arXiv subjects

Chenyang Li

Publications and source records attributed to Chenyang Li.

103 records · Page 6Linked to original sources

arXiv2020

Tagging idea in continuous variable quantum key distribution and its application

Despite tremendous theoretical and experimental progress in continuous variable (CV) quantum key distribution (QKD), its security has not been rigorously established for practical systems with multiple imperfections. The idea of tagging is widely used in security proofs of discrete variable quantum key distribution with imperfect devices. In this paper, we generalize the tagging idea from discrete variable to continuous variable. Based on untagged signals, we prove the security of the imperfect quantum key distribution system in the most conservative case. By introducing a generic imperfection model, we can evaluate and further incorporate multiple imperfections in the different stages such as modulation, channel and detection. Finally, with this generic model and tagging idea, we can prove the security of continuous variable key distribution system with multiple imperfections. Our case study shows our proofs are able to provide secure keys in the presence of both modulation and detection flaws.

quant-ph↗

arXiv2020

Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph

Knowledge graphs (KGs) composed of users, objects, and tags are widely used in web applications ranging from E-commerce, social media sites to news portals. This paper concentrates on an attractive application which aims to predict the object-tag links in the KG for better tag recommendation and object explanation. When predicting the object-tag links, both the first-order and high-order proximities between entities in the KG propagate essential similarity information for better prediction. Most existing methods focus on preserving the first-order proximity between entities in the KG. However, they cannot capture the high-order proximities in an explicit way, and the adopted margin-based criterion cannot measure the first-order proximity on the global structure accurately. In this paper, we propose a novel approach named Dual Graph Embedding (DGE) that models both the first-order and high-order proximities in the KG via an auto-encoding architecture to facilitate better object-tag relation inference. Here the dual graphs contain an object graph and a tag graph that explicitly depict the high-order object-object and tag-tag proximities in the KG. The dual graph encoder in DGE then encodes these high-order proximities in the dual graphs into entity embeddings. The decoder formulates a skip-gram objective that maximizes the first-order proximity between observed object-tag pairs over the global proximity structure. With the supervision of the decoder, the embeddings derived by the encoder will be refined to capture both the first-order and high-order proximities in the KG for better link prediction. Extensive experiments on three real-world datasets demonstrate that DGE outperforms the state-of-the-art methods.

cs.IR↗

arXiv2020

Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs

Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has been receiving attention because of its popular applications such as recommendations. How to make efficient relation inference with few observed links is the main problem on multiple bipartite graphs. Most existing approaches attempt to solve the sparsity problem via learning shared representations to integrate knowledge from multi-source data for shared entities. However, they merely model the correlations from one aspect (e.g. distribution, representation), and cannot impose sufficient constraints on different relations of the shared entities. One effective way of modeling the multi-domain data is to learn the joint distribution of the shared entities across domains.In this paper, we propose Collaborative Adversarial Learning (CAL) that explicitly models the joint distribution of the shared entities across multiple bipartite graphs. The objective of CAL is formulated from a variational lower bound that maximizes the joint log-likelihoods of the observations. In particular, CAL consists of distribution-level and feature-level alignments for knowledge from multiple bipartite graphs. The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs. Extensive experiments on two real-world datasets have shown that the proposed model outperforms the existing methods.

cs.IR↗

arXiv2020

Connected three-body terms in single-reference unitary many-body theories: Iterative and perturbative approximations

This work introduces various approaches to include connected three-body terms in unitary many-body theories, focusing a representative example on the driven similarity renormalization group (DSRG). Starting from the least approximate method - the linearized DSRG truncated to one-, two-, and three-body operators [LDSRG(3)] - we develop several approximate LDSRG(3) models with reduced computational cost. Through a perturbative analysis, we motivate a family of iterative LDSRG(3)-$n$ and -$n'$ ($n=1,2,3,4$) methods that contain a subset of the LDSRG(3) diagrams. Among these variants, the LDSRG(3)-2 scheme has the same computational complexity of coupled cluster theory with singles, doubles, and triples (CCSDT), but it outperforms CCSDT in the accuracy of the predicted correlation energies. We also propose and implement two perturbative triples corrections based on the linearized DSRG truncated to one- and two-body operators augmented with recursive quadratic commutators [qDSRG(2)]. The resulting qDSRG(2)+(T) approach matches the accuracy of the "gold-standard" coupled cluster theory with singles, doubles, and perturbative triples model on the energetics of twenty-eight closed-shell atoms and small molecules.

physics.chem-ph↗

arXiv2019

Small Angle and Non-Monotonic Behavior of the Thermal Conductivity in Twisted Bilayer Graphene

