arXiv subjects

Ke Li

Publications and source records attributed to Ke Li.

At least 433 records · Page 24Linked to original sources

arXiv2018

FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Software

Self-adaptive software (SAS) can reconfigure itself to adapt to the changing environment at runtime, aiming for continually optimizing conflicted non-functional objectives, e.g., response time, energy consumption, throughput and cost etc. In this paper, we present Feature guided and knEe driven Multi-Objective optimization for Self-Adaptive softwAre (FEMOSAA), a novel framework that automatically synergizes the feature model and Multi-Objective Evolutionary Algorithm (MOEA), to optimize SAS at runtime. FEMOSAA operates in two phases: at design time, FEMOSAA automatically transposes the engineers' design of SAS, expressed as a feature model, to fit the MOEA, creating new chromosome representation and reproduction operators. At runtime, FEMOSAA utilizes the feature model as domain knowledge to guide the search and further extend the MOEA, providing a larger chance for finding better solutions. In addition, we have designed a new method to search for the knee solutions, which can achieve a balanced trade-off. We comprehensively evaluated FEMOSAA on two running SAS: one is a highly complex SAS with various adaptable real-world software under the realistic workload trace, another is a service-oriented SAS that can be dynamically composed from services. In particular, we compared the effectiveness and overhead of FEMOSAA against four of its variants and three other search-based frameworks for SAS under various scenarios, including three commonly applied MOEAs, two workload patterns and diverse conflicting quality objectives. The results reveal the effectiveness of FEMOSAA and its superiority over the others with high statistical significance and non-trivial effect sizes.

cs.SE↗

arXiv2018

Squashed entanglement, k-extendibility, quantum Markov chains, and recovery maps

Squashed entanglement [Christandl and Winter, J. Math. Phys. 45(3):829-840 (2004)] is a monogamous entanglement measure, which implies that highly extendible states have small value of the squashed entanglement. Here, invoking a recent inequality for the quantum conditional mutual information [Fawzi and Renner, Commun. Math. Phys. 340(2):575-611 (2015)] greatly extended and simplified in various work since, we show the converse, that a small value of squashed entanglement implies that the state is close to a highly extendible state. As a corollary, we establish an alternative proof of the faithfulness of squashed entanglement [Brandao, Christandl and Yard, Commun. Math. Phys. 306:805-830 (2011)]. We briefly discuss the previous and subsequent history of the Fawzi-Renner bound and related conjectures, and close by advertising a potentially far-reaching generalization to universal and functorial recovery maps for the monotonicity of the relative entropy.

quant-ph↗

arXiv2017

Learning to Optimize Neural Nets

Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinforcement learning algorithms. We develop an extension that is suited to learning optimization algorithms in this setting and demonstrate that the learned optimization algorithm consistently outperforms other known optimization algorithms even on unseen tasks and is robust to changes in stochasticity of gradients and the neural net architecture. More specifically, we show that an optimization algorithm trained with the proposed method on the problem of training a neural net on MNIST generalizes to the problems of training neural nets on the Toronto Faces Dataset, CIFAR-10 and CIFAR-100.

cs.LG↗

arXiv2017

Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, two-archive evolutionary algorithm, for constrained multi-objective optimization. It maintains two co-evolving populations simultaneously: one, denoted as convergence archive, is the driving force to push the population toward the Pareto front; the other one, denoted as diversity archive, mainly tends to maintain the population diversity. In particular, to complement the behavior of the convergence archive and provide as much diversified information as possible, the diversity archive aims at exploring areas under-exploited by the convergence archive including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, comparing to five state-of-the-art constrained evolutionary multi-objective optimizers.

cs.NE↗

arXiv2017

Uncertainties of the $B\to D$ transition form factor from the $D$-Meson Leading-Twist Distribution Amplitude

