arXiv subjects

Fei Liu

Publications and source records attributed to Fei Liu.

At least 307 records · Page 17Linked to original sources

arXiv2017

Sparsify and sweep: an efficient preconditioner for the Lippmann-Schwinger equation

This paper presents an efficient preconditioner for the Lippmann-Schwinger equation that combines the ideas of the sparsifying and the sweeping preconditioners. Following first the idea of the sparsifying preconditioner, this new preconditioner starts by transforming the dense linear system of the Lippmann-Schwinger equation into a nearly sparse system. The key novelty is a newly designed perfectly matched layer (PML) stencil for the boundary degrees of freedoms. The resulting sparse system gives rise to fairly accurate solutions and hence can be viewed as an accurate discretization of the Helmholtz equation. This new PML stencil also paves the way for applying the moving PML sweeping preconditioner to invert the resulting sparse system approximately. When combined with the standard GMRES solver, this new preconditioner for the Lippmann-Schwinger equation takes only a few iterations to converge for both 2D and 3D problems, where the iteration numbers are almost independent of the frequency. To the best of our knowledge, this is the first method that achieves near-linear cost to solve the 3D Lippmann-Schwinger equation in high frequency cases.

math.NA↗

arXiv2017

Resonant Photon-Exciton Coupling in All-Semiconductor Heterostructures Composed of Silicon Nanosphere and Monolayer WS2

Tailoring and enhancing the interaction between light and matter is of great importance for both fundamental researches and future photonic and optoelectronic applications. Due to their high exciton oscillator strength and large exciton binding energy, two-dimensional atomic semiconducting transition metal dichalcogenides have recently emerged as an excellent platform for the strong photon-exciton interaction by integrating with optically resonant cavities. Here, we propose an all-semiconductor system composed of individual silicon nanospheres and monolayer WS2 and investigate the resonance coupling between these two constituents. By coating the silicon nanospheres with monolayer WS2, we demonstrate the strong resonance coupling between the magnetic dipole mode and A-exciton, evidenced by an anticrossing behavior in the scattering energy diagram with a Rabi splitting of 77 meV. Compared with the plasmonic analogues, the resonance coupling in all-semiconductor heterostructure is much stronger and less sensitive to the spacing between the silicon nanosphere core and WS2 shell. When the silicon nanospheres are placed onto the WS2 monolayer with a point contact, resonance coupling manifested by the quenching dips in the scattering spectra can also be observed at ambient conditions, which involves only a few excitons. Finally, resonance coupling in the all-semiconductor heterostructure can be active controlled by temperature scanning. Our findings suggest that this all-semiconductor heterostructure can be exploited for future on-chip nanophotonics associated with strong light-matter interactions.

physics.optics↗

arXiv2017

Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields

Despite successful applications across a broad range of NLP tasks, conditional random fields ("CRFs"), in particular the linear-chain variant, are only able to model local features. While this has important benefits in terms of inference tractability, it limits the ability of the model to capture long-range dependencies between items. Attempts to extend CRFs to capture long-range dependencies have largely come at the cost of computational complexity and approximate inference. In this work, we propose an extension to CRFs by integrating external memory, taking inspiration from memory networks, thereby allowing CRFs to incorporate information far beyond neighbouring steps. Experiments across two tasks show substantial improvements over strong CRF and LSTM baselines.

cs.CL↗

arXiv2017

An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis

Products reviews are one of the major resources to determine the public sentiment. The existing literature on reviews sentiment analysis mainly utilizes supervised paradigm, which needs labeled data to be trained on and suffers from domain-dependency. This article addresses these issues by describes a completely automatic approach for sentiment analysis based on unsupervised ensemble learning. The method consists of two phases. The first phase is contextual analysis, which has five processes, namely (1) data preparation; (2) spelling correction; (3) intensifier handling; (4) negation handling and (5) contrast handling. The second phase comprises the unsupervised learning approach, which is an ensemble of clustering classifiers using a majority voting mechanism with different weight schemes. The base classifier of the ensemble method is a modified k-means algorithm. The base classifier is modified by extracting initial centroids from the feature set via using SentWordNet (SWN). We also introduce new sentiment analysis problems of Australian airlines and home builders which offer potential benchmark problems in the sentiment analysis field. Our experiments on datasets from different domains show that contextual analysis and the ensemble phases improve the clustering performance in term of accuracy, stability and generalization ability.

cs.CL↗

arXiv2017

Localized Sparsifying Preconditioner for Periodic Indefinite Systems

This paper introduces the localized sparsifying preconditioner for the pseudospectral approximations of indefinite systems on periodic structures. The work is built on top of the recently proposed sparsifying preconditioner with two major modifications. First, the local potential information is utilized to improve the accuracy of the preconditioner. Second, an FFT based method to compute the local stencil is proposed to reduce the setup time of the algorithm. Numerical results show that the iteration number of this improved method grows only mildly as the problem size grows, which implies that solving pseudospectral approximation systems is computationally as efficient as solving sparse systems, up to a mildly growing factor.

