arXiv subjects

Tao Chen

Publications and source records attributed to Tao Chen.

At least 487 records · Page 27Linked to original sources

arXiv2020

Robust and Fast Holonomic Quantum Gates with Encoding on Superconducting Circuits

High-fidelity and robust quantum manipulation is the key for scalable quantum computation. Therefore, due to the intrinsic operational robustness, quantum manipulation induced by geometric phases is one of the promising candidates. However, the longer gate time for geometric operations and more physical-implementation difficulties hinder its practical and wide applications. Here, we propose a simplified implementation of universal holonomic quantum gates on superconducting circuits with experimentally demonstrated techniques, which can remove the two main challenges by introducing the time-optimal control into the construction of quantum gates. Remarkably, our scheme is also based on a decoherence-free subspace encoding, with minimal physical qubit resource, which can further immune to error caused by qubit-frequency drift, which is regarded as the main error source for large scale superconducting circuits. Meanwhile, we deliberately design the quantum evolution to eliminate gate error caused by unwanted leakage sources. Therefore, our scheme is more robust than the conventional ones, and thus provides a promising alternative strategy for scalable fault-tolerant quantum computation.

quant-ph↗

arXiv2020

Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline

To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process. However, in existing patterns and methods, domain knowledge and engineers' expertise that is built over time are not explicitly linked to the self-aware processes. This linkage is important, as the knowledge is a valuable asset for the related problems and its absence would cause unnecessary overhead, possibly misleading results and unwise waste of the tremendous benefit that could have been brought by the domain expertise. This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems, relying on well-defined expertise representation, algorithms and techniques. In particular, we present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers to perform difficulty and benefit analysis on possible synergies, in an attempt to keep "engineers-in-the-loop". Through three tutorial case studies, we demonstrate how DBASES can be applied in different domains, within which a carefully selected set of candidates with different synergies can be used for quantitative investigation, providing more informed decisions of the design choices.

cs.SE↗

arXiv2020

DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint Reasoning

The rapidly changing workload of service-based systems can easily cause under-/over-utilization on the component services, which can consequently affect the overall Quality of Service (QoS), such as latency. Self-adaptive services composition rectifies this problem, but poses several challenges: (i) the effectiveness of adaptation can deteriorate due to over-optimistic assumptions on the latency and utilization constraints, at both local and global levels; and (ii) the benefits brought by each composition plan is often short term and is not often designed for long-term benefits -- a natural prerequisite for sustaining the system. To tackle these issues, we propose a two levels constraint reasoning framework for sustainable self-adaptive services composition, called DATESSO. In particular, DATESSO consists of a re ned formulation that differentiates the "strictness" for latency/utilization constraints in two levels. To strive for long-term benefits, DATESSO leverages the concept of technical debt and time-series prediction to model the utility contribution of the component services in the composition. The approach embeds a debt-aware two level constraint reasoning algorithm in DATESSO to improve the efficiency, effectiveness and sustainability of self-adaptive service composition. We evaluate DATESSO on a service-based system with real-world WS-DREAM dataset and comparing it with other state-of-the-art approaches. The results demonstrate the superiority of DATESSO over the others on the utilization, latency and running time whilst likely to be more sustainable.

cs.SE↗

arXiv2020

Cascade EF-GAN: Progressive Facial Expression Editing with Local Focuses

Recent advances in Generative Adversarial Nets (GANs) have shown remarkable improvements for facial expression editing. However, current methods are still prone to generate artifacts and blurs around expression-intensive regions, and often introduce undesired overlapping artifacts while handling large-gap expression transformations such as transformation from furious to laughing. To address these limitations, we propose Cascade Expression Focal GAN (Cascade EF-GAN), a novel network that performs progressive facial expression editing with local expression focuses. The introduction of the local focus enables the Cascade EF-GAN to better preserve identity-related features and details around eyes, noses and mouths, which further helps reduce artifacts and blurs within the generated facial images. In addition, an innovative cascade transformation strategy is designed by dividing a large facial expression transformation into multiple small ones in cascade, which helps suppress overlapping artifacts and produce more realistic editing while dealing with large-gap expression transformations. Extensive experiments over two publicly available facial expression datasets show that our proposed Cascade EF-GAN achieves superior performance for facial expression editing.

