arXiv subjects

Zhi Li

Publications and source records attributed to Zhi Li.

At least 325 records · Page 18Linked to original sources

arXiv2019

Blockchain-Based Cloud Manufacturing: Decentralization

Recently, there has been growing interest in the field of cloud manufacturing (CM) amongst researchers in the manufacturing community. Cloud manufacturing is a customer-driven manufacturing model that was inspired by cloud computing, and its major objective was to provide ubiquitous on-demand access to services. However, the current CM architecture suffers from problems that are associated with a centralized based industrial network framework and third part operation. In a nutshell, centralized networking has had issues with flexibility, efficiency, availability, and security. Therefore, this paper aims to tackle these problems by introducing an ongoing project to a decentralized network architecture for cloud manufacturing which is based on the blockchain technology. In essence, this research paper introduces the blockchain technology as a decentralized peer to peer network for multiple cloud manufacturing providers.

cs.DC↗

arXiv2018

Quantum Oscillations in iron-doped topological insulator Sb2Te3 crystals

We investigated the magnetotransport properties of Fe-doped topological insulator Sb1.96 Fe0.04Te3 single crystals. With doping, the band structure changes significantly and multiple Fermi pockets become evident in the Shubnikov-de Haas oscillations, in contrast to the single frequency detected for pure Sb2Te3. Using complementary density functional theory calculations, we identify an additional bulk hole pocket introduced at the Γ point which originates from the chemical distortion associated with the Fe-dopant. Experimentally, both doped and undoped samples are hole-carrier dominated, however, Fe doping also reduces the carrier density and mobility. The angle dependent quantum oscillations were analyzed to characterize the complex Fermi surface and isolate the dimensionality of each SdH feature. Among the components, at least two pockets possess 2D-like behavior according to their rotational dependence. These results indicate a complex interplay of hybridized dopant bands with the bulk and surface topological electronic structure.

cond-mat.mtrl-sci↗

arXiv2018

Topological metal bands with double-triple-point fermions in optical lattices

Novel fermionic quasiparticles with integer pseudospins in some energy bands, such as pseudospin-1 triple-point fermions, recently attract increasing interest since they are beyond the conventional spin-$1/2$ Dirac and Weyl counterparts. In this paper, we propose a class of pseudospin-1 fermioic excitations emerging in topological metal bands, dubbed double-triple-point (DTP) fermions. We first present a general three-band continuum model with $C_4$ symmetry in three dimensions, which has three types of threefold degenerate points in the bands classified by their topological charges $C=\pm4,\pm2,0$, respectively. They are dubbed DTPs as spin-1 generalization of double-Weyl points. We then construct two-dimensional and three-dimensional tight-binding lattice models of topological metal bands with exotic DTP fermions near the DTPs. In two dimensions, the band gaps close at a trivial DTP with zero Berry phase, which occurs at the transition between the normal and topological insulator phases. In three dimensions, the topological properties of three different DTP fermions in lattice systems are further investigated, and the effects of breaking $C_4$ symmetry are also studied, which generally leads to splitting each quadratic DTP into two linear triple points and gives topological phase diagrams. Using ultracold fermionic atoms in optical lattices, the proposed models can be realized and the topological properties of the DTP fermions can be detected.

cond-mat.quant-gas↗

arXiv2018

Interfacial friction-induced electronic excitation mechanism for tribo-tunneling current generation

Direct-current (d.c.) electricity generation using moving Schottky contact is emerging as a new strategy for mechanical energy conversion. Here, we demonstrate the generation of d.c.tunneling current with a density of ~35 A/m2 at a metal-insulator-semiconductor (MIS) sliding system using micro-tips. The measured current densities were found to be three to four orders of magnitude higher than that observed with the conventional polymer-based triboelectric nanogenerators (TENGs). The electromotive force for the tribo-tunneling transport comes from the dynamic electronic excitation at the frictional interface rather than from the electrostatically trapped surface charges as in the case of conventional TENGs. The strong electronic excitation can give rise to a non-equilibrium interfacial charge variation. Depending on the energy distribution of the excited electrons/holes, the charge variation subsequently dissipates non-adiabatically into tunneling current and trapped surface charges, or adiabatically into heat. These fundamental results not only enhance our understanding of the triboelectric phenomenon, but also open up new paths for the development of next-generation mechanical energy harvesting and sensing techniques.

