select the high-level spatiotemporal semantic information of moving contrast agents flowing in the XCA-imaged vessels. The experimental results show that the proposed method significantly outperforms state-of-the-art methods, especially in the imaging of the vessel network and its distal vessels, by restoring the intensity and geometry profiles of heterogeneous vessels against complex and dynamic backgrounds.

preprint2022arXiv

Smart City Enabled by 5G/6G Networks: An Intelligent Hybrid Random Access Scheme

The Internet of Things (IoT) is the enabler for smart city to achieve the envision of the "Internet of Everything" by intelligently connecting devices without human interventions. The explosive growth of IoT devices makes the amount of business data generated by machine-type communications (MTC) account for a great proportion in all communication services. The fifth-generation (5G) specification for cellular networks defines two types of application scenarios for MTC: One is massive machine type communications (mMTC) requiring massive connections, while the other is ultra-reliable low latency communications (URLLC) requiring high reliability and low latency communications. 6G, as the next generation beyond 5G, will have even stronger scales of mMTC and URLLC. mMTC and URLLC will co-exist in MTC networks for 5G 6G-enabled smart city. To enable massive and reliable LLC access to such heterogeneous MTC networks where mMTC and URLLC co-exist, in this article, we introduce the network architecture of heterogeneous MTC networks, and propose an intelligent hybrid random access scheme for 5G/6G-enabled smart city. Numerical results show that, compared to the benchmark schemes, the proposed scheme significantly improves the successful access probability, and satisfies the diverse quality of services requirements of URLLC and mMTC devices.

preprint2022arXiv

Unusual band splitting and superconducting gap evolution with sulfur substitution in FeSe

High-resolution angle-resolved photoemission measurements were taken on FeSe$_{1-x}$S$_x$ (x=0, 0.04, and 0.08) superconductors. With an ultrahigh energy resolution of 0.4 meV, unusual two hole bands near the Brillouin-zone center, which was possibly a result of additional symmetry breaking, were identified in all the sulfur-substituted samples. In addition, in both of the hole bands highly anisotropic superconducting gaps with resolution limited nodes were evidenced. We find that the larger superconducting gap on the outer hole band is reduced linearly to the nematic transition temperature while the gap on the inner hole is nearly S-substitution independent. Our observations strongly suggest that the superconducting gap increases with enhanced nematicity although the superconducting transition temperature is not only governed by the pairing strength, demonstrating strong constraints on theories in the FeSe family.

preprint2021arXiv

6G Downlink Transmission via Rate Splitting Space Division Multiple Access Based on Grouped Code Index Modulation

A novel rate splitting space division multiple access (SDMA) scheme based on grouped code index modulation (GrCIM) is proposed for the sixth generation (6G) downlink transmission. The proposed RSMA-GrCIM scheme transmits information to multiple user equipments (UEs) through the space division multiple access (SDMA) technique, and exploits code index modulation for rate splitting. Since the CIM scheme conveys information bits via the index of the selected Walsh code and binary phase shift keying (BPSK) signal, our RSMA scheme transmits the private messages of each user through the indices, and the common messages via the BPSK signal. Moreover, the Walsh code set is grouped into several orthogonal subsets to eliminate the interference from other users. A maximum likelihood (ML) detector is used to recovery the source bits, and a mathematical analysis is provided for the upper bound bit error ratio (BER) of each user. Comparisons are also made between our proposed scheme and the traditional SDMA scheme in spectrum utilization, number of available UEs, etc. Numerical results are given to verify the effectiveness of the proposed SDMA-GrCIM scheme.

preprint2021arXiv

A Comprehensive Survey of 6G Wireless Communications

While fifth-generation (5G) communications are being rolled out worldwide, sixth-generation (6G) communications have attracted much attention from both the industry and the academia. Compared with 5G, 6G will have a wider frequency band, higher transmission rate, spectrum efficiency, greater connection capacity, shorter delay, broader coverage, and more robust anti-interference capability to satisfy various network requirements. This survey presents an insightful understanding of 6G wireless communications by introducing requirements, features, critical technologies, challenges, and applications. First, we give an overview of 6G from perspectives of technologies, security and privacy, and applications. Subsequently, we introduce various 6G technologies and their existing challenges in detail, e.g., artificial intelligence (AI), intelligent surfaces, THz, space-air-ground-sea integrated network, cell-free massive MIMO, etc. Because of these technologies, 6G is expected to outperform existing wireless communication systems regarding the transmission rate, latency, global coverage, etc. Next, we discuss security and privacy techniques that can be applied to protect data in 6G. Since edge devices are expected to gain popularity soon, the vast amount of generated data and frequent data exchange make the leakage of data easily. Finally, we predict real-world applications built on the technologies and features of 6G; for example, smart healthcare, smart city, and smart manufacturing will be implemented by taking advantage of AI.

preprint2021arXiv

AI-GAN: Attack-Inspired Generation of Adversarial Examples

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs. Recently different attacks and strategies have been proposed, but how to generate adversarial examples perceptually realistic and more efficiently remains unsolved. This paper proposes a novel framework called Attack-Inspired GAN (AI-GAN), where a generator, a discriminator, and an attacker are trained jointly. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. Through extensive experiments on several popular datasets \eg MNIST and CIFAR-10, AI-GAN achieves high attack success rates and reduces generation time significantly in various settings. Moreover, for the first time, AI-GAN successfully scales to complicated datasets \eg CIFAR-100 with around $90\%$ success rates among all classes.

preprint2021arXiv

CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge

In this paper, we present CogNet, a knowledge base (KB) dedicated to integrating three types of knowledge: (1) linguistic knowledge from FrameNet, which schematically describes situations, objects and events. (2) world knowledge from YAGO, Freebase, DBpedia and Wikidata, which provides explicit knowledge about specific instances. (3) commonsense knowledge from ConceptNet, which describes implicit general facts. To model these different types of knowledge consistently, we introduce a three-level unified frame-styled representation architecture. To integrate free-form commonsense knowledge with other structured knowledge, we propose a strategy that combines automated labeling and crowdsourced annotation. At present, CogNet integrates 1,000+ semantic frames from linguistic KBs, 20,000,000+ frame instances from world KBs, as well as 90,000+ commonsense assertions from commonsense KBs. All these data can be easily queried and explored on our online platform, and free to download in RDF format for utilization under a CC-BY-SA 4.0 license. The demo and data are available at http://cognet.top/.

preprint2021arXiv

Observation of robust edge superconductivity in Fe(Se,Te) under strong magnetic perturbation

The iron-chalcogenide high temperature superconductor Fe(Se,Te) (FST) has been reported to exhibit complex magnetic ordering and nontrivial band topology which may lead to novel superconducting phenomena. However, the recent studies have so far been largely concentrated on its band and spin structures while its mesoscopic electronic and magnetic response, crucial for future device applications, has not been explored experimentally. Here, we used scanning superconducting quantum interference device microscopy for its sensitivity to both local diamagnetic susceptibility and current distribution in order to image the superfluid density and supercurrent in FST. We found that in FST with 10% interstitial Fe, whose magnetic structure was heavily disrupted, bulk superconductivity was significantly suppressed whereas edge still preserved strong superconducting diamagnetism. The edge dominantly carried supercurrent despite of a very long magnetic penetration depth. The temperature dependence of the superfluid density and supercurrent distribution were distinctively different between the edge and the bulk. Our Heisenberg modeling showed that magnetic dopants stabilize anti-ferromagnetic spin correlation along the edge, which may contribute towards its robust superconductivity. Our observations hold implication for FST as potential platforms for topological quantum computation and superconducting spintronics.

preprint2021arXiv

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.

preprint2021arXiv

Secrecy Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave System with Low-resolution DAC

In this letter, we investigate the secrecy rate of an reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) system with hardware limitations. Compared to the RIS-aided systems in most existing works, we consider the case of the RIS-aided mmWave system with low-resolution digital-to-analog converters (LDACs). We formulate a secrecy rate maximization problem hardware constraints. Then by optimizing the RIS phase shift and the transmit beamforming to maximize the secrecy rate. Due to the nonconvexity of the problem, the formulated problem is intractable. To handle the problem, an alternating optimization (AO)-based algorithm is proposed. Specifically, we first use the successive convex approximation (SCA) method to obtain the transmit beamforming. Then the element-wise block coordinate descent (BCD) method is used to obtain the RIS phase shift. Numerical results demonstrate that the RIS can mitigate the hardware loss, and the proposed AO-based algorithm with low complexity outperformances the baselines.

preprint2021arXiv

Spectrum Sharing for 6G Integrated Satellite-Terrestrial Communication Networks Based on NOMA and Cognitive Radio

The explosive growth of bandwidth hungry Internet applications has led to the rapid development of new generation mobile network technologies that are expected to provide broadband access to the Internet in a pervasive manner. For example, 6G networks are capable of providing high-speed network access by exploiting higher frequency spectrum; high-throughout satellite communication services are also adopted to achieve pervasive coverage in remote and isolated areas. In order to enable seamless access, Integrated Satellite-Terrestrial Communication Networks (ISTCN) has emerged as an important research area. ISTCN aims to provide high speed and pervasive network services by integrating broadband terrestrial mobile networks with satellite communication networks. As terrestrial mobile networks began to use higher frequency spectrum (between 3GHz to 40GHz) which overlaps with that of satellite communication (4GHz to 8GHz for C band and 26GHz to 40GHz for Ka band), there are opportunities and challenges. On one hand, satellite terminals can potentially access terrestrial networks in an integrated manner; on the other hand, there will be more congestion and interference in this spectrum, hence more efficient spectrum management techniques are required. In this paper, we propose a new technique to improve spectrum sharing performance by introducing Non-orthogonal Frequency Division Multiplexing (NOMA) and Cognitive Radio (CR) in the spectrum sharing of ISTCN. In essence, NOMA technology improves spectrum efficiency by allowing different users to transmit on the same carrier and distinguishing users by user power levels while CR technology improves spectrum efficiency through dynamic spectrum sharing. Furthermore, some open researches and challenges in ISTCN will be discussed.

preprint2021arXiv

Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning

As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLC) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this work considers a UAV-assisted VLC using non-orthogonal multiple-access. More specifically, we formulate a joint problem of power allocation and UAV's placement to maximize the sum rate of all users, subject to constraints on power allocation, quality of service of users, and UAV's position. Since the problem is non-convex and NP-hard in general, it is difficult to be solved optimally. Moreover, the problem is not easy to be solved by conventional approaches, e.g., coordinate descent algorithms, due to channel modeling in VLC. Therefore, we propose using harris hawks optimization (HHO) algorithm to solve the formulated problem and obtain an efficient solution. We then use the HHO algorithm together with artificial neural networks to propose a design which can be used in real-time applications and avoid falling into the "local minima" trap in conventional trainers. Numerical results are provided to verify the effectiveness of the proposed algorithm and further demonstrate that the proposed algorithm/HHO trainer is superior to several alternative schemes and existing metaheuristic algorithms.

preprint2020arXiv

'I Just Want to Hack Myself to Not Get Distracted': Evaluating Design Interventions for Self-Control on Facebook

