arXiv subjects

Li Li

Publications and source records attributed to Li Li.

At least 739 records · Page 41Linked to original sources

arXiv2017

An Efficient Four-Parameter Affine Motion Model for Video Coding

In this paper, we study a simplified affine motion model based coding framework to overcome the limitation of translational motion model and maintain low computational complexity. The proposed framework mainly has three key contributions. First, we propose to reduce the number of affine motion parameters from 6 to 4. The proposed four-parameter affine motion model can not only handle most of the complex motions in natural videos but also save the bits for two parameters. Second, to efficiently encode the affine motion parameters, we propose two motion prediction modes, i.e., advanced affine motion vector prediction combined with a gradient-based fast affine motion estimation algorithm and affine model merge, where the latter attempts to reuse the affine motion parameters (instead of the motion vectors) of neighboring blocks. Third, we propose two fast affine motion compensation algorithms. One is the one-step sub-pixel interpolation, which reduces the computations of each interpolation. The other is the interpolation-precision-based adaptive block size motion compensation, which performs motion compensation at the block level rather than the pixel level to reduce the interpolation times. Our proposed techniques have been implemented based on the state-of-the-art high efficiency video coding standard, and the experimental results show that the proposed techniques altogether achieve on average 11.1% and 19.3% bits saving for random access and low delay configurations, respectively, on typical video sequences that have rich rotation or zooming motions. Meanwhile, the computational complexity increases of both encoder and decoder are within an acceptable range.

cs.MM↗

arXiv2017

Hyperscaling Violating Solutions in Generalised EMD Theory

This short note is devoted to deriving scaling but hyperscaling violating solutions in a generalised Einstein-Maxwell-Dilaton theory with an arbitrary number of scalars and vectors. We obtain analytic solutions in some special case and discuss the physical constraints on the allowed parameter range in order to have a well-defined holographic ground-state solution.

hep-th↗

arXiv2017

Effect of surface morphology and magnetic impurities on the electronic structure in cobalt-doped BaFe2As2 superconductors

Combined scanning tunneling microscopy, spectroscopy and local barrier height (LBH) studies show that low-temperature-cleaved optimally-doped Ba(Fe1-xCox)2As2 crystals with x=0.06, with Tc = 22 K, have complicated morphologies. Although the cleavage surface and hence the morphologies are variable, the superconducting gap maps show the same gap widths and nanometer size inhomogeneities irrelevant to the morphology. Based on the spectroscopy and LBH maps, the bright patches and dark stripes in the morphologies are identified as Ba and As dominated surface terminations, respectively. Magnetic impurities, possibly due to cobalt or Fe atoms, are believed to create local in-gap state and in addition suppress the superconducting coherence peaks. This study will clarify the confusion on the cleavage surface terminations of the Fe-based superconductors, and its relation with the electronic structures.

cond-mat.supr-con↗

arXiv2016

Adaptive Block Coordinate DIRECT algorithm

DIviding RECTangles (DIRECT) is an efficient and popular method in dealing with bound constrained optimization problems. However, DIRECT suffers from dimension curse, since its computational complexity soars when dimension increases. Besides, DIRECT also converges slowly when the objective function is flat. In this paper, we propose a coordinate DIRECT algorithm, which coincides with the spirits of other coordinate update algorithms. We transform the original problem into a series of sub-problems, where only one or several coordinates are selected to optimize and the rest keeps fixed. For each sub-problem, coordinately dividing the feasible domain enjoys low computational burden. Besides, we develop adaptive schemes to keep the efficiency and flexibility to tackle different functions. Specifically, we use block coordinate update, of which the size could be adaptively selected, and we also employ sequential quadratic programming (SQP) to conduct the local search to efficiently accelerate the convergence even when the objective function is flat. With these techniques, the proposed algorithm achieves promising performance on both efficiency and accuracy in numerical experiments.

math.OC↗

arXiv2016

Pseudo Sequence based 2-D hierarchical reference structure for Light-Field Image Compression

In this paper, we present a novel pseudo sequence based 2-D hierarchical reference structure for light-field image compression. In the proposed scheme, we first decompose the light-field image into multiple views and organize them into a 2-D coding structure according to the spatial coordinates of the corresponding microlens. Then we mainly develop three technologies to optimize the 2-D coding structure. First, we divide all the views into four quadrants, and all the views are encoded one quadrant after another to reduce the reference buffer size as much as possible. Inside each quadrant, all the views are encoded hierarchically to fully exploit the correlations between different views. Second, we propose to use the distance between the current view and its reference views as the criteria for selecting better reference frames for each inter view. Third, we propose to use the spatial relative positions between different views to achieve more accurate motion vector scaling. The whole scheme is implemented in the reference software of High Efficiency Video Coding. The experimental results demonstrate that the proposed novel pseudo-sequence based 2-D hierarchical structure can achieve maximum 14.2% bit-rate savings compared with the state-of-the-art light-field image compression method.