physics.app-ph↗

arXiv2018

Towards Perceptually Optimized End-to-end Adaptive Video Streaming

Measuring Quality of Experience (QoE) and integrating these measurements into video streaming algorithms is a multi-faceted problem that fundamentally requires the design of comprehensive subjective QoE databases and metrics. To achieve this goal, we have recently designed the LIVE-NFLX-II database, a highly-realistic database which contains subjective QoE responses to various design dimensions, such as bitrate adaptation algorithms, network conditions and video content. Our database builds on recent advancements in content-adaptive encoding and incorporates actual network traces to capture realistic network variations on the client device. Using our database, we study the effects of multiple streaming dimensions on user experience and evaluate video quality and quality of experience models. We believe that the tools introduced here will help inspire further progress on the development of perceptually-optimized client adaptation and video streaming strategies. The database is publicly available at http://live.ece.utexas.edu/research/LIVE_NFLX_II/live_nflx_plus.html.

eess.IV↗

arXiv2018

Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers' long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.

cs.IR↗

arXiv2018

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be used to develop more robust machine learning models and to provide standardized benchmarks of models' performance in the adversarial setting. Benchmarks constructed without a standardized implementation of adversarial example construction are not comparable to each other, because a good result may indicate a robust model or it may merely indicate a weak implementation of the adversarial example construction procedure. This technical report is structured as follows. Section 1 provides an overview of adversarial examples in machine learning and of the CleverHans software. Section 2 presents the core functionalities of the library: namely the attacks based on adversarial examples and defenses to improve the robustness of machine learning models to these attacks. Section 3 describes how to report benchmark results using the library. Section 4 describes the versioning system.

cs.LG↗

arXiv2018

Noise constrained least mean absolute third algorithm

The learning speed of an adaptive algorithm can be improved by properly constraining the cost function of the adaptive algorithm. Besides, the stabilization of the NCLMF algorithm is more complicated, whose stability depends solely on the input power of the adaptive filter and the NCLMF algorithm with unbounded repressors is not mean square stability even for a small value of the step-size. So, in this paper, a noise variance constrained least mean absolute third (LMAT) algorithm is investigated. The noise constrained LMAT (NCLMAT) algorithm is obtained by constraining the cost function of the standard LMAT algorithm to the third-order moment of the additive noise. And it can eliminate a variety of non-Gaussian distribution of noise, such as Rayleigh noise, Binary noise and so on. The NCLMAT algorithm is a type of variable step-size LMAT algorithm where the step-size rule arises naturally from the constraints. The main aim of this work is first time to derive the NCLMAT adaptive algorithm, analyze its convergence behavior, mean square error (MSE), mean-square deviation (MSD) and assess its performance in different noise environments. Finally, the experimental results in system identification applications presented here illustrate the principle and efficiency of the NCLMAT algorithm.

eess.SY↗

arXiv2018

Convex Combination of Overlap-Save Frequency-Domain Adaptive Filters

In order to decrease the steady-state error and reduce the computational complexity and increase the ability to identify a large unknown system, a convex combination of overlap-save frequency-domain adaptive filters (COSFDAF) algorithm is proposed. From the articles available, most papers discuss convex combinations of adaptive-filter algorithms focusing on the time domain. Those algorithms show better performances in convergence speed and steady-state error. The major defect of those algorithms, however, is the computational complexity. To deal with this problem and motivated by frequency-domain adaptive filters (FDAF) and convex optimization, this paper gives an adaptive filter algorithm, that consists of combining the two FDAFs using the convex combination principles and derives a formula to update the mixing parameter. The computational complexity of the COSFDAF is analyzed theoretically. The simulation results show that no matter what kinds of signal to be processed, whether correlated (i.e. colored noise) or uncorrelated (i.e. white noise), the proposed algorithm has better performance in identify the unknown coefficients when compared to a single overlap-save FDAF or the convex combination of two time-domain adaptive filters.

eess.SY↗

arXiv2018

SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment

Perceptual video quality assessment models are either frame-based or video-based, i.e., they apply spatiotemporal filtering or motion estimation to capture temporal video distortions. Despite their good performance on video quality databases, video-based approaches are time-consuming and harder to efficiently deploy. To balance between high performance and computational efficiency, Netflix developed the Video Multi-method Assessment Fusion (VMAF) framework, which integrates multiple quality-aware features to predict video quality. Nevertheless, this fusion framework does not fully exploit temporal video quality measurements which are relevant to temporal video distortions. To this end, we propose two improvements to the VMAF framework: SpatioTemporal VMAF and Ensemble VMAF. Both algorithms exploit efficient temporal video features which are fed into a single or multiple regression models. To train our models, we designed a large subjective database and evaluated the proposed models against state-of-the-art approaches. The compared algorithms will be made available as part of the open source package in https://github.com/Netflix/vmaf.