math.NA↗

arXiv2017

Humans of Simulated New York (HOSNY): an exploratory comprehensive model of city life

The model presented in this paper experiments with a comprehensive simulant agent in order to provide an exploratory platform in which simulation modelers may try alternative scenarios and participation in policy decision-making. The framework is built in a computationally distributed online format in which users can join in and visually explore the results. Modeled activity involves daily routine errands, such as shopping, visiting the doctor or engaging in the labor market. Further, agents make everyday decisions based on individual behavioral attributes and minimal requirements, according to social and contagion networks. Fully developed firms and governments are also included in the model allowing for taxes collection, production decisions, bankruptcy and change in ownership. The contributions to the literature are multifold. They include (a) a comprehensive model with detailing of the agents and firms' activities and processes and original use of simultaneously (b) reinforcement learning for firm pricing and demand allocation; (c) social contagion for disease spreading and social network for hiring opportunities; and (d) Bayesian networks for demographic-like generation of agents. All of that within a (e) visually rich environment and multiple use of databases. Hence, the model provides a comprehensive framework from where interactions among citizens, firms and governments can be easily explored allowing for learning and visualization of policies and scenarios.

cs.MA↗

arXiv2017

Room-temperature vacuum Rabi splitting with active control in two-dimensional atomic crystals

Strong light-matter coupling manifested by vacuum Rabi splitting has attracted tremendous attention due to its fundamental importance in cavity quantum-electrodynamics research and great potentials in quantum information applications. A prerequisite for practical applications of the strong coupling in future quantum information processing and coherent manipulation is an all-solid-state system exhibiting room-temperature vacuum Rabi splitting with active control. Here we realized such a system in heterostructure consisting of monolayer WS2 and an individual plasmonic gold nanorod. By taking advantage of both of the small mode volume of the nanorod and large binding energy of the WS2 exciton, giant vacuum Rabi splitting energies of 91 meV ~ 133 meV can be obtained at ambient conditions, which only involve 5 ~ 18 excitons. The vacuum Rabi splitting can be dynamically tuned either by electrostatic gating or temperature scanning. These findings could pave the way towards active quantum optic devices operating at room temperature.

physics.optics↗

arXiv2017

Performance of Proportional Fair Scheduling for Downlink Non-Orthogonal Multiple Access Systems

In this paper, we present the first analytical solution for performance analysis of proportional fair scheduling (PFS) in downlink non-orthogonal multiple access (NOMA) systems. Assuming an ideal NOMA system with an arbitrary number of multiplexed users, we derive a closed-form solution of the optimal power allocation for PFS and design a low-complexity algorithm for joint power allocation and user set selection. We develop an analytical model to analyze the throughput performance of this optimal solution based on stochastic channel modeling. Our analytical performance is proved to be the upper bound for PFS and is used to estimate user data rates and overall throughput in practical NOMA systems. We conduct system-level simulations to evaluate the accuracy of our data rate estimation. The simulation results verify our analysis on the upper bound of PFS performance in NOMA and confirm that using the analytical performance for data rate estimation guarantees high accuracy. The impact of partial and imperfect channel information on the estimation performance is investigated as well.

cs.IT↗

arXiv2017

Dialog state tracking, a machine reading approach using Memory Network

In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules. This paper introduces a novel method of dialog state tracking based on the general paradigm of machine reading and proposes to solve it using an End-to-End Memory Network, MemN2N, a memory-enhanced neural network architecture. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset. The corpus has been converted for the occasion in order to frame the hidden state variable inference as a question-answering task based on a sequence of utterances extracted from a dialog. We show that the proposed tracker gives encouraging results. Then, we propose to extend the DSTC-2 dataset with specific reasoning capabilities requirement like counting, list maintenance, yes-no question answering and indefinite knowledge management. Finally, we present encouraging results using our proposed MemN2N based tracking model.

cs.CL↗

arXiv2017

Analysis of Proportional Fair Scheduling Under Bursty On-Off Traffic

Proportional fair scheduling (PFS) has been adopted as a standard solution for fair resource allocation in modern wireless cellular networks. With the emergence of heterogeneous networks with widely varying user loads, it is of great importance to characterize the performance of PFS under bursty traffic, which is the case in most wireless streaming and data transfer services. In this letter, we provide the first analytical solution to the performance of PFS under bursty on-off traffic load. We use the Gaussian approximation model to derive a closed-form expression of the achievable user data rates. In order to further improve the accuracy of our baseline analytical solution for multi-cell networks, we design a hybrid approximation by employing multi-interference analysis. The simulation results verify that our model guarantees extremely low data rate estimation error, which is further insensitive to changes in session duration, traffic load and user density.