cs.MM↗

arXiv2016

Holographic Pair and Charge Density Waves

We examine a holographic model in which a U(1) symmetry and translational invariance are broken spontaneously at the same time. Our construction provides an example of a system with pair-density wave order, in which the superconducting order parameter is spatially modulated but has a zero average. In addition, the charge density oscillates at twice the frequency of the scalar condensate. Depending on the choice of parameters, the model also admits a state with co-existing superconducting and charge density wave orders, in which the scalar condensate has a uniform component.

hep-th↗

arXiv2016

Experimental Test of Irreducible Four-Qubit Greenberger-Horne-Zeilinger Paradox

Bell's theorem shows a profound contradiction between local realism and quantum mechanics on the level of statistical predictions. It does not involve directly Einstein-Podolsky-Rosen (EPR) correlations. The paradox of Greenberger-Horne-Zeilinger (GHZ) disproves directly the concept of EPR elements of reality, based on the EPR correlations, in an all-versus-nothing way. A three-qubit experimental demonstration of the GHZ paradox was achieved nearly twenty years ago, and followed by demonstrations for more qubits. Still, the GHZ contradictions underlying the tests can be reduced to three-qubit one. We show an irreducible four-qubit GHZ paradox, and report its experimental demonstration. The reducibility loophole is closed. The bound of a three-setting per party Bell-GHZ inequality is violated by $7σ$. The fidelity of the GHZ state was around $81\%$, and an entanglement witness reveals a violation of the separability threshold by $19σ$.

quant-ph↗

arXiv2016

Experimental ten-photon entanglement

Quantum entanglement among multiple spatially separated particles is of fundamental interest, and can serve as central resources for studies in quantum nonlocality, quantum-to-classical transition, quantum error correction, and quantum simulation. The ability of generating an increasing number of entangled particles is an important benchmark for quantum information processing. The largest entangled states were previously created with fourteen trapped ions, eight photons, and five superconducting qubits. Here, based on spontaneous parametric down-converted two-photon entanglement source with simultaneously a high brightness of ~12 MHz/W, a collection efficiency of ~70% and an indistinguishability of ~91% between independent photons, we demonstrate, for the first time, genuine and distillable entanglement of ten single photons under different pump power. Our work creates a state-of-the-art platform for multi-photon experiments, and provide enabling technologies for challenging optical quantum information tasks such as high-efficiency scattershot boson sampling with many photons.

quant-ph↗

arXiv2016

Maximizing Investment Value of Small-Scale PV in a Smart Grid Environment

Determining the optimal size and orientation of small-scale residential based PV arrays will become increasingly complex in the future smart grid environment with the introduction of smart meters and dynamic tariffs. However consumers can leverage the availability of smart meter data to conduct a more detailed exploration of PV investment options for their particular circumstances. In this paper, an optimization method for PV orientation and sizing is proposed whereby maximizing the PV investment value is set as the defining objective. Solar insolation and PV array models are described to form the basis of the PV array optimization strategy. A constrained particle swarm optimization algorithm is selected due to its strong performance in non-linear applications. The optimization algorithm is applied to real-world metered data to quantify the possible investment value of a PV installation under different energy retailers and tariff structures. The arrangement with the highest value is determined to enable prospective small-scale PV investors to select the most cost-effective system.

math.OC↗

arXiv2016

An image compression and encryption scheme based on deep learning

Stacked Auto-Encoder (SAE) is a kind of deep learning algorithm for unsupervised learning. Which has multi layers that project the vector representation of input data into a lower vector space. These projection vectors are dense representations of the input data. As a result, SAE can be used for image compression. Using chaotic logistic map, the compression ones can further be encrypted. In this study, an application of image compression and encryption is suggested using SAE and chaotic logistic map. Experiments show that this application is feasible and effective. It can be used for image transmission and image protection on internet simultaneously.

cs.CV↗

arXiv2016

Full reconstruction of a 14-qubit state within four hours

Full quantum state tomography (FQST) plays a unique role in the estimation of the state of a quantum system without \emph{a priori} knowledge or assumptions. Unfortunately, since FQST requires informationally (over)complete measurements, both the number of measurement bases and the computational complexity of data processing suffer an exponential growth with the size of the quantum system. A 14-qubit entangled state has already been experimentally prepared in an ion trap, and the data processing capability for FQST of a 14-qubit state seems to be far away from practical applications. In this paper, the computational capability of FQST is pushed forward to reconstruct a 14-qubit state with a run time of only 3.35 hours using the linear regression estimation (LRE) algorithm, even when informationally overcomplete Pauli measurements are employed. The computational complexity of the LRE algorithm is first reduced from $O(10^{19})$ to $O(10^{15})$ for a 14-qubit state, by dropping all the zero elements, and its computational efficiency is further sped up by fully exploiting the parallelism of the LRE algorithm with parallel Graphic Processing Unit (GPU) programming. Our result can play an important role in quantum information technologies with large quantum systems.

quant-ph↗

arXiv2016

Pure density functional for strong correlations and the thermodynamic limit from machine learning

