arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 793 records · Page 44Linked to original sources

arXiv2021

Cell-Free Massive MIMO for 6G Wireless Communication Networks

The recently commercialized fifth-generation (5G) wireless communication networks achieved many improvements, including air interface enhancement, spectrum expansion, and network intensification by several key technologies, such as massive multiple-input multiple-output (MIMO), millimeter-wave communications, and ultra-dense networking. Despite the deployment of 5G commercial systems, wireless communications is still facing many challenges to enable connected intelligence and a myriad of applications such as industrial Internet-of-things, autonomous systems, brain-computer interfaces, digital twin, tactile Internet, etc. Therefore, it is urgent to start research on the sixth-generation (6G) wireless communication systems. Among the candidate technologies for such systems, cell-free massive MIMO which combines the advantages of distributed systems and massive MIMO is considered as a key solution to enhance the wireless transmission efficiency and becomes the international frontier. In this paper, we present a comprehensive study on cell-free massive MIMO for 6G wireless communication networks, especially from the signal processing perspective. We focus on enabling physical layer technologies for cell-free massive MIMO, such as user association, pilot assignment, transmitter and receiver design, as well as power control and allocation. Furthermore, some current and future research problems are highlighted.

cs.IT↗

arXiv2021

Atomistic View of Homogeneous Nucleation of Water into Polymorphic Ices

Water is one of the most abundant substances on Earth, and ice, i.e., solid water, has more than 18 known phases. Normally ice in nature exists only as Ice Ih, Ice Ic, or a stacking disordered mixture of both. Although many theoretical efforts have been devoted to understanding the thermodynamics of different ice phases at ambient temperature and pressure, there still remains many puzzles. We simulated the reversible transitions between water and different ice phases by performing full atom molecular dynamics simulations. Using the enhanced sampling method MetaITS with the two selected X-ray diffraction peak intensities as collective variables, the ternary phase diagrams of liquid water, ice Ih, ice Ic at multiple were obtained. We also present a simple physical model which successfully explains the thermodynamic stability of ice. Our results agree with experiments and leads to a deeper understanding of the ice nucleation mechanism.

cond-mat.stat-mech↗

arXiv2021

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Machine learning based solutions have been successfully employed for automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen perturbations to the normal inputs. So far, the adversarial examples can only deceive detectors that rely on syntactic features (e.g., requested permissions, API calls, etc), and the perturbations can only be implemented by simply modifying application's manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new attacking method that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK that can successfully deceive the machine learning detectors. We develop an automated tool to generate the adversarial examples without human intervention. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We demonstrate our attack on two state-of-the-art Android malware detection schemes, MaMaDroid and Drebin. Our results show that the malware detection rates decreased from 96% to 0% in MaMaDroid, and from 97% to 0% in Drebin, with just a small number of codes to be inserted into the APK.

cs.CR↗

arXiv2021

Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey

Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so that governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks. Furthermore, countermeasures are proposed towards developing effective protections from three aspects -- detection, disruption, and isolation.

cs.CR↗

arXiv2021

Device-Independent-Quantum-Randomness-Enhanced Zero-Knowledge Proof

Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information. As an efficient variant of ZKP, non-interactive zero-knowledge proof (NIZKP) adopting the Fiat-Shamir heuristic is essential to a wide spectrum of applications, such as federated learning, blockchain and social networks. However, the heuristic is typically built upon the random oracle model making ideal assumptions about hash functions, which does not hold in reality and thus undermines the security of the protocol. Here, we present a quantum resolution to the problem. Instead of resorting to a random oracle model, we implement a quantum randomness service. This service generates random numbers certified by the loophole-free Bell test and delivers them with postquantum cryptography (PQC) authentication. Employing this service, we conceive and implement a NIZKP of the three-colouring problem. By bridging together three prominent research themes, quantum non-locality, PQC and ZKP, we anticipate this work to open a new paradigm of quantum information science.