eess.IV↗

arXiv2018

Skyrmions in magnetic tunnel junctions

In this work, we demonstrate that skyrmions can be nucleated in the free layer of a magnetic tunnel junction (MTJ) with Dzyaloshinskii-Moriya interactions (DMI) by a spin-polarized current with the assistance of stray fields from the pinned layer. The size, stability and number of created skyrmions can be tuned by either the DMI strength or the stray field distribution. The interaction between the stray field and the DMI effective field is discussed. A device with multi-level tunneling magnetoresistance is proposed, which could pave the ways for skyrmion-MTJ-based multi-bit storage and artificial neural network computation. Our results may facilitate the efficient nucleation and electrical detection of skyrmions.

cond-mat.mes-hall↗

arXiv2017

Rapid formation of black holes in galaxies: a self-limiting growth mechanism

We present high-quality fluid dynamical simulations of isothermal gas flows in a rotating barred potential. We show that a large quantity of gas is driven right into the nucleus of a model galaxy when the potential lacks a central mass concentration, but the inflow stalls at a nuclear ring in comparison simulations that include a central massive object. The radius of the nuclear gas ring increases linearly with the mass of the central object. We argue that bars drive gas right into the nucleus in the early stages of disk galaxy formation, where a nuclear star cluster and perhaps a massive black hole could be created. The process is self-limiting, however, because inflow stalls at a nuclear ring once the mass of gas and stars in the nucleus exceeds ~1% of the disk mass, which shuts off rapid growth of the black hole. We briefly discuss the relevance of these results to the seeding of massive black holes in galaxies, the merger model for quasar evolution, and the existence of massive black holes in disk galaxies that lack a significant classical bulge.

astro-ph.GA↗

arXiv2017

Realization of Flat Band with Possible Nontrivial Topology in Electronic Kagome Lattice

The energy dispersion of fermions or bosons vanishes in momentum space if destructive quantum interference occurs in a frustrated Kagome lattice with only nearest-neighbour (NN) hopping. A discrete flat band (FB) without any dispersion is consequently formed, promising emergence of fractional quantum Hall states (FQHS) at high temperatures. Here, we report experimental realization of a FB with possible nontrivial topology in an electronic Kagome lattice on a twisted multilayer silicene. The electrons are localized in the Kagome lattice due to quantum destructive interference, and thus, their kinetic energy is quenched, which gives rise to a FB peak in density of states. A robust and pronounced one-dimensional edge state has been revealed at Kagome edge, which resides at higher energy than the FB. Our observations of the FB and the exotic edge state in electronic Kagome lattice open up the possibility towards the realization of fractional Chern insulators in two-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2017

Recover Subjective Quality Scores from Noisy Measurements

Simple quality metrics such as PSNR are known to not correlate well with subjective quality when tested across a wide spectrum of video content or quality regime. Recently, efforts have been made in designing objective quality metrics trained on subjective data (e.g. VMAF), demonstrating better correlation with video quality perceived by human. Clearly, the accuracy of such a metric heavily depends on the quality of the subjective data that it is trained on. In this paper, we propose a new approach to recover subjective quality scores from noisy raw measurements, using maximum likelihood estimation, by jointly estimating the subjective quality of impaired videos, the bias and consistency of test subjects, and the ambiguity of video contents all together. We also derive closed-from expression for the confidence interval of each estimate. Compared to previous methods which partially exploit the subjective information, our approach is able to exploit the information in full, yielding tighter confidence interval and better handling of outliers without the need for z-scoring or subject rejection. It also handles missing data more gracefully. Finally, as side information, it provides interesting insights on the test subjects and video contents.

cs.MM↗

arXiv2017

Exploring topological double-Weyl semimetals with cold atoms in optical lattices

We explore the topological properties of double-Weyl semimetals with cold atoms in optical lattices. We first propose to realize a tight-binding model of simulating the double-Weyl semimetal with a pair of double-Weyl points by engineering the atomic hopping in a three-dimensional optical lattice. We show that the double-Weyl points with topological charges of \$pm2$ behave as sink and source of Berry flux in momentum space connecting by two Fermi arcs and they are stabilized by the \$C_{4h}$ point-group symmetry. By applying a realizable \$C_4$ breaking term, we find that each double-Weyl point splits into two single-Weyl points and obtain rich phase diagrams in the parameter space spanned by the strengths of an effective Zeeman term and the \$C_4$ breaking term, which contains a topological and a normal insulating phases and two topological Weyl semimetal phases with eight and four single-Weyl points, apart from the double-Weyl semimetal phase. Furthermore, we demonstrate with numerical simulations that (i) the mimicked double- and single-Weyl points can be detected by measuring the atomic transfer fractions after a Bloch oscillation; (ii) the Chern number of different quantum phases in the phase diagram can be extracted from the center shift of the hybrid Wannier functions, which can be directly measured with the time-of-flight imaging; (iii) the band topology of the \$C_4$ symmetric Bloch Hamiltonian can be detected simply from measuring the spin polarization at the high symmetry momentum points with a condensate in the optical lattice. The proposed system would provide a promising platform for elaborating the intrinsic exotic physics of double-Weyl semimetals and the related topological phase transitions.

