arXiv subjects

Jian Ren

Publications and source records attributed to Jian Ren.

At least 91 records · Page 5Linked to original sources

arXiv2020

Neural Hair Rendering

In this paper, we propose a generic neural-based hair rendering pipeline that can synthesize photo-realistic images from virtual 3D hair models. Unlike existing supervised translation methods that require model-level similarity to preserve consistent structure representation for both real images and fake renderings, our method adopts an unsupervised solution to work on arbitrary hair models. The key component of our method is a shared latent space to encode appearance-invariant structure information of both domains, which generates realistic renderings conditioned by extra appearance inputs. This is achieved by domain-specific pre-disentangled structure representation, partially shared domain encoder layers and a structure discriminator. We also propose a simple yet effective temporal conditioning method to enforce consistency for video sequence generation. We demonstrate the superiority of our method by testing it on a large number of portraits and comparing it with alternative baselines and state-of-the-art unsupervised image translation methods.

cs.CV↗

arXiv2020

Understanding the Discrepancy between IRX and Balmer Decrement in Tracing Galaxy Dust Attenuation

We compare the infrared excess (IRX) and Balmer decrement (${\rm Hα/Hβ}$) as dust attenuation indicators in relation to other galaxy parameters using a sample of $\sim$32 000 local star-forming galaxies (SFGs) carefully selected from SDSS, GALEX and WISE. While at fixed ${\rm Hα/Hβ}$, IRX turns out to be independent on galaxy stellar mass, the Balmer decrement does show a strong mass dependence at fixed IRX. We find the discrepancy, parameterized by the color excess ratio $R_{\rm EBV} \equiv E(B-V)_{\rm IRX}/E(B-V)_{\rm Hα/Hβ}$, is not dependent on the gas-phase metallicity and axial ratio but on the specific star formation rate (SSFR) and galaxy size ($R_{\rm e}$) following $R_{\rm EBV}=0.79+0.15\log({\rm SSFR}/R_{\rm e}^{2})$. This finding reveals that the nebular attenuation as probed by the Balmer decrement becomes increasingly larger than the global (stellar) attenuation of SFGs with decreasing SSFR surface density. This can be understood in the context of an enhanced fraction of intermediate-age stellar populations that are less attenuated by dust than the HII region-traced young population, in conjunction with a decreasing dust opacity of the diffuse ISM when spreading over a larger spatial extent. Once the SSFR surface density of an SFG is known, the conversion between attenuation of nebular and stellar emission can be well estimated using our scaling relation.

astro-ph.GA↗

arXiv2020

Human Motion Transfer from Poses in the Wild

In this paper, we tackle the problem of human motion transfer, where we synthesize novel motion video for a target person that imitates the movement from a reference video. It is a video-to-video translation task in which the estimated poses are used to bridge two domains. Despite substantial progress on the topic, there exist several problems with the previous methods. First, there is a domain gap between training and testing pose sequences--the model is tested on poses it has not seen during training, such as difficult dancing moves. Furthermore, pose detection errors are inevitable, making the job of the generator harder. Finally, generating realistic pixels from sparse poses is challenging in a single step. To address these challenges, we introduce a novel pose-to-video translation framework for generating high-quality videos that are temporally coherent even for in-the-wild pose sequences unseen during training. We propose a pose augmentation method to minimize the training-test gap, a unified paired and unpaired learning strategy to improve the robustness to detection errors, and two-stage network architecture to achieve superior texture quality. To further boost research on the topic, we build two human motion datasets. Finally, we show the superiority of our approach over the state-of-the-art studies through extensive experiments and evaluations on different datasets.

cs.CV↗

arXiv2019

$d$-MABE: Distributed Multilevel Attribute-Based EMR Management and Applications

Current systems used by medical institutions for the management and transfer of Electronic Medical Records (EMR) can be vulnerable to security and privacy threats. In addition, these centralized systems often lack interoperability and give patients limited or no access to their own EMRs. In this paper, we propose a novel distributed data sharing scheme that applies the security benefits of blockchain to handle these concerns. With blockchain, we incorporate smart contracts and a distributed storage system to alleviate the dependence on the record-generating institutions to manage and share patient records. To preserve privacy of patient records, we implement our smart contracts as a method to allow patients to verify attributes prior to granting access rights. Our proposed scheme also facilitates selective sharing of medical records among staff members that belong to different levels of a hierarchical institution. We provide extensive security, privacy, and evaluation analyses to show that our proposed scheme is both efficient and practical.