quant-ph↗

arXiv2021

$k$Folden: $k$-Fold Ensemble for Out-Of-Distribution Detection

Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP). In this work, we propose a simple yet effective framework $k$Folden, which mimics the behaviors of OOD detection during training without the use of any external data. For a task with $k$ training labels, $k$Folden induces $k$ sub-models, each of which is trained on a subset with $k-1$ categories with the left category masked unknown to the sub-model. Exposing an unknown label to the sub-model during training, the model is encouraged to learn to equally attribute the probability to the seen $k-1$ labels for the unknown label, enabling this framework to simultaneously resolve in- and out-distribution examples in a natural way via OOD simulations. Taking text classification as an archetype, we develop benchmarks for OOD detection using existing text classification datasets. By conducting comprehensive comparisons and analyses on the developed benchmarks, we demonstrate the superiority of $k$Folden against current methods in terms of improving OOD detection performances while maintaining improved in-domain classification accuracy. The code and datasets can be found at: \url{https://github.com/ShannonAI/kfolden-ood-detection}.

cs.CL↗

arXiv2021

Robust Ultraviolet to Near-infrared Quantum Emitters in Hexagonal Boron Nitride up to 1100 K

A stable single-photon source working at high temperatures with high brightness and covering full band emission from one host material is critically important for quantum technologies. Here, we find that the certain hBN single-photon emissions (SPEs) can be significantly enhanced by lasers with special wavelengths, which largely broaden the wavelength range of the hBN emitters, down to ultraviolet (357 nm) and up to near-infrared (912 nm). Importantly, these hBN SPEs are still stable even at the temperature up to 1100 Kelvin. The decoupling between single-photon and acoustic phonon is observed at high temperatures. Our work suggests that hBN can be a good host material for generating single-photon sources with ultrabroad wavelength range.

physics.app-ph↗

arXiv2021

RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networks

Deep neural networks (DNNs) have successfully learned useful data representations in various tasks. However, assessing the reliability of these representations remains a challenge. Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty with higher quality, but it is very expensive to train and test. MC-Dropout is another popular method, which is less expensive but lacks the diversity of predictions. To estimate the uncertainty with higher quality in less time, we introduce a ReLU-Based Uncertainty Estimation (RBUE) method. Instead of randomly dropping some neurons of the network as in MC-Dropout or using the randomness of the initial weights of networks as in Deep Ensemble, RBUE adds randomness to the activation function module, making the outputs diverse. Under the method, we propose two strategies, MC-DropReLU and MC-RReLU, to estimate uncertainty. We analyze and compare the output diversity of MC-Dropout and our method from the variance perspective and obtain the relationship between the hyperparameters and predictive diversity in the two methods. Moreover, our method is simple to implement and does not need to modify the existing model. We experimentally validate the RBUE on three widely used datasets, CIFAR10, CIFAR100, and TinyImageNet. The experiments demonstrate that our method has competitive performance but is more favorable in training time and memory requirements.

cs.LG↗

arXiv2021

Practical quantum access network over a 10 Gbit/s Ethernet passive optical network

Quantum key distribution (QKD) provides an information-theoretically secure method to share keys between legitimate users. To achieve large-scale deployment of QKD, it should be easily scalable and cost-effective. The infrastructure construction of quantum access network (QAN) expands network capacity and the integration between QKD and classical optical communications reduces the cost of channel. Here, we present a practical downstream QAN over a 10 Gbit/s Ethernet passive optical network (10G-EPON), which can support up to 64 users. In the full coexistence scheme using the single feeder fiber structure, the co-propagation of QAN and 10G-EPON signals with 9 dB attenuation is achieved over 21 km fiber, and the secure key rate for each of 16 users reaches 1.5 kbps. In the partial coexistence scheme using the dual feeder fiber structure, the combination of QAN and full-power 10G-EPON signals is achieved over 11 km with a network capacity of 64-user. The practical QAN over the 10G-EPON in our work implements an important step towards the achievement of large-scale QKD infrastructure.

quant-ph↗

arXiv2021

Detected the steerability bounds of the generalized Werner states via BackPropagation neural network