Beyond being the world's largest social network, Facebook is for many also one of its greatest sources of digital distraction. For students, problematic use has been associated with negative effects on academic achievement and general wellbeing. To understand what strategies could help users regain control, we investigated how simple interventions to the Facebook UI affect behaviour and perceived control. We assigned 58 university students to one of three interventions: goal reminders, removed newsfeed, or white background (control). We logged use for 6 weeks, applied interventions in the middle weeks, and administered fortnightly surveys. Both goal reminders and removed newsfeed helped participants stay on task and avoid distraction. However, goal reminders were often annoying, and removing the newsfeed made some fear missing out on information. Our findings point to future interventions such as controls for adjusting types and amount of available information, and flexible blocking which matches individual definitions of 'distraction'.

preprint2020arXiv

A novel random access scheme for M2M communication in crowded asynchronous massive MIMO systems

A new random access scheme is proposed to solve the intra-cell pilot collision for M2M communication in crowded asynchronous massive multiple-input multiple-output (MIMO) systems. The proposed scheme utilizes the proposed estimation of signal parameters via rotational invariance technique enhanced (ESPRIT-E) method to estimate the effective timing offsets, and then active UEs obtain their timing errors from the effective timing offsets for uplink message transmission. We analyze the mean squared error of the estimated effective timing offsets of UEs, and the uplink throughput. Simulation results show that, compared to the exiting random access scheme for the crowded asynchronous massive MIMO systems, the proposed scheme can improve the uplink throughput and estimate the effective timing offsets accurately at the same time.

preprint2020arXiv

A Stackelberg Game Approach to Resource Allocation for Intelligent Reflecting Surface Aided Communications

It is known that the capacity of the intelligent reflecting surface (IRS) aided cellular network can be effectively improved by reflecting the incident signals from the transmitter in a low-cost passive reflecting way. In this paper, we study the adoption of an IRS for downlink multi-user communication from a multi-antenna base station (BS). Nevertheless, in the actual network operation, the IRS operator can be selfish or have its own objectives due to competing/limited resources as well as deployment/maintenance cost. Therefore, in this paper, we develop a Stackelbeg game model to analyze the interaction between the BS and the IRS operator. Specifically, different from the existing studies on IRS that merely focus on tuning the reflection coefficient of all the reflection elements, we consider the reflection resource (elements) management, which can be realized via trigger module selection under our proposed IRS architecture that all the reflection elements are partially controlled by independent switches of controller. A Stackelberg game-based alternating direction method of multipliers (ADMM) is proposed to jointly optimize the transmit beamforming at the BS and the passive beamforming of the triggered reflection modules. Numerical examples are presented to verify the proposed studies. It is shown that the proposed scheme is effective in the utilities of both the BS and IRS.

preprint2020arXiv

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception. The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion. For Lidar or visual SLAM, the survey illustrates the basic type and product of sensors, open source system in sort and history, deep learning embedded, the challenge and future. Additionally, visual inertial odometry is supplemented. For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer. The open question and forward thinking with an envision in 6G wireless networks end the paper. The contributions of this paper can be summarized as follows: the paper provides a high quality and full-scale overview in SLAM. It's very friendly for new researchers to hold the development of SLAM and learn it very obviously. Also, the paper can be considered as a dictionary for experienced researchers to search and find new interesting orientation.

preprint2020arXiv

A Two-Body Synergistic Theory for Adsorption and Desorption Kinetics on Surface

A kinetic theory is proposed to elucidate complex nature of adsorption and desorption on surface and to calculate the adsorption and desorption rates in a practically simple way. This theory provides decomposition of the energy barriers and considers synergistic effects of lateral interactions between adsorbates. A concise formulation was derived for adsorption and desorption rates on surfaces covered with multi-component adsorbates. The adsorption and desorption rates are formulated by multiplying a two-body synergistic coefficient that is explicitly dependent on surface coverage, compared respectively to those from classical theories.

preprint2020arXiv

An Analysis of Blockchain Consistency in Asynchronous Networks: Deriving a Neat Bound

Formal analyses of blockchain protocols have received much attention recently. Consistency results of Nakamoto's blockchain protocol are often expressed in a quantity $c$, which denotes the expected number of network delays before some block is mined. With $μ$ (resp., $ν$) denoting the fraction of computational power controlled by benign miners (resp., the adversary), where $μ+ ν= 1$, we prove for the first time that to ensure the consistency property of Nakamoto's blockchain protocol in an asynchronous network, it suffices to have $c$ to be just slightly greater than $\frac{2μ}{\ln (μ/ν)}$. Such a result is both neater and stronger than existing ones. In the proof, we formulate novel Markov chains which characterize the numbers of mined blocks in different rounds.

preprint2020arXiv

Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients

Local differential privacy (LDP) is an emerging privacy standard to protect individual user data. One scenario where LDP can be applied is federated learning, where each user sends in his/her user gradients to an aggregator who uses these gradients to perform stochastic gradient descent. In a case where the aggregator is untrusted and LDP is not applied to each user gradient, the aggregator can recover sensitive user data from these gradients. In this paper, we present a new interactive web demo showcasing the power of local differential privacy by visualizing federated learning with local differential privacy. Moreover, the live demo shows how LDP can prevent untrusted aggregators from recovering sensitive training data. A measure called the exp-hamming recovery is also created to show the extent of how much data the aggregator can recover.

preprint2020arXiv

BiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy

Local differential privacy (LDP) has received much interest recently. In existing protocols with LDP guarantees, a user encodes and perturbs his data locally before sharing it to the aggregator. In common practice, however, users would prefer not to answer all the questions due to different privacy-preserving preferences for different questions, which leads to data missing or the loss of data quality. In this paper, we demonstrate a new approach for addressing the challenges of data perturbation with consideration of users' privacy preferences. Specifically, we first propose BiSample: a bidirectional sampling technique value perturbation in the framework of LDP. Then we combine the BiSample mechanism with users' privacy preferences for missing data perturbation. Theoretical analysis and experiments on a set of datasets confirm the effectiveness of the proposed mechanisms.

preprint2020arXiv

Blockchain for Future Smart Grid: A Comprehensive Survey

The concept of smart grid has been introduced as a new vision of the conventional power grid to figure out an efficient way of integrating green and renewable energy technologies. In this way, Internet-connected smart grid, also called energy Internet, is also emerging as an innovative approach to ensure the energy from anywhere at any time. The ultimate goal of these developments is to build a sustainable society. However, integrating and coordinating a large number of growing connections can be a challenging issue for the traditional centralized grid system. Consequently, the smart grid is undergoing a transformation to the decentralized topology from its centralized form. On the other hand, blockchain has some excellent features which make it a promising application for smart grid paradigm. In this paper, we aim to provide a comprehensive survey on application of blockchain in smart grid. As such, we identify the significant security challenges of smart grid scenarios that can be addressed by blockchain. Then, we present a number of blockchain-based recent research works presented in different literatures addressing security issues in the area of smart grid. We also summarize several related practical projects, trials, and products that have been emerged recently. Finally, we discuss essential research challenges and future directions of applying blockchain to smart grid security issues.

preprint2020arXiv

Blockchain-Based Differential Privacy Cost Management System

Privacy preservation is a big concern for various sectors. To protect individual user data, one emerging technology is differential privacy. However, it still has limitations for datasets with frequent queries, such as the fast accumulation of privacy cost. To tackle this limitation, this paper explores the integration of a secured decentralised ledger, blockchain. Blockchain will be able to keep track of all noisy responses generated with differential privacy algorithm and allow for certain queries to reuse old responses. In this paper, a demo of a proposed blockchain-based privacy management system is designed as an interactive decentralised web application (DApp). The demo created illustrates that leveraging on blockchain will allow the total privacy cost accumulated to decrease significantly.

preprint2020arXiv

Clustering and Power Allocation for UAV-assisted NOMA-VLC Systems: A Swarm Intelligence Approach

Integrating unmanned aerial vehicles (UAV) to non-orthogonal multiple access (NOMA) visible light communications (VLC) exposes many potentials over VLC and NOMA-VLC systems. In this circumstance, user grouping is of importance to reduce the NOMA decoding complexity when the number of users is large; however, this issue has not been considered in the existing study. In this paper, we aim to maximize the weighted sum-rate of all the users by jointly optimizing UAV placement, user grouping, and power allocation in downlink NOMA-VLC systems. We first consider an efficient user clustering strategy, then apply a swarm intelligence approach, namely Harris Hawk Optimization (HHO), to solve the joint UAV placement and power allocation problem. Simulation results show outperformance of the proposed algorithm in comparison with four alternatives: OMA, NOMA without pairing, NOMA-VLC with fixed UAV placement, and random user clustering.

preprint2020arXiv

Developing a measure of online wellbeing and user trust

This paper describes the first stage of the ongoing development of two scales to measure online wellbeing and trust, based on the results of a series of workshops with younger and older adults. The first, the Online Wellbeing Scale includes subscales covering both psychological, or eudaimonic, wellbeing and subjective, or hedonic, wellbeing, as well as digital literacy and online activity; the overall aim is to understand how a user's online experiences affect their wellbeing. The second scale, the Trust Index includes three subscales covering the importance of trust to the user, trusting beliefs, and contextual factors; the aim for this scale is to examine trust in online algorithm-driven systems. The scales will be used together to aid researchers in understanding how trust (or lack of trust) relates to overall wellbeing online. They will also contribute to the development of a suite of tools for empowering users to negotiate issues of trust online, as well as in designing guidelines for the inclusion of trust considerations in the development of online algorithm-driven systems. The next step is to release the prototype scales developed as a result of this pilot in a large online study in to validate the measures.

preprint2020arXiv

Distantly Supervised Relation Extraction in Federated Settings

This paper investigates distantly supervised relation extraction in federated settings. Previous studies focus on distant supervision under the assumption of centralized training, which requires collecting texts from different platforms and storing them on one machine. However, centralized training is challenged by two issues, namely, data barriers and privacy protection, which make it almost impossible or cost-prohibitive to centralize data from multiple platforms. Therefore, it is worthy to investigate distant supervision in the federated learning paradigm, which decouples the model training from the need for direct access to the raw data. Overcoming label noise of distant supervision, however, becomes more difficult in federated settings, since the sentences containing the same entity pair may scatter around different platforms. In this paper, we propose a federated denoising framework to suppress label noise in federated settings. The core of this framework is a multiple instance learning based denoising method that is able to select reliable instances via cross-platform collaboration. Various experimental results on New York Times dataset and miRNA gene regulation relation dataset demonstrate the effectiveness of the proposed method.

preprint2020arXiv

DPCrowd: Privacy-preserving and Communication-efficient Decentralized Statistical Estimation for Real-time Crowd-sourced Data