cs.CR↗

arXiv2019

Bitcoin and Blockchain: Security and Privacy

A cryptocurrency is a decentralized digital currency that is designed for secure and private asset transfer and storage. As a currency, it should be difficult to counterfeit and double-spend. In this paper, we review and analyze the major security and privacy issues of Bitcoin. In particular, we focus on its underlying foundation, blockchain technology. First, we present a comprehensive background of Bitcoin and the preliminary on security. Second, the major security threats and countermeasures of Bitcoin are investigated. We analyze the risk of double-spending attacks, evaluate the probability of success in performing the attacks and derive the profitability for the attacker to perform such attacks. Third, we analyze the underlying Bitcoin peer-to-peer network security risks and Bitcoin storage security. We compare three types of Bitcoin wallets in terms of security, type of services and their trade-offs. Finally, we discuss the security and privacy features of alternative cryptocurrencies and present an overview of emerging technologies today. Our results can help Bitcoin users to determine a trade-off between the risk of double-spending attempts and the transaction time delay or confidence before accepting transactions. These results can also assist miners to develop suitable strategies to get involved in the mining process and maximize their profits.

cs.CR↗

arXiv2019

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch

Designing the structure of neural networks is considered one of the most challenging tasks in deep learning, especially when there is few prior knowledge about the task domain. In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain. Specifically, we first use primary succession to rapidly evolve a population of poorly initialized neural network structures into a more diverse population, followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession. Extinction is applied in both stages to reduce computational cost. Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find better network structures. Experimental results show that our proposed approach can achieve similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost. For example, the network discovered by our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].

cs.NE↗

arXiv2019

Secure OFDM System Design and Capacity Analysis under Disguised Jamming

In this paper, we propose a securely precoded OFDM (SP-OFDM) system for efficient and reliable transmission under disguised jamming, where the jammer intentionally misleads the receiver by mimicking the characteristics of the authorized signal, and causes complete communication failure. More specifically, we bring off a dynamic constellation by introducing secure shared randomness between the legitimate transmitter and receiver, and hence break the symmetricity between the authorized signal and the disguised jamming. We analyze the channel capacities of both the traditional OFDM and SP-OFDM under hostile jamming using the arbitrarily varying channel (AVC) model. It is shown that the deterministic coding capacity of the traditional OFDM is zero under the worst disguised jamming. On the other hand, due to the secure randomness shared between the authorized transmitter and receiver, SP-OFDM can achieve a positive capacity under disguised jamming since the AVC channel corresponding to SP-OFDM is not symmetrizable. A remarkable feature of the proposed SP-OFDM scheme is that while achieving strong jamming resistance, it has roughly the same high spectral efficiency as the traditional OFDM system. The robustness of the proposed SP-OFDM scheme under disguised jamming is demonstrated through both theoretic and numerical analyses.

eess.SP↗

arXiv2019

A Universal Relation of Dust Obscuration Across Cosmic Time

We investigate dust obscuration as parameterised by the infrared excess IRX$\equiv$$L_{\rm IR}/L_{\rm UV}$ in relation to global galaxy properties, using a sample of $\sim$32$\,$000 local star-forming galaxies (SFGs) selected from SDSS, GALEX and WISE. We show that IRX generally correlates with stellar mass ($M_\ast$), star formation rate (SFR), gas-phase metallicity ($Z$), infrared luminosity ($L_{\rm IR}$) and the half-light radius ($R_{\rm e}$). A weak correlation of IRX with axial ratio (b/a) is driven by the inclination and thus seen as a projection effect. By examining the tightness and the scatter of these correlations, we find that SFGs obey an empirical relation of the form $IRX$=$10^α\,(L_{\rm IR})^β\,R_{\rm e}^{-γ}\,(b/a)^{-δ}$ where the power-law indices all increase with metallicity. The best-fitting relation yields a scatter of $\sim$0.17$\,$dex and no dependence on stellar mass. Moreover, this empirical relation also holds for distant SFGs out to $z=3$ in a population-averaged sense, suggesting it to be universal over cosmic time. Our findings reveal that IRX approximately increases with $L_{\rm IR}/R_{\rm e}^{[1.3 - 1.5]}$ instead of $L_{\rm IR}/R_{\rm e}^{2}$ (i.e., surface density). We speculate this may be due to differences in the spatial extent of stars versus star formation and/or complex star-dust geometries. We conclude that not stellar mass but IR luminosity, metallicity and galaxy size are the key parameters jointly determining dust obscuration in SFGs.