We use error BackPropagation (BP) neural network to determine whether an arbitrary two-qubit quantum state is steerable and optimize the steerability bounds of the generalized Werner state. The results show that no matter how we choose the features for the quantum states, we can use the BP neural network to construct several models to realize high-performance quantum steering classifiers compared with the support vector machine (SVM). In addition, we predict the steerability bounds of the generalized Werner states by using the classifiers which are newly constructed by the BP neural network, that is, the predicted steerability bounds are closer to the theoretical bounds. In particular, high-performance classifiers with partial information of the quantum states which we only need to measure in three fixed measurement directions are obtained.

quant-ph↗

arXiv2021

Sk-Unet Model with Fourier Domain for Mitosis Detection

Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem. Swapping the low-frequency spectrum of source and target images is shown effective to alleviate the discrepancy between different scanners. Our Fourier-based segmentation method can achieve F1 with 0.7456 on the preliminary test set.

eess.IV↗

arXiv2021

Gradient zigzag metamaterial beams as broadband vibration isolators for beam-like structures

Phononic crystals (PCs) and metamaterials are artificially structured materials with the unprecedented property of the existence of complete stop-bands. However, the generally narrow width of the stop-bands in such materials severely limits their applicability. Here, a particular kind of gradient zigzag metamaterial beams exclusively with flat bands within a broad frequency range is proposed based upon our previously proposed uniform counterparts. The calculated band structures by the Finite Element Method (FEM) and the Spectral Element Method (SEM) show the distinctly different characteristics of the gradient zigzag metamaterial beams from the conventional PCs and metamaterials, where the band structures are totally filled with flat bands within a broad frequency range without the normal pass bands. These flat bands are caused by the local resonance of certain parts in the gradient zigzag metamaterial beams, and the elastic waves with the frequencies at these flat bands are trapped in the resonant parts and cannot propagate. As a result, our proposed gradient zigzag metamaterial beams can be used as broadband vibration isolators for beam-like structures, which is verified by both numerical simulations and experiments conducted on the 3D-printed samples. This design strategy opens a new avenue for designing and constructing novel broadband vibration isolators.

physics.app-ph↗

arXiv2021

Destabilization of black holes and stars by generalized Proca fields

We demonstrate that black holes and stars in general relativity can be destabilized by perturbations of non-minimally coupled vector fields. Focusing on static and spherically symmetric backgrounds, our analysis shows that black holes with sufficiently small mass and stars with sufficiently high densities are subject to ghost or gradient-type instabilities. This holds for a large class of vector-tensor theories whenever non-minimal couplings contribute to linearized dynamics about a state with vanishing vector field and applies to generalized Proca models that have sparked attention for their potential role in cosmology and astrophysics. The stability criteria translate into bounds of relevance for low-scale theories of dark energy and for ultra-light dark matter scenarios.

gr-qc↗

arXiv2021

Device-independent randomness expansion against quantum side information

The ability to produce random numbers that are unknown to any outside party is crucial for many applications. Device-independent randomness generation does not require trusted devices and therefore provides strong guarantees of the security of the output, but it comes at the price of requiring the violation of a Bell inequality for implementation. A further challenge is to make the bounds in the security proofs tight enough to allow randomness expansion with contemporary technology. Although randomness has been generated in recent experiments, the amount of randomness consumed in doing so has been too high to certify expansion based on existing theory. Here we present an experiment that demonstrates device-independent randomness expansion. By developing a Bell test setup with a single-photon detection efficiency of around $84\%$ and by using a spot-checking protocol, we achieve a net gain of $2.57\times10^8$ certified bits with a soundness error $3.09\times10^{-12}$. The experiment ran for $19.2$ h, which corresponds to an average rate of randomness generation of $13,527$ bits per second. By developing the entropy accumulation theorem, we establish security against quantum adversaries. We anticipate that this work will lead to further improvements that push device-independence towards commercial viability.