cs.CV↗

arXiv2020

On Carbon Nanotubes in the Interstellar Medium

Since their discovery in 1991, carbon nanotubes (CNTs) -- a novel one-dimensional carbon allotrope -- have attracted considerable interest worldwide because of their potential technological applications such as electric and optical devices. In the astrophysical context, CNTs may be present in the interstellar space since many of the other allotropes of carbon (e.g., amorphous carbon, fullerenes, nanodiamonds, graphite, polycyclic aromatic hydrocarbons, and possibly graphene as well) are known to be widespread in the Universe, as revealed by presolar grains in carbonaceous primitive meteorites and/or by their fingerprint spectral features in astronomical spectra. In addition, there are also experimental and theoretical pathways to the formation of CNTs in the interstellar medium (ISM). In this work, we examine their possible presence in the ISM by comparing the observed interstellar extinction curve with the ultraviolet/optical absorption spectra experimentally obtained for single-walled CNTs of a wide range of diameters and chiralities. Based on the absence in the interstellar extinction curve of the ~4.5 and 5.25 eV $π$-plasmon absorption bands which are pronounced in the experimental spectra of CNTs, we place an upper limit of ~10 ppm of C/H (i.e., ~4% of the total interstellar C) on the interstellar CNT abundance.

astro-ph.GA↗

arXiv2020

Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study

Data-driven defect prediction has become increasingly important in software engineering process. Since it is not uncommon that data from a software project is insufficient for training a reliable defect prediction model, transfer learning that borrows data/knowledge from other projects to facilitate the model building at the current project, namely cross-project defect prediction (CPDP), is naturally plausible. Most CPDP techniques involve two major steps, i.e., transfer learning and classification, each of which has at least one parameter to be tuned to achieve their optimal performance. This practice fits well with the purpose of automated parameter optimization. However, there is a lack of thorough understanding about what are the impacts of automated parameter optimization on various CPDP techniques. In this paper, we present the first empirical study that looks into such impacts on 62 CPDP techniques, 13 of which are chosen from the existing CPDP literature while the other 49 ones have not been explored before. We build defect prediction models over 20 real-world software projects that are of different scales and characteristics. Our findings demonstrate that: (1) Automated parameter optimization substantially improves the defect prediction performance of 77\% CPDP techniques with a manageable computational cost. Thus more efforts on this aspect are required in future CPDP studies. (2) Transfer learning is of ultimate importance in CPDP. Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques. This finding provides important insights about the future design of CPDP techniques.

cs.NE↗

arXiv2020

Fast holonomic quantum computation on superconducting circuits with optimal control

Geometric phases induced in quantum evolutions have built-in noise-resilient characters, and thus can find applications in many robust quantum manipulation tasks. Here, we propose a feasible and fast scheme for universal quantum computation on superconducting circuits with nonadiabatic non-Abelian geometric phases, using resonant interaction of three-level quantum system. In our scheme, arbitrary single-qubit quantum gates can be implemented in a single-loop scenario by shaping both the amplitudes and phases of the two driving microwave fields resonantly coupled to a transmon device. Moreover, nontrivial two-qubit gates can also be realized with an auxiliary transmon simultaneously coupled to the two target transmons in an effective resonant way. In particular, our proposal can be compatible to various optimal control techniques, which further enhances the robustness of the quantum operations. Therefore, our proposal represents a promising way towards fault-tolerant quantum computation on solid-state quantum circuits.