In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time collaborative statistical estimation by disseminating statistics from their separate databases. Despite no raw data sharing, the real-time statistics could still expose the data privacy of crowd-sourcing participants. For mitigating the privacy concern, while traditional differential privacy (DP) mechanism can be simply implemented to perturb the statistics in each timestamp and independently for each dimension, this may suffer a great utility loss from the real-time and multi-dimensional crowd-sourced data. Also, the real-time broadcasting would bring significant overheads in the whole network. To tackle the issues, we propose a novel privacy-preserving and communication-efficient decentralized statistical estimation algorithm (DPCrowd), which only requires intermittently sharing the DP protected parameters with one-hop neighbors by exploiting the temporal correlations in real-time crowd-sourced data. Then, with further consideration of spatial correlations, we develop an enhanced algorithm, DPCrowd+, to deal with multi-dimensional infinite crowd-data streams. Extensive experiments on several datasets demonstrate that our proposed schemes DPCrowd and DPCrowd+ can significantly outperform existing schemes in providing accurate and consensus estimation with rigorous privacy protection and great communication efficiency.

preprint2020arXiv

Enabling Cross-chain Transactions: A Decentralized Cryptocurrency Exchange Protocol

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on the Ethereum blockchain and deployed on the Ethereum test network. We not only implement transactions between individual users, but also allow transactions between multiple users. The experimental result proves that the cost of our scheme is acceptable.

preprint2020arXiv

Event Coreference Resolution via a Multi-loss Neural Network without Using Argument Information

Event coreference resolution(ECR) is an important task in Natural Language Processing (NLP) and nearly all the existing approaches to this task rely on event argument information. However, these methods tend to suffer from error propagation from the stage of event argument extraction. Besides, not every event mention contains all arguments of an event, and argument information may confuse the model that events have arguments to detect event coreference in real text. Furthermore, the context information of an event is useful to infer the coreference between events. Thus, in order to reduce the errors propagated from event argument extraction and use context information effectively, we propose a multi-loss neural network model that does not need any argument information to do the within-document event coreference resolution task and achieve a significant performance than the state-of-the-art methods.

preprint2020arXiv

Feature Distillation With Guided Adversarial Contrastive Learning

Deep learning models are shown to be vulnerable to adversarial examples. Though adversarial training can enhance model robustness, typical approaches are computationally expensive. Recent works proposed to transfer the robustness to adversarial attacks across different tasks or models with soft labels.Compared to soft labels, feature contains rich semantic information and holds the potential to be applied to different downstream tasks. In this paper, we propose a novel approach called Guided Adversarial Contrastive Distillation (GACD), to effectively transfer adversarial robustness from teacher to student with features. We first formulate this objective as contrastive learning and connect it with mutual information. With a well-trained teacher model as an anchor, students are expected to extract features similar to the teacher. Then considering the potential errors made by teachers, we propose sample reweighted estimation to eliminate the negative effects from teachers. With GACD, the student not only learns to extract robust features, but also captures structural knowledge from the teacher. By extensive experiments evaluating over popular datasets such as CIFAR-10, CIFAR-100 and STL-10, we demonstrate that our approach can effectively transfer robustness across different models and even different tasks, and achieve comparable or better results than existing methods. Besides, we provide a detailed analysis of various methods, showing that students produced by our approach capture more structural knowledge from teachers and learn more robust features under adversarial attacks.

preprint2020arXiv

Human Mobility Trends during the COVID-19 Pandemic in the United States

In March of this year, COVID-19 was declared a pandemic and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations regarding the pandemic propagation and the non-pharmaceutical interventions. All mobility metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states and becomes more stable after the stay-at-home order with a smaller range of fluctuation. There exists overall mobility heterogeneity between the income or population density groups. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. The study suggests that the public mobility trends conform with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.

preprint2020arXiv

Intelligent Reflecting Surface Aided Network: Power Control for Physical-Layer Broadcasting

As a recently proposed idea for future wireless systems, intelligent reflecting surface (IRS) can assist communications between entities which do not have high-quality direct channels in between. Specifically, an IRS comprises many low-cost passive elements, each of which reflects the incident signal by incurring a phase change so that the reflected signals add coherently at the receiver. In this paper, for an IRS-aided wireless network, we study the problem of power control at the base station (BS) for physical-layer broadcasting under quality of service (QoS) constraints at mobile users, by jointly designing the transmit beamforming at the BS and the phase shifts of the IRS units. Furthermore, we derive a lower bound of the minimum transmit power at the BS to present the performance bound for optimization methods. Simulation results show that, the transmit power at the BS approaches the lower bound with the increase of the number of IRS units, and is much lower than that of the communication system without IRS.

preprint2020arXiv

Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach

Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS), and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLFPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLFPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.

preprint2020arXiv

Intelligent Reflecting Surface Meets Mobile Edge Computing: Enhancing Wireless Communications for Computation Offloading

We consider computation offloading for edge computing in a wireless network equipped with intelligent reflecting surfaces (IRSs). IRS is an emerging technology and has recently received great attention since they can improve the wireless propagation environment in a configurable manner and enhance the connections between mobile devices (MDs) and access points (APs). At this point not many papers consider edge computing in the novel context of wireless communications aided by IRS. In our studied setting, each MD offloads computation tasks to the edge server located at the AP to reduce the associated comprehensive cost, which is a weighted sum of time and energy. The edge server adjusts the IRS to maximize its earning while maintaining MDs' incentives for offloading and guaranteeing each MD a customized information rate. This problem can be formulated into a difficult optimization problem, which has a sum-of-ratio objective function as well as a bunch of nonconvex constraints. To solve this problem, we first develop an iterative evaluation procedure to identify the feasibility of the problem when confronting an arbitrary set of information rate requirement. This method serves as a sufficient condition for the problem being feasible and provides a feasible solution. Based on that we develop an algorithm to optimize the objective function. Our numerical results show that the presence of IRS enables the AP to guarantee higher information rate to all MDs and at the same time improve the earning of the edge server.

preprint2020arXiv

Intelligent Reflecting Surface-Aided Backscatter Communications

We introduce a novel system setup where a backscatter device operates in the presence of an intelligent reflecting surface (IRS). In particular, we study the bistatic backscatter communication (BackCom) system assisted by an IRS. The phase shifts at the IRS are optimized jointly with the transmit beamforming vector of the carrier emitter to minimize the transmit power consumption at the carrier emitter whilst guaranteeing a required BackCom performance. The unique channel characteristics arising from multiple reflections at the IRS render the optimization problem highly non-convex. Therefore, we jointly utilize the minorization-maximization algorithm and the semidefinite relaxation technique to present an approximate solution for the optimal IRS phase shift design. We also extend our analytical results to the monostatic BackCom system. Numerical results indicate that the introduction of the IRS brings about considerable reductions in transmit power, even with moderate IRS sizes, which can be translated to range increases over the non-IRS-assisted BackCom system.

preprint2020arXiv

IRS-Assisted Millimeter Wave Communications: Joint Power Allocation and Beamforming Design

Intelligent reflecting surface (IRS) technology offers more feasible propagation paths for millimeter-wave (mmWave) communication systems to overcome blockage than existing technologies. In this paper, we consider a downlink wireless system with the IRS and formulate a joint power allocation and beamforming design problem to maximize the weighted sum-rate, which is a multi-variable optimization problem. To solve the problem, we propose a novel alternating manifold optimization based beamforming algorithm. Simulation results show that our proposed optimization algorithm outperforms existing algorithms significantly in improving the weighted sum-rate of the wireless communication system.

preprint2020arXiv

Knowledge Guided Metric Learning for Few-Shot Text Classification

The training of deep-learning-based text classification models relies heavily on a huge amount of annotation data, which is difficult to obtain. When the labeled data is scarce, models tend to struggle to achieve satisfactory performance. However, human beings can distinguish new categories very efficiently with few examples. This is mainly due to the fact that human beings can leverage knowledge obtained from relevant tasks. Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. A novel parameter generator network is investigated to this end, which is able to use the external knowledge to generate relation network parameters. Metrics can be transferred among tasks when equipped with these generated parameters, so that similar tasks use similar metrics while different tasks use different metrics. Through experiments, we demonstrate that our method outperforms the state-of-the-art few-shot text classification models.

preprint2020arXiv

Local Differential Privacy and Its Applications: A Comprehensive Survey

With the fast development of Information Technology, a tremendous amount of data have been generated and collected for research and analysis purposes. As an increasing number of users are growing concerned about their personal information, privacy preservation has become an urgent problem to be solved and has attracted significant attention. Local differential privacy (LDP), as a strong privacy tool, has been widely deployed in the real world in recent years. It breaks the shackles of the trusted third party, and allows users to perturb their data locally, thus providing much stronger privacy protection. This survey provides a comprehensive and structured overview of the local differential privacy technology. We summarise and analyze state-of-the-art research in LDP and compare a range of methods in the context of answering a variety of queries and training different machine learning models. We discuss the practical deployment of local differential privacy and explore its application in various domains. Furthermore, we point out several research gaps, and discuss promising future research directions.

preprint2020arXiv

MAT: Motion-Aware Multi-Object Tracking

Modern multi-object tracking (MOT) systems usually model the trajectories by associating per-frame detections. However, when camera motion, fast motion, and occlusion challenges occur, it is difficult to ensure long-range tracking or even the tracklet purity, especially for small objects. Although re-identification is often employed, due to noisy partial-detections, similar appearance, and lack of temporal-spatial constraints, it is not only unreliable and time-consuming, but still cannot address the false negatives for occluded and blurred objects. In this paper, we propose an enhanced MOT paradigm, namely Motion-Aware Tracker (MAT), focusing more on various motion patterns of different objects. The rigid camera motion and nonrigid pedestrian motion are blended compatibly to form the integrated motion localization module. Meanwhile, we introduce the dynamic reconnection context module, which aims to balance the robustness of long-range motion-based reconnection, and includes the cyclic pseudo-observation updating strategy to smoothly fill in the tracking fragments caused by occlusion or blur. Additionally, the 3D integral image module is presented to efficiently cut useless track-detection association connections with temporal-spatial constraints. Extensive experiments on MOT16 and MOT17 challenging benchmarks demonstrate that our MAT approach can achieve the superior performance by a large margin with high efficiency, in contrast to other state-of-the-art trackers.

preprint2020arXiv

Observed mobility behavior data reveal social distancing inertia

The research team has utilized an integrated dataset, consisting of anonymized location data, COVID-19 case data, and census population information, to study the impact of COVID-19 on human mobility. The study revealed that statistics related to social distancing, namely trip rate, miles traveled per person, and percentage of population staying at home have all showed an unexpected trend, which we named social distancing inertia. The trends showed that as soon as COVID-19 cases were observed, the statistics started improving, regardless of government actions. This suggests that a portion of population who could and were willing to practice social distancing voluntarily and naturally reacted to the emergence of COVID-19 cases. However, after about two weeks, the statistics saturated and stopped improving, despite the continuous rise in COVID-19 cases. The study suggests that there is a natural behavior inertia toward social distancing, which puts a limit on the extent of improvement in the social-distancing-related statistics. The national data showed that the inertia phenomenon is universal, happening in all the U.S. states and for all the studied statistics. The U.S. states showed a synchronized trend, regardless of the timeline of their statewide COVID-19 case spreads or government orders.

preprint2020arXiv

Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA

Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation track with 6 tasks and attracted more than 1,600 teams. In this paper, we give an overview of the knowledge graph evaluation tract at CCKS 2019. By reviewing the task definition, successful methods, useful resources, good strategies and research challenges associated with each task in CCKS 2019, this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches.