cs.IT↗

arXiv2016

Characteristic Functions Based on Quantum Jump Trajectory

Characteristic functions (CFs) provide a very efficient method for evaluating the probability density functions of stochastic thermodynamic quantities and investigating their statistical features in quantum master equations (QMEs). A conventional procedure for obtaining these functions is to resort to a first-principles approach; namely, the evolution equations of the CFs of the combined system and its environment are obtained and then projected into the degrees of freedom of the system. However, the QMEs can be unraveled by a quantum jump trajectory. Thermodynamic quantities such as the heat, work, and entropy production can be well defined along a trajectory. Hence, on the basis of the notion of a trajectory, can we straightforwardly derive these CFs, e.g., their evolution equations? This is essential to establish the self-contained stochastic thermodynamics of a QME. In this paper, we show that it is indeed plausible and also simple. Particularly, these equations are fully consistent with those obtained by the first-principles method. Our results have practical significance; they indicate that the quantum fluctuation relations could be verified by more realistic photocounting experiments.

cond-mat.stat-mech↗

arXiv2016

Gated End-to-End Memory Networks

Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks, MemN2N, have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact question-answering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-to-End trainable Memory Network architecture, GMemN2N. From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art.

cs.CL↗

arXiv2016

A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts

Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.

cs.CL↗

arXiv2016

A time-work uncertainty relation in quantum systems

In quantum systems, a plausible definition of work is based on two energy measurement scheme. Considering that energy change of quantum system obeys a time-energy uncertainty relation, it shall be interesting to see whether such type of work as well obeys an analogous uncertainty relation. In this note I argue that this relation indeed exists for closed quantum systems and open quantum systems, which the latter are assumed to be weakly coupled with their environments.

cond-mat.stat-mech↗

arXiv2016

Impact of Edge States on Device Performance of Phosphorene Heterojunction Tunneling Field Effect Transistors

Black phosphorus (BP) tunneling transistors (TFETs) using heterojunction (He) are investigated by atomistic quantum transport simulations. It is observed that edge states have a great impact on transport characteristics of BP He-TFETs, which result in the potential pinning effect and deteriorate the gate control. While, on-state current can be effectively enhanced by using hydrogen to saturate the edge dangling bonds in BP He-TFETs, in which edge states are quenched. By extending layered BP with a smaller band gap to the channel region and modulating the BP thickness, device performance of BP He-TFETs can be further optimized and fulfill the requirements of the international technology road-map for semiconductors (ITRS) 2013 for low power applications. In 15 nm 3L-1L and 4L-1L BP He-TFETs along armchair direction on-state current can reach above 10$^3$ $μ$A/$μ$m with the fixed off-state current of 10 $pA/μ$m. It is also found that ambipolar effect can be effectively suppressed in BP He-TFETs.

cond-mat.mes-hall↗

arXiv2016

From Incremental Meaning to Semantic Unit (phrase by phrase)

This paper describes an experimental approach to Detection of Minimal Semantic Units and their Meaning (DiMSUM), explored within the framework of SemEval 2016 Task 10. The approach is primarily based on a combination of word embeddings and parserbased features, and employs unidirectional incremental computation of compositional embeddings for multiword expressions.

cs.CL↗

arXiv2016

Additive Sweeping Preconditioner for the Helmholtz Equation

We introduce a new additive sweeping preconditioner for the Helmholtz equation based on the perfect matched layer (PML). This method divides the domain of interest into thin layers and proposes a new transmission condition between the subdomains where the emphasis is on the boundary values of the intermediate waves. This approach can be viewed as an effective approximation of an additive decomposition of the solution operator. When combined with the standard GMRES solver, the iteration number is essentially independent of the frequency. Several numerical examples are tested to show the efficiency of this new approach.

math.NA↗

arXiv2015

Calculating work in weakly driven quantum master equations: backward and forward equations

I present a technical report indicating that the two methods used for calculating characteristic functions for the work distribution in weakly driven quantum master equations are equivalent. One involves applying the notion of quantum jump trajectory [Phys. Rev. E 89, 042122 (2014)], while the other is based on two energy measurements on the combined system and reservoir [Silaev, et al., Phys. Rev. E 90, 022103 (2014)]. These represent backward and forward methods, respectively, which adopt a very similar approach to that of the Kolmogorov backward and forward equations used in classical stochastic theory. The microscopic basis for the former method is also clarified. In addition, a previously unnoticed equality related to the heat is also revealed.

cond-mat.stat-mech↗