quant-ph↗

arXiv2020

Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition

Affective computing and cognitive theory are widely used in modern human-computer interaction scenarios. Human faces, as the most prominent and easily accessible features, have attracted great attention from researchers. Since humans have rich emotions and developed musculature, there exist a lot of fine-grained expressions in real-world applications. However, it is extremely time-consuming to collect and annotate a large number of facial images, of which may even require psychologists to correctly categorize them. To the best of our knowledge, the existing expression datasets are only limited to several basic facial expressions, which are not sufficient to support our ambitions in developing successful human-computer interaction systems. To this end, a novel Fine-grained Facial Expression Database - F2ED is contributed in this paper, and it includes more than 200k images with 54 facial expressions from 119 persons. Considering the phenomenon of uneven data distribution and lack of samples is common in real-world scenarios, we further evaluate several tasks of few-shot expression learning by virtue of our F2ED, which are to recognize the facial expressions given only few training instances. These tasks mimic human performance to learn robust and general representation from few examples. To address such few-shot tasks, we propose a unified task-driven framework - Compositional Generative Adversarial Network (Comp-GAN) learning to synthesize facial images and thus augmenting the instances of few-shot expression classes. Extensive experiments are conducted on F2ED and existing facial expression datasets, i.e., JAFFE and FER2013, to validate the efficacy of our F2ED in pre-training facial expression recognition network and the effectiveness of our proposed approach Comp-GAN to improve the performance of few-shot recognition tasks.

cs.CV↗

arXiv2020

High-fidelity geometric gate for silicon-based spin qubits

High-fidelity manipulation is the key for the physical realization of fault-tolerant quantum computation. Here, we present a protocol to realize universal nonadiabatic geometric gates for silicon-based spin qubits. We find that the advantage of geometric gates over dynamical gates depends crucially on the evolution loop for the construction of the geometric phase. Under appropriate evolution loops, both the geometric single-qubit gates and the CNOT gate can outperform their dynamical counterparts for both systematic and detuning noises. We also perform randomized benchmarking using noise amplitudes consistent with experiments in silicon. For the static noise model, the averaged fidelities of geometric gates are around 99.90\% or above, while for the time-dependent $1/f$-type noise, the fidelities are around 99.98\% when only the detuning noise is present. We also show that the improvement in fidelities of the geometric gates over dynamical ones typically increases with the exponent $α$ of the $1/f$ noise, and the ratio can be as high as 4 when $α\approx 3$. Our results suggest that geometric gates with judiciously chosen evolution loops can be a powerful way to realize high-fidelity quantum gates.

quant-ph↗

arXiv2020

Tunable non-reciprocal quantum transport through a dissipative Aharonov-Bohm ring in ultracold atoms

We report the experimental observation of tunable, non-reciprocal quantum transport of a Bose-Einstein condensate in a momentum lattice. By implementing a dissipative Aharonov-Bohm (AB) ring in momentum space and sending atoms through it, we demonstrate a directional atom flow by measuring the momentum distribution of the condensate at different times. While the dissipative AB ring is characterized by the synthetic magnetic flux through the ring and the laser-induced loss on it, both the propagation direction and transport rate of the atom flow sensitively depend on these highly tunable parameters. We demonstrate that the non-reciprocity originates from the interplay of the synthetic magnetic flux and the laser-induced loss, which simultaneously breaks the inversion and the time-reversal symmetries. Our results open up the avenue for investigating non-reciprocal dynamics in cold atoms, and highlight the dissipative AB ring as a flexible building element for applications in quantum simulation and quantum information.

cond-mat.quant-gas↗

arXiv2020

Topological quantum walks in momentum space with a Bose-Einstein condensate

We report the experimental implementation of discrete-time topological quantum walks of a Bose-Einstein condensate in momentum space. Introducing stroboscopic driving sequences to the generation of a momentum lattice, we show that the dynamics of atoms along the lattice is effectively governed by a periodically driven Su-Schrieffer-Heeger model, which is equivalent to a discrete-time topological quantum walk. We directly measure the underlying topological invariants through time-averaged mean chiral displacements, which are consistent with our experimental observation of topological phase transitions. We then observe interaction-induced localization in the quantum-walk dynamics, where atoms tend to populate a single momentum-lattice site under interactions that are non-local in momentum space. Our experiment opens up the avenue of investigating discrete-time topological quantum walks using cold atoms, where the many-body environment and tunable interactions offer exciting new possibilities.