preprint2020arXiv

Poisson Kernel Avoiding Self-Smoothing in Graph Convolutional Networks

Graph convolutional network (GCN) is now an effective tool to deal with non-Euclidean data, such as social networks in social behavior analysis, molecular structure analysis in the field of chemistry, and skeleton-based action recognition. Graph convolutional kernel is one of the most significant factors in GCN to extract nodes' feature, and some improvements of it have reached promising performance theoretically and experimentally. However, there is limited research about how exactly different data types and graph structures influence the performance of these kernels. Most existing methods used an adaptive convolutional kernel to deal with a given graph structure, which still not reveals the internal reasons. In this paper, we started from theoretical analysis of the spectral graph and studied the properties of existing graph convolutional kernels. While taking some designed datasets with specific parameters into consideration, we revealed the self-smoothing phenomenon of convolutional kernels. After that, we proposed the Poisson kernel that can avoid self-smoothing without training any adaptive kernel. Experimental results demonstrate that our Poisson kernel not only works well on the benchmark dataset where state-of-the-art methods work fine, but also is evidently superior to them in synthetic datasets.

preprint2020arXiv

Quarantine Fatigue: first-ever decrease in social distancing measures after the COVID-19 outbreak before reopening United States

By the emergence of the novel coronavirus disease (COVID-19) in Wuhan, China, and its rapid outbreak worldwide, the infectious illness has changed our everyday travel patterns. In this research, our team investigated the changes in the daily mobility pattern of people during the pandemic by utilizing an integrated data panel. To incorporate various aspects of human mobility, the team focused on the Social Distancing Index (SDI) which was calculated based on five basic mobility measures. The SDI patterns showed a plateau stage in the beginning of April that lasted for about two weeks. This phenomenon then followed by a universal decline of SDI, increased number of trips and reduction in percentage of people staying at home. We called the observation Quarantine Fatigue. The Rate of Change (ROC) method was employed to trace back the start date of quarantine fatigue which was indicated to be April 15th. Our analysis showed that despite the existence of state-to-state variations, most states started experiencing a quarantine fatigue phenomenon during the same period. This observation became more important by knowing that none of the states had officially announced the reopening until late April showing that people decided to loosen up their social distancing practices before the official reopening announcement. Moreover, our analysis indicated that official reopening led to a rapid decline in SDI, raising the concern of a second wave of outbreak. The synchronized trend among states also emphasizes the importance of a more nationwide decision-making attitude for the future as the condition of each state depends on the nationwide behavior.

preprint2020arXiv

Reconfigurable Intelligent Surface for MISO Systems with Proportional Rate Constraints

This paper investigates the system spectral efficiency (SE) in reconfigurable intelligent surface (RIS)-aided multiuser multiple-input single-output (MISO) systems, where RIS can reconfigure the propagation environment via a large number of controllable and intelligent phase shifters. In order to explore the system SE performance behavior with user proportional fairness for such a system, an optimization problem is formulated to maximize the SE by jointly considering the power allocation at the base station (BS) and phase shift at the RIS, under nonlinear proportional rate fairness constraints. To solve the nonconvex optimization problem, an effective solution is developed, which capitalizes on an iterative algorithm with closed-form expressions, i.e., alternatively optimizing the transmit power at the BS and the reflecting phase shift at the RIS. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed alternative algorithm.

preprint2020arXiv

Refinements in Motion and Appearance for Online Multi-Object Tracking

Modern multi-object tracking (MOT) system usually involves separated modules, such as motion model for location and appearance model for data association. However, the compatible problems within both motion and appearance models are always ignored. In this paper, a general architecture named as MIF is presented by seamlessly blending the Motion integration, three-dimensional(3D) Integral image and adaptive appearance feature Fusion. Since the uncertain pedestrian and camera motions are usually handled separately, the integrated motion model is designed using our defined intension of camera motion. Specifically, a 3D integral image based spatial blocking method is presented to efficiently cut useless connections between trajectories and candidates with spatial constraints. Then the appearance model and visibility prediction are jointly built. Considering scale, pose and visibility, the appearance features are adaptively fused to overcome the feature misalignment problem. Our MIF based tracker (MIFT) achieves the state-of-the-art accuracy with 60.1 MOTA on both MOT16&17 challenges.

preprint2020arXiv

Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems

Simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) are two promising techniques for providing enhanced wireless communication capability and sustainable energy supply to energy-constrained wireless devices. Moreover, the combination of the IRS and the SWIPT can create the "one plus one greater than two" effect. However, due to the broadcast nature of wireless media, the IRS-aided SWIPT systems are vulnerable to eavesdropping. In this paper, we study the security issue of the IRS-aided SWIPT systems. The objective is to maximize the secrecy rate by jointly designing the transmit beamforming and artificial noise (AN) covariance matrix at a base station (BS) and reflective beamforming at an IRS, under transmit power constraint at the BS and energy harvesting (EH) constraints at multiple energy receivers. To tackle the formulated non-convex problem, we first employ an alternating optimization (AO) algorithm to decouple the coupling variables. Then, reflective beamforming, transmit beamforming and AN covariance matrix can be optimized by using a penalty-based algorithm and semidefinite relaxation (SDR) method, respectively. Simulation results demonstrate the effectiveness of the proposed scheme over baseline schemes.

preprint2020arXiv

Towards Distributed Privacy-Preserving Prediction

In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a generally applicable Distributed Privacy-Preserving Prediction (DPPP) framework, in which instead of sharing more sensitive data or model parameters, an untrusted aggregator combines only multiple models' predictions under provable privacy guarantee. Our framework integrates two main techniques to guarantee individual privacy. First, we introduce the improved Binomial Mechanism and Discrete Gaussian Mechanism to achieve distributed differential privacy. Second, we utilize homomorphic encryption to ensure that the aggregator learns nothing but the noisy aggregated prediction. Experimental results demonstrate that our framework has comparable performance to the non-private frameworks and delivers better results than the local differentially private framework and standalone framework.

preprint2019arXiv

Neutron Spin Resonance in the Heavily Hole-doped KFe$_{2}$As$_{2}$ Superconductor

We report high-resolution neutron scattering measurements of the low energy spin fluctuations of KFe$_{2}$As$_{2}$, the end member of the hole-doped Ba$_{1-x}$K$_x$Fe$_2$As$_2$ family with only hole pockets, above and below its superconducting transition temperature $T_c$ ($\sim$ 3.5 K). Our data reveals clear spin fluctuations at the incommensurate wave vector ($0.5\pmδ$, 0, $L$), ($δ$ = 0.2)(1-Fe unit cell), which exhibit $L$-modulation peaking at $L=0.5$. Upon cooling to the superconducting state, the incommensurate spin fluctuations gradually open a spin-gap and form a sharp spin resonance mode. The incommensurability ($2δ$ = 0.4) of the resonance mode ($\sim1.2$ meV) is considerably larger than the previously reported value ($2δ$ $\approx0.32$) at higher energies ($\ge\sim6$ meV). The determination of the momentum structure of spin fluctuation in the low energy limit allows a direct comparison with the realistic Fermi surface and superconducting gap structure. Our results point to an $s$-wave pairing with a reversed sign between the hole pockets near the zone center in KFe$_{2}$As$_{2}$.

preprint2019arXiv

PandaX limits on light dark matter with light mediator in the singlet extension of MSSM

Using the latest PandaX limits on light dark matter (DM) with light mediator, we check the implication on the parameter space of the general singlet extension of MSSM (without $Z_3$ symmetry), which can have a sizable DM self-interaction to solve the small-scale structure problem. We find that the PandaX limits can stringently constrain such a paramter space, depending on the coupling $λ$ between the singlet and doublet Higgs fields. For the singlet extension of MSSM with $Z_3$ symmetry, the so-called NMSSM, we also demonstrate the PandaX constraints on its parameter space which gives a light DM with correct relic density but without sufficient self-interaction to solve the small-scale structure problem. We find that in this NMSSM the GeV dark matter with a sub-GeV mediator has been stringently constrained.

preprint2019arXiv

Testing electroweak SUSY for muon $g-2$ and dark matter at the LHC and beyond

Given that the LHC experiment has produced strong constraints on the colored supersymmetric particles (sparticles), testing the electroweak supersymmetry (EWSUSY) will be the next crucial task at the LHC. On the other hand, the light electroweakinos and sleptons in the EWSUSY can also contribute to the dark matter (DM) and low energy lepton observables. The precision measurements of them will provide the indirect evidence of SUSY. In this work, we confront the EWSUSY with the muon $g-2$ anomaly, the DM relic density, the direct detection limits and the latest LHC Run-2 data. We find that the sneutrino DM or the neutralino DM with sizable higgsino component has been excluded by the direct detections. Then two viable scenarios are pinned down: one has the light compressed bino and sleptons but heavy higgsinos, and the other has the light compressed bino, winos and sleptons. In the former case, the LSP and slepton masses have to be smaller than about 350 GeV. While in the latter case, the LSP and slepton masses have to be smaller than about 700 GeV and 800 GeV, respectively. From investigating the observability of these sparticles in both scenarios at future colliders, it turns out that the HE-LHC with a luminosity of 15 ab$^{-1}$ can exclude the whole BHL and most part of BWL scenarios at $2σ$ level. The precision measurement of the Higgs couplings at the lepton colliders could play a complementary role of probing the BWL scenario.

preprint2017arXiv

Self-Taught Convolutional Neural Networks for Short Text Clustering

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction methods. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets.

preprint2016arXiv

Hexagonal phase stabilization and magnetic orders of multiferroic Lu$_{1-x}$Sc$_x$FeO$_3$

Hexagonal LuFeO$_3$ has drawn a lot of research attention due to its contentious room-temperature multiferroicity. Due to the unstability of hexagonal phase in the bulk form, most experimental studies focused on LuFeO$_3$ thin films which can be stabilized by strain using proper substrates. Here we report on the hexagonal phase stabilization, magnetism, and magnetoelectric coupling of bulk LuFeO$_3$ by partial Sc-substitution of Lu. First, our first-principles calculations show that the hexagonal structure can be stabilized by partial Sc substitution, while the multiferroic properties including the noncollinear magnetic order and geometric ferroelectricity remain robustly unaffected. Therefore, Lu$_{1-x}$Sc$_x$FeO$_3$ can act as a platform to check the multiferroicity of LuFeO$_3$ and related materials in the bulk form. Second, the magnetic characterizations on bulk Lu$_{1-x}$Sc$_x$FeO$_3$ demonstrate a magnetic anomaly (probable antiferromagnetic ordering) above room temperature, $\sim425-445$ K, followed by magnetic transitions in low temperatures ($\sim167-172$ K). In addition, a magnetoelectric response is observed in the low temperature region. Our study provides useful information on the multiferroic physics of hexagonal $R$FeO$_3$ and related systems.