quant-ph↗

arXiv2021

Learn to Communicate with Neural Calibration: Scalability and Generalization

The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. Unfortunately, such design cannot be easily and directly applied to future wireless networks, which will be characterized by large-scale ultra-dense networks whose design complexity scales exponentially with the network size. Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models. Recently, deep learning-based approaches have emerged as potential alternatives for designing complex and dynamic wireless systems. However, existing learning-based methods have limited capabilities to scale with the problem size and to generalize with varying network settings. In this paper, we propose a scalable and generalizable neural calibration framework for future wireless system design, where a neural network is adopted to calibrate the input of conventional model-based algorithms. Specifically, the backbone of a traditional time-efficient algorithm is integrated with deep neural networks to achieve a high computational efficiency, while enjoying enhanced performance. The permutation equivariance property, carried out by the topological structure of wireless systems, is furthermore utilized to develop a generalizable neural network architecture. The proposed neural calibration framework is applied to solve challenging resource management problems in massive multiple-input multiple-output (MIMO) systems. Simulation results will show that the proposed neural calibration approach enjoys significantly improved scalability and generalization compared with the existing learning-based methods.

eess.SP↗

arXiv2021

An Open Problem on the Bentness of Mesnager's Functions

Let $n=2m$. In the present paper, we study the binomial Boolean functions of the form $$f_{a,b}(x) = \mathrm{Tr}_1^{n}(a x^{2^m-1 }) +\mathrm{Tr}_1^{2}(bx^{\frac{2^n-1}{3} }), $$ where $m$ is an even positive integer, $a\in \mathbb{F}_{2^n}^*$ and $b\in \mathbb{F}_4^*$. We show that $ f_{a,b}$ is a bent function if the Kloosterman sum $$K_{m}\left(a^{2^m+1}\right)=1+ \sum_{x\in \mathbb{F}_{2^m}^*} (-1)^{\mathrm{Tr}_1^{m}(a^{2^m+1} x+ \frac{1}{x})}$$ equals $4$, thus settling an open problem of Mesnager. The proof employs tools including computing Walsh coefficients of Boolean functions via multiplicative characters, divisibility properties of Gauss sums, and graph theory.

cs.IT↗

arXiv2021

An accurate moving wall boundary algorithm for Direct Simulation of Monte Carlo in unsteady rarefied flow

An accurate algorithm is proposed to improve the prediction of a particle in collision with a moving wall within the direct simulation Monte Carlo (DSMC) framework for the simulation of unsteady rarefied flows. This algorithm is able to predict the particle-wall collision in a coupled manner by removing the assumption employed by the approximate algorithm, in that the wall is frozen during the collision. The trajectory equation of the particle is theoretically constructed in a moving object coordinate system. It can accurately describe the geometries of the collision between a particle and an arbitrarily shaped object of which the motion incorporates both translation and rotation, thus allowing to deal with complex problems. In contrast, the approximate algorithm ignores the effect of the moving wall on the particle movement during the collision, and therefore induces error that is an increasing function of the wall velocity. Four rarefied flow problems are applied to validate the accurate algorithm. It is shown that the algorithm can produce results perfectly consistent with the Maxwellian theoretical solutions and ensure particle conservation to avoid gas leakage. It is also shown in a three-dimensional case of a re-entry module that the steady simulation fails to reproduce the hysteresis effect while the unsteady simulation using the accurate algorithm can do that, indicating that the unsteady simulation with an appropriate algorithm as proposed in the present work is essentially required in such applications.

physics.comp-ph↗

arXiv2021

First Constraints on Nuclear Coupling of Axionlike Particles from the Binary Neutron Star Gravitational Wave Event GW170817

Light axion fields, if they exist, can be sourced by neutron stars due to their coupling to nuclear matter, and play a role in binary neutron star mergers. We report on a search for such axions by analysing the gravitational waves from the binary neutron star inspiral GW170817. We find no evidence of axions in the sampled parameter space. The null result allows us to impose constraints on axions with masses below $10^{-11} {\rm eV}$ by excluding the ones with decay constants ranging from $1.6\times10^{16} {\rm GeV}$ to $10^{18} {\rm GeV}$ at $3σ$ confidence level. Our analysis provides the first constraints on axions from neutron star inspirals, and rules out a large region in parameter space that has not been probed by the existing experiments.

hep-ph↗