cond-mat.quant-gas↗

arXiv2019

Fragmentation and isomerization of polycyclic aromatic hydrocarbons in the interstellar medium: coronene as a case study

Aims. Due to the limitations of current computational technology, the fragmentation and isomerization products of vibrationally-excited polycyclic aromatic hydrocarbon (PAH) molecules and their derivatives are poorly studied. In this work, we investigate the intermediate products of PAHs and their derivatives as well as the gas-phase reactions relevant to the interstellar medium, with coronene as a case study. Methods. Based on the semi-empirical method of PM3 as implemented in the CP2K program, molecular dynamics simulations are performed to model the major processes (e.g., vibrations, fragmentations, and isomerizations) of coronene and its derivatives (e.g., methylated coronene, hydrogenated coronene, dehydrogenated coronene, nitrogen-substituted coronene, and oxygen-substituted coronene) at temperatures of 3000 K and 4000 K. Results. We find that the anharmonic effects are crucial for the simulation of vibrational excitation. For the molecules studied here, H2, CO, HCN, and CH2 are the major fragments. Following the dissociation of these small units, most of the molecules could maintain their ring structures, but a few molecules would break completely into carbon chains. The transformation from hexagon to pentagon or heptagon may occur and the heteroatomic substitutions (e.g., N- or O-substitutions) facilitate the transformation.

astro-ph.GA↗

arXiv2019

Scalable nonadiabatic holonomic quantum computation on a superconducting qubit lattice

Geometric phase is an indispensable element for achieving robust and high-fidelity quantum gates due to its built-in noise-resilience feature. However, due to the complexity of manipulation and the intrinsic leakage of the encoded quantum information to non-logical-qubit basis, the experimental realization of universal nonadiabatic holonomic quantum computation is very difficult. Here, we propose to implement scalable nonadiabatic holonomic quantum computation with decoherence-free subspace encoding on a two-dimensional square superconducting transmon-qubit lattice, where only the two-body interaction of neighboring qubits, from the simplest capacitive coupling, is needed. Meanwhile, we introduce qubit-frequency driving to achieve tunable resonant coupling for the neighboring transmon qubits, and thus avoiding the leakage problem. In addition, our presented numerical simulation shows that high-fidelity quantum gates can be obtained, verifying the advantages of the robustness and scalability of our scheme. Therefore, our scheme provides a promising way towards the physical implementation of robust and scalable quantum computation.

quant-ph↗

arXiv2019

Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.

cs.AI↗

arXiv2019

Resource Awareness in Unmanned Aerial Vehicle-Assisted Mobile-Edge Computing Systems

This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, in which the UAV provides complementary computation resource to the terrestrial MEC system. The UAV processes the received computation tasks from the mobile users (MUs) by creating the corresponding virtual machines. Due to finite shared I/O resource of the UAV in the MEC system, each MU competes to schedule local as well as remote task computations across the decision epochs, aiming to maximize the expected long-term computation performance. The non-cooperative interactions among the MUs are modeled as a stochastic game, in which the decision makings of a MU depend on the global state statistics and the task scheduling policies of all MUs are coupled. To approximate the Nash equilibrium solutions, we propose a proactive scheme based on the long short-term memory and deep reinforcement learning (DRL) techniques. A digital twin of the MEC system is established to train the proactive DRL scheme offline. Using the proposed scheme, each MU makes task scheduling decisions only with its own information. Numerical experiments show a significant performance gain from the scheme in terms of average utility per MU across the decision epochs.

cs.NI↗

arXiv2019

A Gegenbauer Neural Network with Regularized Weights Direct Determination for Classification