The $B\to D$ transition form factor (TFF) $f^{B\to D}_+(q^2)$ is determined mainly by the $D$-meson leading-twist distribution amplitude (DA) , $ϕ_{2;D}$, if the proper chiral current correlation function is adopted within the light-cone QCD sum rules. It is therefore significant to make a comprehensive study of DA $ϕ_{2;D}$ and its impact on $f^{B\to D}_+(q^2)$. In this paper, we calculate the moments of $ϕ_{2;D}$ with the QCD sum rules under the framework of the background field theory. New sum rules for the leading-twist DA moments $\left<ξ^n\right>_D$ up to fourth order and up to dimension-six condensates are presented. At the scale $μ= 2 \rm GeV$, the values of the first four moments are: $\left<ξ^1\right>_D = -0.418^{+0.021}_{-0.022}$, $\left<ξ^2\right>_D = 0.289^{+0.023}_{-0.022}$, $\left<ξ^3\right>_D = -0.178 \pm 0.010$ and $\left<ξ^4\right>_D = 0.142^{+0.013}_{-0.012}$. Basing on the values of $\left<ξ^n\right>_D(n=1,2,3,4)$, a better model of $ϕ_{2;D}$ is constructed. Applying this model for the TFF $f^{B\to D}_+(q^2)$ under the light cone sum rules, we obtain $f^{B\to D}_+(0) = 0.673^{+0.038}_{-0.041}$ and $f^{B\to D}_+(q^2_{\rm max}) = 1.124^{+0.053}_{-0.058}$. The uncertainty of $f^{B\to D}_+(q^2)$ from $ϕ_{2;D}$ is estimated and we find its impact should be taken into account, especially in low and central energy region. The branching ratio $\mathcal{B}(B\to Dl\barν_l)$ is calculated, which is consistent with experimental data.

hep-ph↗

arXiv2017

Fast k-Nearest Neighbour Search via Prioritized DCI

Most exact methods for k-nearest neighbour search suffer from the curse of dimensionality; that is, their query times exhibit exponential dependence on either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing (DCI) offers a promising way of circumventing the curse and successfully reduces the dependence of query time on intrinsic dimensionality from exponential to sublinear. In this paper, we propose a variant of DCI, which we call Prioritized DCI, and show a remarkable improvement in the dependence of query time on intrinsic dimensionality. In particular, a linear increase in intrinsic dimensionality, or equivalently, an exponential increase in the number of points near a query, can be mostly counteracted with just a linear increase in space. We also demonstrate empirically that Prioritized DCI significantly outperforms prior methods. In particular, relative to Locality-Sensitive Hashing (LSH), Prioritized DCI reduces the number of distance evaluations by a factor of 14 to 116 and the memory consumption by a factor of 21.

cs.LG↗

arXiv2017

Self-protected nanoscale thermometry based on spin defects in silicon carbide

Quantum sensors with solid state electron spins have attracted considerable interest due to their nanoscale spatial resolution.A critical requirement is to suppress the environment noise of the solid state spin sensor.Here we demonstrate a nanoscale thermometer based on silicon carbide (SiC) electron spins.We experimentally demonstrate that the performance of the spin sensor is robust against dephasing due to a self protected machenism. The SiC thermometry may provide a promising platform for sensing in a noisy environment ,e.g. biological system sensing.

quant-ph↗

arXiv2017

Evolutionary Many-Objective Optimization Based on Adversarial Decomposition

The decomposition-based method has been recognized as a major approach for multi-objective optimization. It decomposes a multi-objective optimization problem into several single-objective optimization subproblems, each of which is usually defined as a scalarizing function using a weight vector. Due to the characteristics of the contour line of a particular scalarizing function, the performance of the decomposition-based method strongly depends on the Pareto front's shape by merely using a single scalarizing function, especially when facing a large number of objectives. To improve the flexibility of the decomposition-based method, this paper develops an adversarial decomposition method that leverages the complementary characteristics of two different scalarizing functions within a single paradigm. More specifically, we maintain two co-evolving populations simultaneously by using different scalarizing functions. In order to avoid allocating redundant computational resources to the same region of the Pareto front, we stably match these two co-evolving populations into one-one solution pairs according to their working regions of the Pareto front. Then, each solution pair can at most contribute one mating parent during the mating selection process. Comparing with nine state-of-the-art many-objective optimizers, we have witnessed the competitive performance of our proposed algorithm on 130 many-objective test instances with various characteristics and Pareto front's shapes.

cs.NE↗

arXiv2017

Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing

Existing methods for retrieving k-nearest neighbours suffer from the curse of dimensionality. We argue this is caused in part by inherent deficiencies of space partitioning, which is the underlying strategy used by most existing methods. We devise a new strategy that avoids partitioning the vector space and present a novel randomized algorithm that runs in time linear in dimensionality of the space and sub-linear in the intrinsic dimensionality and the size of the dataset and takes space constant in dimensionality of the space and linear in the size of the dataset. The proposed algorithm allows fine-grained control over accuracy and speed on a per-query basis, automatically adapts to variations in data density, supports dynamic updates to the dataset and is easy-to-implement. We show appealing theoretical properties and demonstrate empirically that the proposed algorithm outperforms locality-sensitivity hashing (LSH) in terms of approximation quality, speed and space efficiency.

cs.DS↗

arXiv2017

Scalable fabrication of single silicon vacancy defect arrays in silicon carbide using focused ion beam