astro-ph.GA↗

arXiv2018

Security and Privacy Enhancement for Outsourced Biometric Identification

A lot of research has been focused on secure outsourcing of biometric identification in the context of cloud computing. In such schemes, both the encrypted biometric database and the identification process are outsourced to the cloud. The ultimate goal is to protect the security and privacy of the biometric database and the query templates. Security analysis shows that previous schemes suffer from the enrolment attack and unnecessarily expose more information than needed. In this paper, we propose a new secure outsourcing scheme aims at enhancing the security from these two aspects. First, besides all the attacks discussed in previous schemes, our proposed scheme is also secure against the enrolment attack. Second, we model the identification process as a fixed radius similarity query problem instead of the kNN search problem. Such a modelling is able to reduce the exposed information thus enhancing the privacy of the biometric database. Our comprehensive security and complexity analysis show that our scheme is able to enhance the security and privacy of the biometric database and query templates while maintaining the same computational savings from outsourcing.

cs.CR↗

arXiv2018

Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images

Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain without requiring labeling of images at the target domain. The adaptation is achieved through adversarial training to find an invariant feature space along with the proposed Siamese architecture on the target domain to add a regularization that is appropriate for the whole-slide images. We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.

cs.CV↗

arXiv2018

Factorized Adversarial Networks for Unsupervised Domain Adaptation

In this paper, we propose Factorized Adversarial Networks (FAN) to solve unsupervised domain adaptation problems for image classification tasks. Our networks map the data distribution into a latent feature space, which is factorized into a domain-specific subspace that contains domain-specific characteristics and a task-specific subspace that retains category information, for both source and target domains, respectively. Unsupervised domain adaptation is achieved by adversarial training to minimize the discrepancy between the distributions of two task-specific subspaces from source and target domains. We demonstrate that the proposed approach outperforms state-of-the-art methods on multiple benchmark datasets used in the literature for unsupervised domain adaptation. Furthermore, we collect two real-world tagging datasets that are much larger than existing benchmark datasets, and get significant improvement upon baselines, proving the practical value of our approach.

cs.CV↗

arXiv2018

Cylindrical-water-resonator-based ultra-broadband microwave absorber

A cylindrical-water-resonator-based absorber with ultra-broad operating band at microwave band is demonstrated theoretically and experimentally for the first time. By utilizing dielectric resonator (DR) mode and spoof surface plasmon polariton (SPP) mode of the cylindrical water resonators, the proposed absorber owns an absorptivity higher than 90% over almost the whole ultra-broad operating band from 5.58 GHz to 24.21 GHz, with a relative bandwidth as high as 125%. The angular tolerance and thermal stability of the proposed absorber are simulated and the results indicate the good performance of the absorber under wide incident angles and weakly dependent on water temperature. Low cost, ultra-broad operating band, good wide-angle characteristic and thermal stability make the absorber promising in the application of antenna measurement, steady technology and energy harvesting.

physics.app-ph↗

arXiv2018

P-MOD: Secure Privilege-Based Multilevel Organizational Data-Sharing in Cloud Computing

Cloud computing has changed the way enterprises store, access and share data. Data is constantly being uploaded to the cloud and shared within an organization built on a hierarchy of many different individuals that are given certain data access privileges. With more data storage needs turning over to the cloud, finding a secure and efficient data access structure has become a major research issue. With different access privileges, individuals with more privileges (at higher levels of the hierarchy) are granted access to more sensitive data than those with fewer privileges (at lower levels of the hierarchy). In this paper, a Privilege-based Multilevel Organizational Data-sharing scheme~(P-MOD) is proposed that incorporates a privilege-based access structure into an attribute-based encryption mechanism to handle these concerns. Each level of the privilege-based access structure is affiliated with an access policy that is uniquely defined by specific attributes. Data is then encrypted under each access policy at every level to grant access to specific data users based on their data access privileges. An individual ranked at a certain level can decrypt the ciphertext (at that specific level) if and only if that individual owns a correct set of attributes that can satisfy the access policy of that level. The user may also decrypt the ciphertexts at the lower levels with respect to the user's level. Security analysis shows that P-MOD is secure against adaptively chosen plaintext attack assuming the DBDH assumption holds.The comprehensive performance analysis demonstrates that P-MOD is more efficient in computational complexity and storage space than the existing schemes in secure data sharing within an organization.