cond-mat.quant-gas↗

arXiv2017

Achieving Adaptation for Adaptive Systems via Runtime Verification: A Model-Driven Approach

Self-adaptive systems (SASs) are capable of adjusting its behavior in response to meaningful changes in the operational con-text and itself. The adaptation needs to be performed automatically through self-managed reactions and decision-making processes at runtime. To support this kind of automatic behavior, SASs must be endowed by a rich runtime support that can detect requirements violations and reason about adaptation decisions. Requirements Engineering for SASs primarily aims to model adaptation logic and mechanisms. Requirements models will guide the design decisions and runtime behaviors of sys-tem-to-be. This paper proposes a model-driven approach for achieving adaptation against non-functional requirements (NFRs), i.e. reliability and performances. The approach begins with the models in RE stage and provides runtime support for self-adaptation. We capture adaptation mechanisms as graphical elements in the goal model. By assigning reliability and performance attributes to related system tasks, we derive the tagged sequential diagram for specifying the reliability and performances of system behaviors. To formalize system behavior, we transform the requirements model to the corresponding behavior model, expressed by Label Transition Systems (LTS). To analyze the reliability requirements and performance requirements, we merged the sequential diagram and LTS to a variable Discrete-Time Markov Chains (DTMC) and a variable Continuous-Time Markov Chains (CTMC) respectively. Adaptation candidates are characterized by the variable states. The optimal decision is derived by verifying the concerned NFRs and reducing the decision space. Our approach is implemented through the demonstration of a mobile information system.

cs.SE↗

arXiv2017

Modeling Uncertainty and Evolving Self-Adaptive Software: A Fuzzy Theory Based Requirements Engineering Approach

Self-adaptive software (SAS) is capable of adjusting its behavior in response to meaningful changes in the operational context and itself. Due to the inherent volatility of the open and changeable environment in which SAS is embedded, the ability of adaptation is highly demanded by many software-intensive systems. Two concerns, i.e., the requirements uncertainty and the context uncertainty are most important among others at Requirements Engineering (RE) stage. However, requirements analyzers can hardly figure out the mathematical relation between requirements, system behavior and context, especially for complex and nonlinear systems, due to the existence of above uncertainties, misunderstanding and ambiguity of prior knowledge. An essential issue to be addressed is how to model and specify these uncertainties at RE stage and how to utilize the prior knowledge to achieve adaptation. In this paper, we propose a fuzzy-based approach to modeling uncertainty and achieving evolution. The approach introduces specifications to describe fuzziness. Based on the specifications, we derive a series of reasoning rules as knowledge base for achieving adaptation and evolution. These two targets are implemented through four reasoning schemas from a control theory perspective. Specifically, forward reasoning schema is used for direct adaptation; backward reasoning schema is used for optimal adaptation. Parameter-identified schema implements learning evolution by considering SAS as the gray-box system, while system-identified reasoning schema implements learning evolution by considering SAS as the gray-box system. The former two schemas function as the control group, while the latter two are de-signed as the experimental groups to illustrate the learning ability. Our approach is implemented under three types of context through the demonstration of a mobile computing application.

cs.SE↗

arXiv2017

A Model-Based Fuzzy Control Approach to Achieving Adaptation with Contextual Uncertainties

Self-adaptive system (SAS) is capable of adjusting its behavior in response to meaningful changes in the operational context and itself. Due to the inherent volatility of the open and changeable environment in which SAS is embedded, the ability of adaptation is highly demanded by many software-intensive systems. Two concerns, i.e., the requirements uncertainty and the context uncertainty are most important among others. An essential issue to be addressed is how to dynamically adapt non-functional requirements (NFRs) and task configurations of SASs with context uncertainty. In this paper, we propose a model-based fuzzy control approach that is underpinned by the feedforward-feedback control mechanism. This approach identifies and represents NFR uncertainties, task uncertainties and context uncertainties with linguistic variables, and then designs an inference structure and rules for the fuzzy controller based on the relations between the requirements model and the context model. The adaptation of NFRs and task configurations is achieved through fuzzification, inference, defuzzification and readaptation. Our approach is demonstrated with a mobile computing application and is evaluated through a series of simulation experiments.

cs.SE↗