In this work, we present a method for targeted, maskless, and scalable fabrication of single silicon vacancy (VSi) defect arrays in silicon carbide (SiC) using focused ion beam. The resolution of implanted VSi defects is limited to a few tens of nanometers, defined by the diameter of the ion beam. Firstly, we studied the photoluminescence (PL) spectrum and optically detected magnetic resonance (ODMR) of the generated defect spin ensemble, confirming that the synthesized centers were in the desired defect state. Then we investigated the fluorescence properties of single VSi defects and our measurements indicate the presence of a photostable single photon source. Finally, we find that the Si++ ion to VSi defect conversion yield increases as the implanted dose decreases. The reliable production of VSi defects in silicon carbide could pave the way for its applications in quantum photonics and quantum information processing.

quant-ph↗

arXiv2017

Microsecond dark-exciton valley polarization memory in 2D heterostructures

Transition metal dichalcogenides (TMDs) have valley degree of freedom, which features optical selection rule and spin-valley locking, making them promising for valleytronics devices and quantum computation. For either application, a long valley polarization lifetime is crucial. Previous results showed that it is around picosecond in monolayer excitons, nanosecond for electrons, holes or local excitons and tens of nanosecond for interlayer excitons. Here we show that dark excitons in 2D heterostructures provide a microsecond valley polarization memory thanks to the magnetic field induced suppression of valley mixing. The lifetime of the dark excitons shows magnetic field and temperature dependence which is consistent with the theoretical prediction. The long dark exciton lifetime and valley polarization lifetime in 2D heterostructures make them promising for long-distance exciton transport and macroscopic quantum state generations.

cond-mat.mes-hall↗

arXiv2017

Dynamic Multi-Objectives Optimization with a Changing Number of Objectives

Existing studies on dynamic multi-objective optimization focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature. Instead of changing the shape or position of the Pareto-optimal front/set when having time-dependent objective functions, increasing or decreasing the number of objectives usually leads to the expansion or contraction of the dimension of the Pareto-optimal front/set manifold. Unfortunately, most existing dynamic handling techniques can hardly be adapted to this type of dynamics. In this paper, we report our attempt toward tackling the dynamic multi-objective optimization problems with a changing number of objectives. We implement a new two-archive evolutionary algorithm which maintains two co-evolving populations simultaneously. In particular, these two populations are complementary to each other: one concerns more about the convergence while the other concerns more about the diversity. The compositions of these two populations are adaptively reconstructed once the environment changes. In addition, these two populations interact with each other via a mating selection mechanism. Comprehensive experiments are conducted on various benchmark problems with a time-dependent number of objectives. Empirical results fully demonstrate the effectiveness of our proposed algorithm.

cs.NE↗

arXiv2017

Effective face landmark localization via single deep network

In this paper, we propose a novel face alignment method using single deep network (SDN) on existing limited training data. Rather than using a max-pooling layer followed one convolutional layer in typical convolutional neural networks (CNN), SDN adopts a stack of 3 layer groups instead. Each group layer contains two convolutional layers and a max-pooling layer, which can extract the features hierarchically. Moreover, an effective data augmentation strategy and corresponding training skills are also proposed to over-come the lack of training images on COFW and 300-W da-tasets. The experiment results show that our method outper-forms state-of-the-art methods in both detection accuracy and speed.

cs.CV↗

arXiv2017

Matching-Based Selection with Incomplete Lists for Decomposition Multi-Objective Optimization

The balance between convergence and diversity is a key issue of evolutionary multi-objective optimization. The recently proposed stable matching-based selection provides a new perspective to handle this balance under the framework of decomposition multi-objective optimization. In particular, the stable matching between subproblems and solutions, which achieves an equilibrium between their mutual preferences, implicitly strikes a balance between the convergence and diversity. Nevertheless, the original stable matching model has a high risk of matching a solution with a unfavorable subproblem which finally leads to an imbalanced selection result. In this paper, we propose an adaptive two-level stable matching-based selection for decomposition multi-objective optimization. Specifically, borrowing the idea of stable matching with incomplete lists, we match each solution with one of its favorite subproblems by restricting the length of its preference list during the first-level stable matching. During the second-level stable matching, the remaining subproblems are thereafter matched with their favorite solutions according to the classic stable matching model. In particular, we develop an adaptive mechanism to automatically set the length of preference list for each solution according to its local competitiveness. The performance of our proposed method is validated and compared with several state-of-the-art evolutionary multi-objective optimization algorithms on 62 benchmark problem instances. Empirical results fully demonstrate the competitive performance of our proposed method on problems with complicated Pareto sets and those with more than three objectives.