We use density-matrix renormalization group, applied to a one-dimensional model of continuum Hamiltonians, to accurately solve chains of hydrogen atoms of various separations and numbers of atoms. We train and test a machine-learned approximation to $F[n]$, the universal part of the electronic density functional, to within quantum chemical accuracy. Our calculation (a) bypasses the standard Kohn-Sham approach, avoiding the need to find orbitals, (b) includes the strong correlation of highly-stretched bonds without any specific difficulty (unlike all standard DFT approximations) and (c) is so accurate that it can be used to find the energy in the thermodynamic limit to quantum chemical accuracy.

cond-mat.str-el↗

arXiv2016

Deep data mining in a real space: Separation of intertwined electronic responses in a lightly-doped BaFe2As2

Electronic interactions present in material compositions close to the superconducting dome play a key role in the manifestation of high-Tc superconductivity. In many correlated electron systems, however, the parent or underdoped states exhibit strongly inhomogeneous electronic landscape at the nanoscale that may be associated with competing, coexisting, or intertwined chemical disorder, strain, magnetic, and structural order parameters. Here we demonstrate an approach based on a combination of scanning tunneling microscopy/spectroscopy (STM/S) and advanced statistical learning for an automatic separation and extraction of statistically significant electronic behaviors in the spin density wave (SDW) regime of a lightly (~1%) gold-doped BaFe2As2. We show that the decomposed STS spectral features have a direct relevance to fundamental physical properties of the system, such as SDW-induced gap, pseudogap-like state, and impurity resonance states.

cond-mat.mtrl-sci↗

arXiv2016

Quantum Criticality and DBI Magneto-resistance

We use the DBI action from string theory and holography to study the magneto-resistance at quantum criticality with hyperscaling violation. We find and analyze a rich class of scaling behaviors for the magneto-resistance. A special case describes the scaling results found in pnictides by Hayers et al. in~\cite{analytis}.

cond-mat.str-el↗

arXiv2016

A Fast Pseudo-Stochastic Sequential Cipher Generator Based on RBMs

Based on Restricted Boltzmann Machines (RBMs), an improved pseudo-stochastic sequential cipher generator is proposed. It is effective and efficient because of the two advantages: this generator includes a stochastic neural network that can perform the calculation in parallel, that is to say, all elements are calculated simultaneously; unlimited number of sequential ciphers can be generated simultaneously for multiple encryption schemas. The periodicity and the correlation of the output sequential ciphers meet the requirements for the design of encrypting sequential data. In the experiment, the generated sequential cipher is used to encrypt the image, and better performance is achieved in terms of the key space analysis, the correlation analysis, the sensitivity analysis and the differential attack. The experimental result is promising that could promote the development of image protection in computer security.

cs.CR↗

arXiv2016

ELM control with RMP: plasma response models and the role of edge peeling response

Resonant magnetic perturbations (RMP) have extensively been demonstrated as a plausible technique for mitigating or suppressing large edge localized modes (ELMs). Associated with this is a substantial amount of theory and modelling efforts during recent years. Various models describing the plasma response to the RMP fields have been proposed in the literature, and are briefly reviewed in this work. Despite their simplicity, linear response models can provide alternative criteria, than the vacuum field based criteria, for guiding the choice of the coil configurations to achieve the best control of ELMs. The role of the edge peeling response to the RMP fields is illustrated as a key indicator for the ELM mitigation in low collisionality plasmas, in various tokamak devices.

physics.plasm-ph↗

arXiv2016

Entanglement Swapping with Independent Sources over an Optical Fibre Network

Teleportation of an entangled state, known as entanglement swapping, plays an essential role in quantum communication and network.Here we report a field-test entanglement swapping experiment with two independent telecommunication band entangled photon-pair sources over the optical fibre network of Hefei city. The two sources are located at two nodes 12 km apart and the Bell-state measurement is performed in a third location which is connected to the two source nodes with 14.7 km and 10.6 km optical fibres. An average visibility of 79.9+/-4.8% is observed in our experiment, which is high enough to infer a violation of Bell inequality. With the entanglement swapping setup, we demonstrate a source independent quantum key distribution, which is also immune to any attack against detection in the measurement site.

quant-ph↗

arXiv2016

Watch out for This Commit! A Study of Influential Software Changes

One single code change can significantly influence a wide range of software systems and their users. For example, 1) adding a new feature can spread defects in several modules, while 2) changing an API method can improve the performance of all client programs. Developers often may not clearly know whether their or others' changes are influential at commit time. Rather, it turns out to be influential after affecting many aspects of a system later. This paper investigates influential software changes and proposes an approach to identify them early, i.e., immediately when they are applied. We first conduct a post-mortem analysis to discover existing influential changes by using intuitions such as isolated changes and changes referred by other changes in 10 open source projects. Then we re-categorize all identified changes through an open-card sorting process. Subsequently, we conduct a survey with 89 developers to confirm our influential change categories. Finally, from our ground truth we extract features, including metrics such as the complexity of changes, terms in commit logs and file centrality in co-change graphs, to build machine learning classifiers. The experiment results show that our prediction model achieves overall with random samples 86.8% precision, 74% recall and 80.4% F-measure respectively.

cs.SE↗