cs.CR↗

arXiv2017

PassBio: Privacy-Preserving User-Centric Biometric Authentication

The proliferation of online biometric authentication has necessitated security requirements of biometric templates. The existing secure biometric authentication schemes feature a server-centric model, where a service provider maintains a biometric database and is fully responsible for the security of the templates. The end-users have to fully trust the server in storing, processing and managing their private templates. As a result, the end-users' templates could be compromised by outside attackers or even the service provider itself. In this paper, we propose a user-centric biometric authentication scheme (PassBio) that enables end-users to encrypt their own templates with our proposed light-weighted encryption scheme. During authentication, all the templates remain encrypted such that the server will never see them directly. However, the server is able to determine whether the distance of two encrypted templates is within a pre-defined threshold. Our security analysis shows that no critical information of the templates can be revealed under both passive and active attacks. PassBio follows a "compute-then-compare" computational model over encrypted data. More specifically, our proposed Threshold Predicate Encryption (TPE) scheme can encrypt two vectors x and y in such a manner that the inner product of x and y can be evaluated and compared to a pre-defined threshold. TPE guarantees that only the comparison result is revealed and no key information about x and y can be learned. Furthermore, we show that TPE can be utilized as a flexible building block to evaluate different distance metrics such as Hamming distance and Euclidean distance over encrypted data. Such a compute-then-compare computational model, enabled by TPE, can be widely applied in many interesting applications such as searching over encrypted data while ensuring data security and privacy.

cs.CR↗

arXiv2017

Discrete Dynamic Causal Modeling and Its Relationship with Directed Information

This paper explores the discrete Dynamic Causal Modeling (DDCM) and its relationship with Directed Information (DI). We prove the conditional equivalence between DDCM and DI in characterizing the causal relationship between two brain regions. The theoretical results are demonstrated using fMRI data obtained under both resting state and stimulus based state. Our numerical analysis is consistent with that reported in previous study.

q-bio.NC↗

arXiv2017

Generation of High-Purity Millimeter-Wave Orbital Angular Momentum Modes Using Horn Antenna: Theory and Implementation

Twisted electromagnetic waves, of which the helical phase front is called orbital angular momentum (OAM), have been recently explored for quantum information, high speed communication and radar detections. In this context, generation of high purity waves carrying OAM is of great significance and challenge from low frequency band to optical area. Here, a novel strategy of mode combination method is proposed to generate twisted waves with arbitrary order of OAM index. The higher order mode of a circular horn antenna is used to generate the twisted waves with quite high purity. The proposed strategy is verified with theoretical analysis, numerical simulation and experiments. A circular horn antenna operating at millimeter wave band is designed, fabricated, and measured. Two twisted waves with OAM index of l=+1 and l=-1 with a mode purity as high as 87% are obtained. Compared with the other OAM antennas, the antenna proposed here owns a high antenna gain (over 12 dBi) and wide operating bandwidth (over 15%). The high mode purity, high antenna gain and wide operating band make the antenna suitable for the twisted-wave applications, not only in the microwave and millimeter wave band, but also in the terahertz band.

physics.class-ph↗

arXiv2017

Microwave Vortex-Beam Emitter Based on Spoof Surface Plasmon Polaritons

Since the orbital angular momentum (OAM) being investigated intensively in the optical region, there are growing interests in employing OAM to solve the problem in wireless communications as a new method. It is found that the independence between different OAM modes is crucial to wireless communications. Motivated by the tremendous potential of OAM in communication systems, we propose a novel method to generate vortex beams by spoof surface plasmon polaritons (SPPs). A looped double-layer spoof SPP waveguide is applied to realize the transmission of electromagnetic waves. Beam emitting is accomplished through a series of circular patches, whose role is not only the radiation units but also resonators giving rise to the phase shifts required by the vortex beam. The proposed method is validated by both numerical calculation and experiment. The measured results show that the spoof SPPs are radiated by the circular patches and the vortex beam carrying different OAM modes are observed at different frequencies. This is the first time to generate the OAM modes by spoof SPPs. The proposed method possesses smaller size and is much easier to be integrated into integrated circuits. The simple structure and design procedure make the proposed method promising in future wireless communication systems.

physics.class-ph↗

arXiv2017

Performance Optimization of Network Coding Based Communication and Reliable Storage in Internet of Things

Internet or things (IoT) is changing our daily life rapidly. Although new technologies are emerging everyday and expanding their influence in this rapidly growing area, many classic theories can still find their places. In this paper, we study the important applications of the classic network coding theory in two important components of Internet of things, including the IoT core network, where data is sensed and transmitted, and the distributed cloud storage, where the data generated by the IoT core network is stored. First we propose an adaptive network coding (ANC) scheme in the IoT core network to improve the transmission efficiency. We demonstrate the efficacy of the scheme and the performance advantage over existing schemes through simulations. %Next we study the application of network coding in the distributed cloud storage. Next we introduce the optimal storage allocation problem in the network coding based distributed cloud storage, which aims at searching for the most reliable allocation that distributes the $n$ data components into $N$ data centers, given the failure probability $p$ of each data center. Then we propose a polynomial-time optimal storage allocation (OSA) scheme to solve the problem. Both the theoretical analysis and the simulation results show that the storage reliability could be greatly improved by the OSA scheme.

cs.NI↗