cs.NE↗

arXiv2017

Coherent control of a strongly driven silicon vacancy optical transition in diamond

The ability to prepare, optically read out and coherently control single quantum states is a key requirement for quantum information processing. Optically active solid state emitters have emerged as promising candidates with their prospects for on chip integration as quantum nodes and sources of coherent photons for connecting these nodes. Under strongly driving resonant laser field, such quantum emitter can exhibit quantum behavior such as Autler-Townes splitting and Mollow triplet spectrum. Here we demonstrate coherent control of a strongly driven optical transition in silicon vacancy (SiV) center in diamond. Rapid optical detection of photons enabled the observation of time resolved coherent Rabi oscillations and the Mollow triplet from an optical transition of a single SiV defect. Detection with a probing transition further confirmed Autler-Townes splitting generated by a strong laser field. Coherence time of the emitted photons is shown to be comparable to its lifetime and robust under very strong driving laser field, which is promising for generation of indistinguishable photons.

quant-ph↗

arXiv2017

Integration of Preferences in Decomposition Multi-Objective Optimization

Most existing studies on evolutionary multi-objective optimization focus on approximating the whole Pareto-optimal front. Nevertheless, rather than the whole front, which demands for too many points (especially in a high-dimensional space), the decision maker might only interest in a partial region, called the region of interest. In this case, solutions outside this region can be noisy to the decision making procedure. Even worse, there is no guarantee that we can find the preferred solutions when tackling problems with complicated properties or a large number of objectives. In this paper, we develop a systematic way to incorporate the decision maker's preference information into the decomposition-based evolutionary multi-objective optimization methods. Generally speaking, our basic idea is a non-uniform mapping scheme by which the originally uniformly distributed reference points on a canonical simplex can be mapped to the new positions close to the aspiration level vector specified by the decision maker. By these means, we are able to steer the search process towards the region of interest either directly or in an interactive manner and also handle a large number of objectives. In the meanwhile, the boundary solutions can be approximated given the decision maker's requirements. Furthermore, the extent of the region of the interest is intuitively understandable and controllable in a closed form. Extensive experiments, both proof-of-principle and on a variety of problems with 3 to 10 objectives, fully demonstrate the effectiveness of our proposed method for approximating the preferred solutions in the region of interest.

cs.NE↗

arXiv2017

Zeeman splitting via spin-valley-layer coupling in bilayer MoTe2

Atomically thin transition metal dichalcogenides (TMDs) possess coupling of spin and valley degrees of freedom, making them promising for spin-valleytronics. ln monolayer TMDs, the emission helicity is locked to the valleys as a consequence of spin-orbit coupling and inversion symmetry breaking, leading to a valley analog of Zeeman effect in presence of out-of-plane magnetic field. As inversion symmetry is recovered in bilayers, the emission helicity should no longer be locked to the valleys. Here we show that Zeeman splitting, however, still persists in bilayers, as a result of an additional degree of freedom viz., the layer pseudospin and spin-valley-layer locking. In contrast to monolayer, Zeeman splitting here exists without lifting valley degeneracy. The degree of circularly polarized photoluminescence can be tuned with magnetic field from $-27\%$ to $27\%$. Our results demonstrate the control of degree of freedom in bilayer with magnetic field, which, together with previous electric field control, makes bilayer a promising platform for spin-valley quantum gates based on magnetoelectric effects.

cond-mat.mtrl-sci↗

arXiv2016

Decoupling of a Neutron Interferometer from Temperature Gradients

Neutron interferometry enables precision measurements that are typically operated within elaborate, multi-layered facilities which provide substantial shielding from environmental noise. These facilities are necessary to maintain the coherence requirements in a perfect crystal neutron interferometer which is extremely sensitive to local environmental conditions such as temperature gradients across the interferometer, external vibrations, and acoustic waves. The ease of operation and breadth of applications of perfect crystal neutron interferometry would greatly benefit from a mode of operation which relaxes these stringent isolation requirements. Here, the INDEX Collaboration and National Institute of Standards and Technology demonstrates the functionality of a neutron interferometer in vacuum and characterize the use of a compact vacuum chamber enclosure as a means to isolate the interferometer from spatial temperature gradients and time-dependent temperature fluctuations. The vacuum chamber is found to have no depreciable effect on the performance of the interferometer (contrast) while improving system stability, thereby showing that it is feasible to replace large temperature isolation and control systems with a compact vacuum enclosure for perfect crystal neutron interferometry.

physics.ins-det↗