preprint2016arXiv

Higgs exotic decays in general NMSSM with self-interacting dark matter

Under current LHC and dark matter constraints, the general NMSSM can have self-interacting dark matter to explain the cosmological small structure. In this scenario, the dark matter is the light singlino-like neutralino ($χ$) which self-interacts through exchanging the light singlet-like scalars ($h_1, a_1$). These light scalars and neutralinos inevitably interact with the 125 GeV SM-like Higgs boson ($h_{SM}$), which cause the Higgs exotic decays $h_{SM} \to h_1 h_1, a_1 a_1, χχ$. We first demonstrate the parameter space required by the explanation of the cosmological small structure and then display the Higgs exotic decays. We find that in such a parameter space the Higgs exotic decays can have branching ratios of a few percent, which should be accessible in the future $e^+e^-$ colliders.

preprint2016arXiv

Magnetic ground state of FeSe

Elucidating the nature of the magnetism of a high-temperature superconductor is crucial for establishing its pairing mechanism. The parent compounds of the cuprate and iron-pnictide superconductors exhibit Néel and stripe magnetic order, respectively. However, FeSe, the structurally simplest iron-based superconductor, shows nematic order (Ts = 90 K), but not magnetic order in the parent phase, and its magnetic ground state is intensely debated. Here, we report inelastic neutron-scattering experiments that reveal both stripe and Néel spin fluctuations over a wide energy range at 110 K. On entering the nematic phase, a substantial amount of spectral weight is transferred from the Néel to the stripe spin fluctuations. Moreover, the total fluctuating magnetic moment of FeSe is ~ 60% larger than that in the iron pnictide BaFe2As2. Our results suggest that FeSe is a novel S = 1 nematic quantum-disordered paramagnet interpolating between the Néel and stripe magnetic instabilities.

preprint2016arXiv

Observation of quasi-two-dimensional Dirac fermions in ZrTe5

Since the discovery of graphene, layered materials have attracted extensive interests owing to their unique electronic and optical characteristics. Among them, Dirac semimetal, one of the most appealing categories, has been a long-sought objective in layered systems beyond graphene. Recently, layered pentatelluride ZrTe5 was found to host signatures of Dirac semimetal. However, the low Fermi level in ZrTe5 strongly hinders a comprehensive understanding of the whole picture of electronic states through photoemission measurements, especially in the conduction band. Here, we report the observation of Dirac fermions in ZrTe5 through magneto-optics and magneto-transport. By applying magnetic field, we observe a square-root-B dependence of inter-Landau-level resonance and Shubnikov-de Haas (SdH) oscillations with non-trivial Berry phase, both of which are hallmarks of Dirac fermions. The angular-dependent SdH oscillations show a clear quasi-two-dimensional feature with highly anisotropic Fermi surface and band topology, in stark contrast to the 3D Dirac semimetal such as Cd3As2. This is further confirmed by the angle-dependent Berry phase measurements and the observation of bulk quantum Hall plateaus. The unique band dispersion is theoretically understood: the system is at the critical point between a 3D Dirac semimetal and a topological insulator phase. With the confined interlayer dispersion and reducible dimensionality, our work establishes ZrTe5 as an ideal platform for exploring exotic physical phenomena of Dirac fermions.

preprint2016arXiv

Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information

With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Knowledge base-based question answering (KB-QA) is one of the most promising approaches to access the substantial knowledge. Meantime, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is unable to express the proper information of the question. Hence, we present a neural attention-based model to represent the questions dynamically according to the different focuses of various candidate answer aspects. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. And it also alleviates the out of vocabulary (OOV) problem, which helps the attention model to represent the question more precisely. The experimental results on WEBQUESTIONS demonstrate the effectiveness of the proposed approach.

preprint2016arXiv

Selective AP-sequence Based Indoor Localization without Site Survey

In this paper, we propose an indoor localization system employing ordered sequence of access points (APs) based on received signal strength (RSS). Unlike existing indoor localization systems, our approach does not require any time-consuming and laborious site survey phase to characterize the radio signals in the environment. To be precise, we construct the fingerprint map by cutting the layouts of the interested area into regions with only the knowledge of positions of APs. This can be done offline within a second and has a potential for practical use. The localization is then achieved by matching the ordered AP-sequence to the ones in the fingerprint map. Different from traditional fingerprinting that employing all APs information, we use only selected APs to perform localization, due to the fact that, without site survey, the possibility in obtaining the correct AP sequence is lower if it involves more APs. Experimental results show that, the proposed system achieves localization accuracy < 5m with an accumulative density function (CDF) of 50% to 60% depending on the density of APs. Furthermore, we observe that, using all APs for localization might not achieve the best localization accuracy, e.g. in our case, 4 APs out of total 7 APs achieves the best performance. In practice, the number of APs used to perform localization should be a design parameter based on the placement of APs.

preprint2016arXiv

Solving Resource Recharging Station Location-routing Problem through a Resource-space-time Network Representation

The resource recharging station location routing problem is a generalization of the location routing problem with sophisticated and critical resource consumption and recharging constraints. Based on a representation of discretized acyclic resource-space-time networks, we propose a generic formulation to optimize dynamic infrastructure location and routes decisions. The proposed integer linear programming formulation could greatly simplify the modeling representation of time window, resource change, and sub-tour constraints through a well-structured multi-dimensional network. An approximation solution framework based on the Lagrangian relaxation is developed to decompose the problem to a knapsack sub-problem for selecting recharging stations and a vehicle routing sub-problem in a space-time network. Both sub-problems can be solved through dynamic programming algorithms to obtain optimal solution. A number of experiments are used to demonstrate the Lagrangian multiplier adjustment-based location routing decision making, as well as the effectiveness of the developed algorithm in large-scale networks.

preprint2016arXiv

Transition from sign-reversed to sign-preserved Cooper-pairing symmetry in sulfur-doped iron selenide superconductors

An essential step toward elucidating the mechanism of superconductivity is to determine the sign/phase of superconducting order parameter, as it is closely related to the pairing interaction. In conventional superconductors, the electron-phonon interaction induces attraction between electrons near the Fermi energy and results in a sign-preserved s-wave pairing. For high-temperature superconductors, including cuprates and iron-based superconductors, prevalent weak coupling theories suggest that the electron pairing is mediated by spin fluctuations which lead to repulsive interactions, and therefore that a sign-reversed pairing with an s+-or d-wave symmetry is favored. Here, by using magnetic neutron scattering, a phase sensitive probe of superconducting gap, we report the observation of a transition from the sign-reversed to sign-preserved Cooper-pairing symmetry with insignificant changes in Tc in the S-doped iron selenide superconductors KxFe2-y(Se1-zSz)2. We show that a rather sharp magnetic resonant mode well below the superconducting gap (2delta) in the undoped sample (z = 0) is replaced by a broad hump structure above 2delta under 50% S doping. These results cannot be readily explained by simple spin fluctuation-exchange pairing theories and, therefore, multiple pairing channels are required to describe superconductivity in this system. Our findings may also yield a simple explanation for the sometimes contradictory data on the sign of the superconducting order parameter in iron-based materials.

preprint2015arXiv

$n$-ary Hom-Nambu algebras

In this paper, we define $ω$-derivations, and study some properties of $ω$-derivations, with its properties we can structure a new $n$-ary Hom-Nambu algebra from an $n$-ary Hom-Nambu algebra. In addition, we also give derivations and representations of $n$-ary Hom-Nambu algebras.

preprint2015arXiv

Connectivity in Secure Wireless Sensor Networks under Transmission Constraints

In wireless sensor networks (WSNs), the Eschenauer-Gligor (EG) key pre-distribution scheme is a widely recognized way to secure communications. Although connectivity properties of secure WSNs with the EG scheme have been extensively investigated, few results address physical transmission constraints. These constraints reflect real-world implementations of WSNs in which two sensors have to be within a certain distance from each other to communicate. In this paper, we present zero-one laws for connectivity in WSNs employing the EG scheme under transmission constraints. These laws help specify the critical transmission ranges for connectivity. Our analytical findings are confirmed via numerical experiments. In addition to secure WSNs, our theoretical results are also applied to frequency hopping in wireless networks.

preprint2015arXiv

Crossover of the pairing symmetry from $s$- to $d$-wave in iron pnictide superconductors

One of the major themes in the physics of condensed matter is unconventional superconductivity in correlated electronic systems. The symmetry structure of Cooper pairs is thought to be the key to the understanding of the pairing mechanism of their superconductivity. In iron pnictide superconductors, the precise symmetry of the order parameter as well its evolution with doping remains highly controversial. There is no general consensus on the nature of pairing in iron-based superconductors leaving the perspectives ranging from $s^{++}$ wave, to $s^{\pm}$, and $d$ wave. Here we show that doping induced a sudden change of the pairing symmetry from the $s^{++}$- to $d$-wave in the BaFe$_{2-x}$Ni$_{x}$As$_{2}$ system with an $s^{\pm}$ state near the crossover boundary at $x=0.15$. The presence of the $s^{\pm}$ state is of particular scientific interest because an intermediate phase breaks the pairing symmetry when the $s$- and $d$-wave phases come together to interfere. For $x <$ 0.15, the temperature dependence of the London penetration depth, lower critical field, and specific heat in the superconducting (SC) state provides strong evidence for a two-band $s$-wave order parameter. Upon doping for $x >$ 0.15, we find the temperature and magnetic-field contributions to the specific heat in $T^{2}$ and $\sqrt{H}$, respectively, indicating line nodes in the SC gap. These findings thus clarify the symmetry nature as well as the evolution of the SC order parameter with doping in pnictide superconductors.

preprint2015arXiv

Designing Securely and Reliably Connected Wireless Sensor Networks

In wireless sensor networks, the $q$-composite key predistribution scheme is a widely recognized way to secure communications. Although connectivity properties of secure sensor networks with the $q$-composite scheme have been studied in the literature, few results address physical transmission constraints since it is challenging to analyze the network connectivity in consideration of both the $q$-composite scheme and transmission constraints together. These transmission constraints reflect real-world implementations of sensor networks in which two sensors have to be within a certain distance from each other to communicate. In this paper, we rigorously derive conditions for connectivity in sensor networks employing the $q$-composite scheme under transmission constraints. Furthermore, we extend the analysis to consider the unreliability of wireless links by modeling each link being independently active with some probability. Our results provide useful guidelines for designing securely and reliably connected sensor networks. We also present numerical experiments to confirm the analytical results.

preprint2015arXiv

Exact Analysis of k-Connectivity in Secure Sensor Networks with Unreliable Links

The Eschenauer--Gligor (EG) random key predistribution scheme has been widely recognized as a typical approach to secure communications in wireless sensor networks (WSNs). However, there is a lack of precise probability analysis on the reliable connectivity of WSNs under the EG scheme. To address this, we rigorously derive the asymptotically exact probability of $k$-connectivity in WSNs employing the EG scheme with unreliable links represented by independent on/off channels, where $k$-connectivity ensures that the network remains connected despite the failure of any $(k-1)$ sensors or links. Our analytical results are confirmed via numerical experiments, and they provide precise guidelines for the design of secure WSNs that exhibit a desired level of reliability against node and link failures.

preprint2015arXiv

How to Generate a Good Word Embedding?