Single-hidden layer feed forward neural networks (SLFNs) are widely used in pattern classification problems, but a huge bottleneck encountered is the slow speed and poor performance of the traditional iterative gradient-based learning algorithms. Although the famous extreme learning machine (ELM) has successfully addressed the problems of slow convergence, it still has computational robustness problems brought by input weights and biases randomly assigned. Thus, in order to overcome the aforementioned problems, in this paper, a novel type neural network based on Gegenbauer orthogonal polynomials, termed as GNN, is constructed and investigated. This model could overcome the computational robustness problems of ELM, while still has comparable structural simplicity and approximation capability. Based on this, we propose a regularized weights direct determination (R-WDD) based on equality-constrained optimization to determine the optimal output weights. The R-WDD tends to minimize the empirical risks and structural risks of the network, thus to lower the risk of over fitting and improve the generalization ability. This leads us to a the final GNN with R-WDD, which is a unified learning mechanism for binary and multi-class classification problems. Finally, as is verified in the various comparison experiments, GNN with R-WDD tends to have comparable (or even better) generalization performances, computational scalability and efficiency, and classification robustness, compared to least square support vector machine (LS-SVM), ELM with Gaussian kernel.

cs.LG↗

arXiv2019

The infrared bands of polycyclic aromatic hydrocarbons in the 1.6-1.7 μm wavelength region

Context. The 3.3 $μ$m aromatic C-H stretching band of polycyclic aromatic hydrocarbon (PAH) molecules seen in a wide variety of astrophysical regions is often accompanied by a series of weak satellite bands at ~3.4-3.6 $μ$m. One of these sources, IRAS 21282+5050, a planetary nebula, also exhibits a weak band at ~1.68 $μ$m. While the satellite features at ~3.4-3.6 $μ$m are often attributed to the anharmonicities of PAHs, it is not clear whether overtones or combination bands dominate the 1.68 $μ$m feature. Aims. In this work, we examine the anharmonic spectra of eight PAH molecules, including anthracene, tetracene, pentacene, phenanthrene, chrysene, benz[a]anthracene, pyrene, and perylene, to explore the origin of the infrared bands in the 1.6-1.7 $μ$m waveelngth region. Methods. Density Functional Theory (DFT) in combination with the vibrational second-order perturbation theory (VPT2) is utilized for computing the anharmonic spectra of PAHs. To simulate the vibrational excitation process of PAHs, the Wang-Landau random walk technique is employed. Results. All the dominant bands in the 1.6-1.7 $μ$m wavelength range and in the 3.1-3.5 $μ$m C-H stretching region are calculated and tabulated. It is demonstrated that combination bands dominate the 1.6-1.7 $μ$m region, while overtones are rare and weak in this region. We also calculate the intensity ratios of the 3.1-3.5 $μ$m C-H stretching features to the bands in the 1.6-1.7 $μ$m region, $I_{3.1-3.5}/I_{1.6-1.7}$, for both ground and vibrationally excited states. On average, we obtain $\langle I_{3.1-3.5}/I_{1.6-1.7} \rangle$ $\approx$ 12.6 and $\langle I_{3.1-3.5}/I_{1.6-1.7} \rangle$ $\approx$ 17.6 for PAHs at ground states and at vibrationally excited states, respectively.

astro-ph.IM↗

arXiv2019

Variational and numerical analysis of a dynamic viscoelastic contact problem with friction and wear

In this paper, we consider a dynamic viscoelastic contact problem with friction and wear, and describe it as a system of nonlinear partial differential equations. We formulate the previous problem as a hyperbolic quasi-variational inequality by employing the variational method. We adopt the Rothe method to show the existence and uniqueness of weak solution for the hyperbolic quasi-variational inequality under mild conditions. We also give a fully discrete scheme for solving the hyperbolic quasi-variational inequality and obtain error estimates for the fully discrete scheme.

math.OC↗