We analyze three critical components of word embedding training: the model, the corpus, and the training parameters. We systematize existing neural-network-based word embedding algorithms and compare them using the same corpus. We evaluate each word embedding in three ways: analyzing its semantic properties, using it as a feature for supervised tasks and using it to initialize neural networks. We also provide several simple guidelines for training word embeddings. First, we discover that corpus domain is more important than corpus size. We recommend choosing a corpus in a suitable domain for the desired task, after that, using a larger corpus yields better results. Second, we find that faster models provide sufficient performance in most cases, and more complex models can be used if the training corpus is sufficiently large. Third, the early stopping metric for iterating should rely on the development set of the desired task rather than the validation loss of training embedding.

preprint2015arXiv

k-Connectivity of Random Key Graphs

Random key graphs represent topologies of secure wireless sensor networks that apply the seminal Eschenauer-Gligor random key predistribution scheme to secure communication between sensors. These graphs have received much attention and also been used in diverse application areas beyond secure sensor networks; e.g., cryptanalysis, social networks, and recommender systems. Formally, a random key graph with $n$ nodes is constructed by assigning each node $X_n$ keys selected uniformly at random from a pool of $Y_n$ keys and then putting an undirected edge between any two nodes sharing at least one key. Considerable progress has been made in the literature to analyze connectivity and $k$-connectivity of random key graphs, where $k$-connectivity of a graph ensures connectivity even after the removal of $k$ nodes or $k$ edges. Yet, it still remains an open question for $k$-connectivity in random key graphs under $X_n \geq 2$ and $X_n = o(\sqrt{\ln n})$ (the case of $X_n=1$ is trivial). In this paper, we answer the above problem by providing an exact analysis of $k$-connectivity in random key graphs under $X_n \geq 2$.

preprint2015arXiv

Landau level splitting in Cd3As2 under high magnetic fields

Three-dimensional topological Dirac semimetals (TDSs) are a new kind of Dirac materials that exhibit linear energy dispersion in the bulk and can be viewed as three-dimensional graphene. It has been proposed that TDSs can be driven to other exotic phases like Weyl semimetals, topological insulators and topological superconductors by breaking certain symmetries. Here we report the first transport experiment on Landau level splitting in TDS Cd3As2 single crystals under high magnetic fields, suggesting the removal of spin degeneracy by breaking time reversal symmetry. The detected Berry phase develops an evident angular dependence and possesses a crossover from nontrivial to trivial state under high magnetic fields, a strong hint for a fierce competition between the orbit-coupled field strength and the field-generated mass term. Our results unveil the important role of symmetry breaking in TDSs and further demonstrate a feasible path to generate a Weyl semimetal phase by breaking time reversal symmetry.

preprint2015arXiv

Monotone Increasing Properties and Their Phase Transitions in Uniform Random Intersection Graphs

Uniform random intersection graphs have received much interest and been used in diverse applications. A uniform random intersection graph with $n$ nodes is constructed as follows: each node selects a set of $K_n$ different items uniformly at random from the same pool of $P_n$ distinct items, and two nodes establish an undirected edge in between if and only if they share at least one item. For such graph denoted by $G(n, K_n, P_n)$, we present the following results in this paper. First, we provide an exact analysis on the probabilities of $G(n, K_n, P_n)$ having a perfect matching and having a Hamilton cycle respectively, under $P_n = ω\big(n (\ln n)^5\big)$ (all asymptotic notation are understood with $n \to \infty$). The analysis reveals that just like ($k$-)connectivity shown in prior work, for both properties of perfect matching containment and Hamilton cycle containment, $G(n, K_n, P_n)$ also exhibits phase transitions: for each property above, as $K_n$ increases, the limit of the probability that $G(n, K_n, P_n)$ has the property increases from $0$ to $1$. Second, we compute the phase transition widths of $G(n, K_n, P_n)$ for $k$-connectivity (KC), perfect matching containment (PMC), and Hamilton cycle containment (HCC), respectively. For a graph property $R$ and a positive constant $a < \frac{1}{2}$, with the phase transition width $d_n(R, a)$ defined as the difference between the minimal $K_n$ ensuring $G(n, K_n, P_n)$ having property $R$ with probability at least $1-a$ or $a$, we show for any positive constants $a<\frac{1}{2}$ and $k$: (i) If $P_n=Ω(n)$ and $P_n=o(n\ln n)$, then $d_n(KC, a)$ is either $0$ or $1$ for each $n$ sufficiently large. (ii) If $P_n=Θ(n\ln n)$, then $d_n(KC, a)=Θ(1)$. (iii) If $P_n=ω(n\ln n)$, then $d_n(KC, a)=ω(1)$. (iv) If $P_n=ω\big(n (\ln n)^5\big)$, $d_n(PMC, a)$ and $d_n(HCC, a)$ are both $ω(1)$.

preprint2015arXiv

Nodeless superconductivity in the presence of spin-density wave in pnictide superconductors: The case of BaFe$_{2-x}$Ni$_{x}$As$_{2}$

The characteristics of Fe-based superconductors are manifested in their electronic, magnetic properties, and pairing symmetry of the Cooper pair, but the latter remain to be explored. Usually in these materials, superconductivity coexists and competes with magnetic order, giving unconventional pairing mechanisms. We report on the results of the bulk magnetization measurements in the superconducting state and the low-temperature specific heat down to 0.4 K for BaFe$_{2-x}$Ni$_{x}$As$_{2}$ single crystals. The {electronic} specific heat displays a pronounced anomaly at the superconducting transition temperature and a small residual part {at low temperatures in the superconducting state}. The normal-state Sommerfeld coefficient increases with Ni doping for $x$ = 0.092, 0.096, and 0.10, which illustrates the competition between magnetism and superconductivity. Our analysis of the temperature dependence of the superconducting-state specific heat and the London penetration depth provides strong evidence for a two-band $s$-wave order parameter. Further, the data of the London penetration depth calculated from the lower critical field follow an exponential temperature dependence, characteristic of a fully gapped superconductor. These observations clearly show that the superconducting gap in the nearly optimally doped compounds is nodeless.

preprint2015arXiv

On connectivity in a general random intersection graph

There has been growing interest in studies of general random intersection graphs. In this paper, we consider a general random intersection graph $\mathbb{G}(n,\overrightarrow{a}, \overrightarrow{K_n},P_n)$ defined on a set $\mathcal{V}_n$ comprising $n$ vertices, where $\overrightarrow{a}$ is a probability vector $(a_1,a_2,\ldots,a_m)$ and $\overrightarrow{K_n}$ is $(K_{1,n},K_{2,n},\ldots,K_{m,n})$. This graph has been studied in the literature including a most recent work by Yağan [arXiv:1508.02407]. Suppose there is a pool $\mathcal{P}_n$ consisting of $P_n$ distinct objects. The $n$ vertices in $\mathcal{V}_n$ are divided into $m$ groups $\mathcal{A}_1, \mathcal{A}_2, \ldots, \mathcal{A}_m$. Each vertex $v$ is independently assigned to exactly a group according to the probability distribution with $\mathbb{P}[v \in \mathcal{A}_i]= a_i$, where $i=1,2,\ldots,m$. Afterwards, each vertex in group $\mathcal{A}_i$ independently chooses $K_{i,n}$ objects uniformly at random from the object pool $\mathcal{P}_n$. Finally, an undirected edge is drawn between two vertices in $\mathcal{V}_n$ that share at least one object. This graph model $\mathbb{G}(n,\overrightarrow{a}, \overrightarrow{K_n},P_n)$ has applications in secure sensor networks and social networks. We investigate connectivity in this general random intersection graph $\mathbb{G}(n,\overrightarrow{a}, \overrightarrow{K_n},P_n)$ and present a sharp zero-one law. Our result is also compared with the zero-one law established by Yağan.

preprint2015arXiv

Random intersection graphs and their applications in security, wireless communication, and social networks

Random intersection graphs have received much interest and been used in diverse applications. They are naturally induced in modeling secure sensor networks under random key predistribution schemes, as well as in modeling the topologies of social networks including common-interest networks, collaboration networks, and actor networks. Simply put, a random intersection graph is constructed by assigning each node a set of items in some random manner and then putting an edge between any two nodes that share a certain number of items. Broadly speaking, our work is about analyzing random intersection graphs, and models generated by composing it with other random graph models including random geometric graphs and Erdős-Rényi graphs. These compositional models are introduced to capture the characteristics of various complex natural or man-made networks more accurately than the existing models in the literature. For random intersection graphs and their compositions with other random graphs, we study properties such as ($k$-)connectivity, ($k$-)robustness, and containment of perfect matchings and Hamilton cycles. Our results are typically given in the form of asymptotically exact probabilities or zero-one laws specifying critical scalings, and provide key insights into the design and analysis of various real-world networks.

preprint2015arXiv

Short Text Hashing Improved by Integrating Multi-Granularity Topics and Tags

Due to computational and storage efficiencies of compact binary codes, hashing has been widely used for large-scale similarity search. Unfortunately, many existing hashing methods based on observed keyword features are not effective for short texts due to the sparseness and shortness. Recently, some researchers try to utilize latent topics of certain granularity to preserve semantic similarity in hash codes beyond keyword matching. However, topics of certain granularity are not adequate to represent the intrinsic semantic information. In this paper, we present a novel unified approach for short text Hashing using Multi-granularity Topics and Tags, dubbed HMTT. In particular, we propose a selection method to choose the optimal multi-granularity topics depending on the type of dataset, and design two distinct hashing strategies to incorporate multi-granularity topics. We also propose a simple and effective method to exploit tags to enhance the similarity of related texts. We carry out extensive experiments on one short text dataset as well as on one normal text dataset. The results demonstrate that our approach is effective and significantly outperforms baselines on several evaluation metrics.

preprint2015arXiv

Strong Interplay between Stripe Spin Fluctuations, Nematicity and Superconductivity in FeSe

Elucidating the microscopic origin of nematic order in iron-based superconducting materials is important because the interactions that drive nematic order may also mediate the Cooper pairing. Nematic order breaks fourfold rotational symmetry in the iron plane, which is believed to be driven by either orbital or spin degrees of freedom. However, as the nematic phase often develops at a temperature just above or coincides with a stripe magnetic phase transition, experimentally determining the dominant driving force of nematic order is difficult. Here, we use neutron scattering to study structurally the simplest iron-based superconductor FeSe, which displays a nematic (orthorhombic) phase transition at $T_s=90$ K, but does not order antiferromagnetically. Our data reveal substantial stripe spin fluctuations, which are coupled with orthorhombicity and are enhanced abruptly on cooling to below $T_s$. Moreover, a sharp spin resonance develops in the superconducting state, whose energy (~4 meV) is consistent with an electron boson coupling mode revealed by scanning tunneling spectroscopy, thereby suggesting a spin fluctuation-mediated sign-changing pairing symmetry. By normalizing the dynamic susceptibility into absolute units, we show that the magnetic spectral weight in FeSe is comparable to that of the iron arsenides. Our findings support recent theoretical proposals that both nematicity and superconductivity are driven by spin fluctuations.

preprint2015arXiv

Structural and Magnetic Phase Diagram of CrAs and its Relationship with Pressure-induced Superconductivity

Most unconventional superconductors, including cuprates and iron-based superconductors, are derived from chemical doping or application of pressure on their collinearly magnetic-ordered parent compounds[1-5]. The recently discovered pressure-induced superconductor CrAs, as a rare example of a non-collinear helimagnetic superconductor, has therefore generated great interest in understanding microscopic magnetic properties and their interplay with superconductivity [6-8]. Unlike cuprates and iron based superconductors where the magnetic moment direction barely changes upon doping, here we show that CrAs exhibits a spin reorientation from the ab plane to the ac plane, along with an abrupt drop of the magnetic propagation vector at a critical pressure (Pc~0.6 GPa). This magnetic phase transition coincides with the emergence of bulk superconductivity, indicating a direct connection between magnetism and superconductivity. With further increasing pressure, the magnetic order completely disappears near the optimal Tc regime (P~0.94 GPa). Moreover, the Cr magnetic moments between nearest neighbors tend to be aligned antiparallel with increasing pressure toward the optimal superconductivity regime. Our findings suggest that the non-collinear helimagnetic order is strongly coupled to structural and electronic degrees of freedom, and that antiferromagnetic correlations associated with the low magnetic vector phase are crucial for superconductivity.

preprint2015arXiv

Thickness Measurements from Single X-ray Phase-contrast Speckle Projection

We propose a one-shot thickness measurement method for sponge-like structures using a propagation-based X-ray phase-contrast imaging (P-PCI) method. In P-PCI, the air-material interface refracts the incident X-ray. Refracted many times along their paths by such a structure, incident X-rays propagate randomly within a small divergent angle range, resulting in a speckle pattern in the captured image. We found structure thickness and contrast of a phase-contrast projection are directly related in images. This relationship can be described by a natural logarithm equation. Thus, from the one phase-contrast view, depth information can be retrieved from its contrast. Our preliminary biological experiments indicate promise in its application to measurements requiring in vivo and ongoing assessment of lung tumor progression.

preprint2015arXiv

Threshold Functions in Random s-Intersection Graphs

Random $s$-intersection graphs have recently received considerable attention in a wide range of application areas. In such a graph, each vertex is equipped with a set of items in some random manner, and any two vertices establish an undirected edge in between if and only if they have at least $s$ common items. In particular, in a uniform random $s$-intersection graph, each vertex independently selects a fixed number of items uniformly at random from a common item pool, while in a binomial random $s$-intersection graph, each item in some item pool is independently attached to each vertex with the same probability. For binomial/uniform random $s$-intersection graphs, we establish threshold functions for perfect matching containment, Hamilton cycle containment, and $k$-robustness, where $k$-robustness is in the sense of Zhang and Sundaram [IEEE Conf. on Decision & Control '12]. We show that these threshold functions resemble those of classical Erdős-Rényi graphs, where each pair of vertices has an undirected edge independently with the same probability.

preprint2014arXiv

Neutron Scattering Measurements of Spatially Anisotropic Magnetic Exchange Interactions in Semiconducting K0.85Fe1.54Se2 (TN=280 K)

We use neutron scattering to study the spin excitations associated with the stripe antiferromagnetic (AFM) order in semiconducting K$_{0.85}$Fe$_{1.54}$Se$_2$ ($T_N$=$280$ K). We show that the spin wave spectra can be accurately described by an effective Heisenberg Hamiltonian with highly anisotropic in-plane couplings at $T$= $5$ K. At high temperature ($T$= $300$ K) above $T_N$, short range magnetic correlation with anisotropic correlation lengths are observed. Our results suggest that, despite the dramatic difference in the Fermi surface topology, the in-plane anisotropic magnetic couplings are a fundamental property of the iron based compounds; this implies that their antiferromagnetism may originate from local strong correlation effects rather than weak coupling Fermi surface nesting.

preprint2014arXiv

On $k$-connectivity and minimum vertex degree in random $s$-intersection graphs

Random $s$-intersection graphs have recently received much interest in a wide range of application areas. Broadly speaking, a random $s$-intersection graph is constructed by first assigning each vertex a set of items in some random manner, and then putting an undirected edge between all pairs of vertices that share at least $s$ items (the graph is called a random intersection graph when $s=1$). A special case of particular interest is a uniform random $s$-intersection graph, where each vertex independently selects the same number of items uniformly at random from a common item pool. Another important case is a binomial random $s$-intersection graph, where each item from a pool is independently assigned to each vertex with the same probability. Both models have found numerous applications thus far including cryptanalysis, and the modeling of recommender systems, secure sensor networks, online social networks, trust networks and small-world networks (uniform random $s$-intersection graphs), as well as clustering analysis, classification, and the design of integrated circuits (binomial random $s$-intersection graphs). In this paper, for binomial/uniform random $s$-intersection graphs, we present results related to $k$-connectivity and minimum vertex degree. Specifically, we derive the asymptotically exact probabilities and zero-one laws for the following three properties: (i) $k$-vertex-connectivity, (ii) $k$-edge-connectivity and (iii) the property of minimum vertex degree being at least $k$.

preprint2014arXiv

On the strengths of connectivity and robustness in general random intersection graphs

Random intersection graphs have received much attention for nearly two decades, and currently have a wide range of applications ranging from key predistribution in wireless sensor networks to modeling social networks. In this paper, we investigate the strengths of connectivity and robustness in a general random intersection graph model. Specifically, we establish sharp asymptotic zero-one laws for $k$-connectivity and $k$-robustness, as well as the asymptotically exact probability of $k$-connectivity, for any positive integer $k$. The $k$-connectivity property quantifies how resilient is the connectivity of a graph against node or edge failures. On the other hand, $k$-robustness measures the effectiveness of local diffusion strategies (that do not use global graph topology information) in spreading information over the graph in the presence of misbehaving nodes. In addition to presenting the results under the general random intersection graph model, we consider two special cases of the general model, a binomial random intersection graph and a uniform random intersection graph, which both have numerous applications as well. For these two specialized graphs, our results on asymptotically exact probabilities of $k$-connectivity and asymptotic zero-one laws for $k$-robustness are also novel in the literature.

preprint2014arXiv

On Topological Properties of Wireless Sensor Networks under the q-Composite Key Predistribution Scheme with On/Off Channels

The q-composite key predistribution scheme [1] is used prevalently for secure communications in large-scale wireless sensor networks (WSNs). Prior work [2]-[4] explores topological properties of WSNs employing the q-composite scheme for q = 1 with unreliable communication links modeled as independent on/off channels. In this paper, we investigate topological properties related to the node degree in WSNs operating under the q-composite scheme and the on/off channel model. Our results apply to general q and are stronger than those reported for the node degree in prior work even for the case of q being 1. Specifically, we show that the number of nodes with certain degree asymptotically converges in distribution to a Poisson random variable, present the asymptotic probability distribution for the minimum degree of the network, and establish the asymptotically exact probability for the property that the minimum degree is at least an arbitrary value. Numerical experiments confirm the validity of our analytical findings.

preprint2014arXiv

Representations and $T$*-extensions of hom-Jordan-Lie algebras

The purpose of this paper is to study representations and $T$*-extensions of hom-Jordan-Lie algebras. In particular, adjoint representations, trivial representations, deformations and many properties of $T$*-extensions of hom-Jordan-Lie algebras are studied in detail. Derivations and central extensions of hom-Jordan-Lie algebras are also discussed as an application.

preprint2014arXiv

Structuring research methods and data with the Research Object model: genomics workflows as a case study

One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows.

preprint2014arXiv

The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.

preprint2013arXiv

(k,m)-connectivity in Mobile Clustered Wireless Networks

This paper has been withdrawn by the author due to a crucial error in the calculation of Equation (28). We propose a novel concept of $(k,m)$-connectivity in mobile clustered wireless networks, in which there are $n$ mobile cluster members and $n^d$ static cluster heads, where $k,m,d$ are all positive constants and $k\leq m$. $(k,m)$-connectivity signifies that in a time period consisting of $m$ time slots, there exist at least $k$ time slots for each cluster member and in any one of these $k$ time slots the cluster member can directly communicate with at least one cluster head. We investigate the critical transmission range of asymptotic $(k,m)$-connectivity when cluster members move according to random walk or i.i.d. mobility model. Under random walk mobility, we propose two general heterogeneous velocity models in which cluster members may move with different velocities. Under both mobility models, we also define weak and strong parameters conditions, resulting in different accuracies of evaluations on the probability that the network is asymptotically $(k,m)$-connected, denoted as $P(\mathcal {C})$ below for simplicity. For both mobilities, under weak parameters condition, we provide bounds on $P(\mathcal {C})$ and derive the critical transmission range for $(k,m)$-connectivity. For random walk mobility with one kind of velocity model and i.i.d. mobility, under strong parameters condition, we present a precise asymptotic probability distribution of $P(\mathcal {C})$ in terms of the transmission radius. Our results offer fundamental insights and theoretical guidelines on design of large-scale wireless networks.

preprint2013arXiv

Neutron-Diffraction Measurements of an Antiferromagnetic Semiconducting Phase in the Vicinity of the High-Temperature Superconducting State of K$_x$Fe$_{2-y}$Se$_2$

The recently discovered K-Fe-Se high temperature superconductor has caused heated debate regarding the nature of its parent compound. Transport, angle-resolved photoemission spectroscopy, and STM measurements have suggested that its parent compound could be insulating, semiconducting or even metallic [M. H. Fang, H.-D. Wang, C.-H. Dong, Z.-J. Li, C.-M. Feng, J. Chen, and H. Q. Yuan, Europhys. Lett. 94, 27009 (2011); F. Chen et al. Phys. Rev. X 1, 021020 (2011); and W. Li et al.,Phys. Rev. Lett. 109, 057003 (2012)]. Because the magnetic ground states associated with these different phases have not yet been identified and the relationship between magnetism and superconductivity is not fully understood, the real parent compound of this system remains elusive. Here, we report neutron-diffraction experiments that reveal a semiconducting antiferromagnetic (AFM) phase with rhombus iron vacancy order. The magnetic order of the semiconducting phase is the same as the stripe AFM order of the iron pnictide parent compounds. Moreover, while the root5*root5 block AFM phase coexists with superconductivity, the stripe AFM order is suppressed by it. This leads us to conjecture that the new semiconducting magnetic ordered phase is the true parent phase of this superconductor.

preprint2013arXiv

Room-temperature multiferroic hexagonal LuFeO$_3$ films

The crystal and magnetic structures of single-crystalline hexagonal LuFeO$_3$ films have been studied using x-ray, electron and neutron diffraction methods. The polar structure of these films are found to persist up to 1050 K; and the switchability of the polar behavior is observed at room temperature, indicating ferroelectricity. An antiferromagnetic order was shown to occur below 440 K, followed by a spin reorientation resulting in a weak ferromagnetic order below 130 K. This observation of coexisting multiple ferroic orders demonstrates that hexagonal LuFeO$_3$ films are room-temperature multiferroics.

preprint2012arXiv

Discrete Variational Calculus for B-spline Approximated Curves

We study variational problems for curves approximated by B-spline curves. We show that, one can obtain discrete Euler-Lagrange equations, for the data describing the approximated curves. Our main application is to the curve completion problem in 2D and 3D. In this case, the aim is to find various aesthetically pleasing solutions as opposed to a solution of a physical problem. The Lagrangians of interest are invariant under the special Euclidean group action for which B-spline approximated curves are well suited. Smooth Lagrangians with special Euclidean symmetries involve curvature, torsion, and arc length. Expressions in these, in the original coordinates, are highly complex. We show that, by contrast, relatively simple discrete Lagrangians offer excellent results for the curve completion problem. The methods we develop for the discrete curve completion problem are general and can be used to solve other discrete variational problems for B-spline curves.

preprint2011arXiv

Electron-Spin Excitation Coupling in an Electron Doped Copper Oxide Superconductor

High-temperature (high-Tc) superconductivity in the copper oxides arises from electron or hole doping of their antiferromagnetic (AF) insulating parent compounds. The evolution of the AF phase with doping and its spatial coexistence with superconductivity are governed by the nature of charge and spin correlations and provide clues to the mechanism of high-Tc superconductivity. Here we use a combined neutron scattering and scanning tunneling spectroscopy (STS) to study the Tc evolution of electron-doped superconducting Pr0.88LaCe0.12CuO4-delta obtained through the oxygen annealing process. We find that spin excitations detected by neutron scattering have two distinct modes that evolve with Tc in a remarkably similar fashion to the electron tunneling modes in STS. These results demonstrate that antiferromagnetism and superconductivity compete locally and coexist spatially on nanometer length scales, and the dominant electron-boson coupling at low energies originates from the electron-spin excitations.

preprint2011arXiv

Neutron Scattering Studies of spin excitations in hole-doped Ba0.67K0.33Fe2As2 superconductor

We report inelastic neutron scattering experiments on single crystals of superconducting Ba0.67K0.33Fe2As2 (Tc = 38 K). In addition to confirming the resonance previously found in powder samples, we find that spin excitations in the normal state form longitudinally elongated ellipses along the QAFM direction in momentum space, consistent with density functional theory predictions. On cooling below Tc, while the resonance preserves its momentum anisotropy as expected, spin excitations at energies below the resonance become essentially isotropic in the in-plane momentum space and dramatically increase their correlation length. These results suggest that the superconducting gap structures in Ba0.67Ka0.33Fe2As2 are more complicated than those suggested from angle resolved photoemission experiments.

preprint2010arXiv

Electron-doping evolution of the low-energy spin excitations in the iron arsenide BaFe$_{2-x}$Ni$_{x}$As$_{2}$ superconductors

We use elastic and inelastic neutron scattering to systematically investigate the evolution of the low-energy spin excitations of the iron arsenide superconductor BaFe2-xNixAs2 as a function of nickel doping x. In the undoped state, BaFe2As2 exhibits a tetragonal-to-orthorhombic structural phase transition and simultaneously develops a collinear antiferromagnetic (AF) order below TN = 143 K. Upon electron-doping of x = 0.075 to induce bulk superconductivity with Tc = 12.3 K, the AF ordering temperature reduces to TN = 58 K.We show that the appearance of bulk superconductivity in BaFe1.925Ni0.075As2 coincides with a dispersive neutron spin resonance in the spin excitation spectra, and a reduction in the static ordered moment. For optimally doped BaFe1.9Ni0.1As2 (Tc = 20 K) and overdoped BaFe1.85Ni0.15As2 (Tc = 15 K) superconductors, the static AF long-range order is completely suppressed and the spin excitation spectra are dominated by a resonance and spin-gap at lower energies. We determine the electron-doping dependence of the neutron spin resonance and spin gap energies, and demonstrate that the three-dimensional nature of the resonance survives into the overdoped regime. If spin excitations are important for superconductivity, these results would suggest that the three-dimensional character of the electronic superconducting gaps are prevalent throughout the phase diagram, and may be critical for superconductivity in these materials.

preprint2009arXiv

Resonance as a probe of the electron superconducting gap in BaFe1.9Ni0.1As2

The discovery of high-transition temperature (high-Tc) superconductivity near antiferromagnetism in iron arsenides raised the possibility of an unconventional superconducting mechansim1-8. The observation of clear Fermi surfaces and nodeless superconducting gaps by angle resolved photoemission9-12 suggests that electron pairing in these materials may be mediated by quasiparticle excitations between sign reversed hole and electron Fermi pockets5-8. Although the presence of a 'resonance' in the spin excitation spectrum found by inelastic neutron scattering13-17 is consistent with this picture18-20, there has been no direct evidence connecting the resonance to the superconducting gap energy. Here we show that for the optimally electron doped BaFe1.9Ni0.1As2 (Tc =20 K, Fig. 1c) iron arsenide superconductor, application of a magnetic field that suppresses the superconductivity and superconducting gap energy also reduces the intensity and energy of the resonance. These results suggest that the energy of the resonance is proportional to the electron pairing energy, and thus indicate that spin fluctuations are intimately related to the mechanism of superconductivity in iron arsenides.

preprint2009arXiv

Spin Waves and Magnetic Exchange Interactions in CaFe2As2

Antiferromagnetism is relevant to high temperature (high-Tc) superconductivity because copper oxide and iron arsenide high-Tc superconductors arise from electron- or hole-doping of their antiferromagnetic (AF) ordered parent compounds. There are two broad classes of explanation for the phenomenon of antiferromagnetism: in the local moment picture, appropriate for the insulating copper oxides, AF interactions are well described by a Heisenberg Hamiltonian; while in the itinerant model, suitable for metallic chromium, AF order arises from quasiparticle excitations of a nested Fermi surface. There has been contradictory evidence regarding the microscopic origin of the AF order in iron arsenide materials, with some favoring a localized picture while others supporting an itinerant point of view. More importantly, there has not even been agreement about the simplest effective ground state Hamiltonian necessary to describe the AF order. Here we report inelastic neutron scattering mapping of spin-wave excitations in CaFe2As2, a parent compound of the iron arsenide family of superconductors. We find that the spin waves in the entire Brillouin zone can be described by an effective three-dimensional local moment Heisenberg Hamiltonian, but the large in-plane anisotropy cannot. Therefore, magnetism in the parent compounds of iron arsenide superconductors is neither purely local nor purely itinerant; rather it is a complicated mix of the two.

preprint2005arXiv

Effects of R-parity violation in unpolarized top quark decay into polarized W-boson

We calculate one-loop R-parity violating corrections to the top quark decay into a bottom quark and a polarized W-gauge boson. The corrections are presented according to the total corrections, the longitudinal corrections and the transverse corrections, respectively. We compared our results with the ${\cal O}(α_s)$ QCD corrections, the ${\cal O}(α)$ electroweak (EW) and finite width corrections, and also the supersymmetric (SUSY) corrections with R-parity conservation, respectively.

Jun Zhao

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107 published item(s)

DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios

Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules

UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach

A Whittaker category for the Symplectic Lie algebra

An LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks

Anomalous contribution to the nematic electronic states from the structural transition in FeSe revealed by time- and angle-resolved photoemission spectroscopy

Connected Vehicles: A Privacy Analysis

Editorial on Research Topic: High-Tc Superconductivity in Electron-Doped Iron Selenide and Related Compounds

Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning

Ferroelectricity driven by magnetism in quasi-one-dimensional Ba9Fe3Se15

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

Frustrated magnetic interactions in FeSe

Privacy and Robustness in Federated Learning: Attacks and Defenses

Privacy-Preserving Aggregation in Federated Learning: A Survey

Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting

Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography

Smart City Enabled by 5G/6G Networks: An Intelligent Hybrid Random Access Scheme

Unusual band splitting and superconducting gap evolution with sulfur substitution in FeSe

6G Downlink Transmission via Rate Splitting Space Division Multiple Access Based on Grouped Code Index Modulation

A Comprehensive Survey of 6G Wireless Communications

AI-GAN: Attack-Inspired Generation of Adversarial Examples

CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge

Observation of robust edge superconductivity in Fe(Se,Te) under strong magnetic perturbation

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Secrecy Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave System with Low-resolution DAC

Spectrum Sharing for 6G Integrated Satellite-Terrestrial Communication Networks Based on NOMA and Cognitive Radio

Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning

'I Just Want to Hack Myself to Not Get Distracted': Evaluating Design Interventions for Self-Control on Facebook

A novel random access scheme for M2M communication in crowded asynchronous massive MIMO systems

A Stackelberg Game Approach to Resource Allocation for Intelligent Reflecting Surface Aided Communications

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

A Two-Body Synergistic Theory for Adsorption and Desorption Kinetics on Surface

An Analysis of Blockchain Consistency in Asynchronous Networks: Deriving a Neat Bound

Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients

BiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy

Blockchain for Future Smart Grid: A Comprehensive Survey

Blockchain-Based Differential Privacy Cost Management System

Clustering and Power Allocation for UAV-assisted NOMA-VLC Systems: A Swarm Intelligence Approach

Developing a measure of online wellbeing and user trust

Distantly Supervised Relation Extraction in Federated Settings

DPCrowd: Privacy-preserving and Communication-efficient Decentralized Statistical Estimation for Real-time Crowd-sourced Data

Enabling Cross-chain Transactions: A Decentralized Cryptocurrency Exchange Protocol

Event Coreference Resolution via a Multi-loss Neural Network without Using Argument Information

Feature Distillation With Guided Adversarial Contrastive Learning

Human Mobility Trends during the COVID-19 Pandemic in the United States

Intelligent Reflecting Surface Aided Network: Power Control for Physical-Layer Broadcasting

Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach

Intelligent Reflecting Surface Meets Mobile Edge Computing: Enhancing Wireless Communications for Computation Offloading

Intelligent Reflecting Surface-Aided Backscatter Communications

IRS-Assisted Millimeter Wave Communications: Joint Power Allocation and Beamforming Design

Knowledge Guided Metric Learning for Few-Shot Text Classification

Local Differential Privacy and Its Applications: A Comprehensive Survey

MAT: Motion-Aware Multi-Object Tracking

Observed mobility behavior data reveal social distancing inertia

Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA

Poisson Kernel Avoiding Self-Smoothing in Graph Convolutional Networks

Quarantine Fatigue: first-ever decrease in social distancing measures after the COVID-19 outbreak before reopening United States

Reconfigurable Intelligent Surface for MISO Systems with Proportional Rate Constraints

Refinements in Motion and Appearance for Online Multi-Object Tracking

Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems

Towards Distributed Privacy-Preserving Prediction

Neutron Spin Resonance in the Heavily Hole-doped KFe$_{2}$As$_{2}$ Superconductor

PandaX limits on light dark matter with light mediator in the singlet extension of MSSM

Testing electroweak SUSY for muon $g-2$ and dark matter at the LHC and beyond

Self-Taught Convolutional Neural Networks for Short Text Clustering

Hexagonal phase stabilization and magnetic orders of multiferroic Lu$_{1-x}$Sc$_x$FeO$_3$

Higgs exotic decays in general NMSSM with self-interacting dark matter

Magnetic ground state of FeSe

Observation of quasi-two-dimensional Dirac fermions in ZrTe5

Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information

Selective AP-sequence Based Indoor Localization without Site Survey

Solving Resource Recharging Station Location-routing Problem through a Resource-space-time Network Representation

Transition from sign-reversed to sign-preserved Cooper-pairing symmetry in sulfur-doped iron selenide superconductors

$n$-ary Hom-Nambu algebras