Nothing is known about the thermal conductivity in twisted bilayer graphene (TBG) at small twist angles, and how it approaches its aligned value as the twist angle approaches 0 degrees. To provide insight into these questions, we perform large scale non-equilibrium molecular dynamics calculations on commensurate TBG structures with angles down to 1.87 degrees. The results show a smooth, non-monotonic behavior of the thermal conductivity with respect to the commensurate lattice constant. As the commensurate lattice constant increases, the thermal conductivity initially decreases by 50%, and then it returns to 90% of its aligned value as the angle is reduced to 1.89 degrees. These same qualitative trends are followed by the trends in the shear elastic constant, the wrinkling intensity, and the out-of-plane ZA2 phonon frequency. The picture that emerges of the physical mechanism governing the thermal conductivity is that misorientation reduces the shear elastic constant; the reduced shear elastic constant enables greater wrinkling; and the greater wrinkling reduces the thermal conductivity. The small-angle behavior of the thermal conductivity raises the question of how do response functions approach their aligned values as the twist angle approaches 0 degrees. Is the approach gradual, discontinuous, or a combination of the two?

physics.comp-ph↗

arXiv2019

Improving the efficiency of the multireference driven similarity renormalization group via sequential transformation, density fitting, and the non-interacting virtual orbital approximation

This study examines several techniques to improve the efficiency of the linearized multireference driven similarity renormalization group truncated to one- and two-body operators [MR-LDSRG(2)]. We propose a sequential MR-LDSRG(2) [sq-MR-LDSRG(2)] approach, in which one-body rotations are folded exactly into the Hamiltonian. This new approach is combined with density fitting (DF) to reduce the storage cost of two-electron integrals. To further avoid the storage of large four-index intermediates, we propose a non-interacting virtual orbit (NIVO) approximation in which tensor blocks labeled by three and four virtual indices are neglected. The NIVO approximation reduces the computational cost prefactor of the MR-LDSRG(2) bringing it closer to that of coupled cluster with singles and doubles (CCSD). We test the effect of the DF and NIVO approximations on the MR-LDSRG(2) and sq-MR-LDSRG(2) methods by computing properties of eight diatomic molecules. The diatomic constants obtained by DF-sq-MR-LDSRG(2)+NIVO are found to be as accurate as those from the original MR-LDSRG(2) and coupled cluster theory with singles, doubles, and perturbative triples. Finally, we demonstrate that the DF-sq-MR-LDSRG(2)+NIVO scheme can be applied to chemical systems with more than 550 basis functions by computing the automerization energy of cyclobutadiene with a quintuple-zeta basis set. The predicted automerization energy is found similar to the values computed with Mukherjee's state-specific multireference coupled cluster theory with singles and doubles.

physics.chem-ph↗

arXiv2018

Skeleton-based Gesture Recognition Using Several Fully Connected Layers with Path Signature Features and Temporal Transformer Module

The skeleton based gesture recognition is gaining more popularity due to its wide possible applications. The key issues are how to extract discriminative features and how to design the classification model. In this paper, we first leverage a robust feature descriptor, path signature (PS), and propose three PS features to explicitly represent the spatial and temporal motion characteristics, i.e., spatial PS (S_PS), temporal PS (T_PS) and temporal spatial PS (T_S_PS). Considering the significance of fine hand movements in the gesture, we propose an "attention on hand" (AOH) principle to define joint pairs for the S_PS and select single joint for the T_PS. In addition, the dyadic method is employed to extract the T_PS and T_S_PS features that encode global and local temporal dynamics in the motion. Secondly, without the recurrent strategy, the classification model still faces challenges on temporal variation among different sequences. We propose a new temporal transformer module (TTM) that can match the sequence key frames by learning the temporal shifting parameter for each input. This is a learning-based module that can be included into standard neural network architecture. Finally, we design a multi-stream fully connected layer based network to treat spatial and temporal features separately and fused them together for the final result. We have tested our method on three benchmark gesture datasets, i.e., ChaLearn 2016, ChaLearn 2013 and MSRC-12. Experimental results demonstrate that we achieve the state-of-the-art performance on skeleton-based gesture recognition with high computational efficiency.

cs.CV↗

arXiv2018

Secure quantum communication in the presence of phase and polarization dependent loss

Silicon photonics holds the promise of the miniaturization of quantum communication devices. Recently, silicon chip optical transmitters for quantum key distribution (QKD) have been built and demonstrated experimentally. Nonetheless, these silicon chips suffer substantial polarization and phase dependent loss which, if unchecked, could compromise the security of QKD systems. Here, we first restore the security by regarding the single photons without phase and polarization dependence as untagged and secure qubits. Next, by using a post-selection technique, one could implement a secure QKD protocol that provides a high key generation rate even in the presence of severe phase and polarization dependent loss. Our solution is simple to realize in a practical experiment as it does not require any hardware modification.

quant-ph↗

arXiv2017

Driven similarity renormalization group: Third-order multireference perturbation theory

A third-order multireference perturbation theory based on the driven similarity renormalization group approach (DSRG-MRPT3) is presented. The DSRG-MRPT3 method has several appealing features: a) it is intruder free, b) it is size consistent, c) it leads to a non-iterative algorithm with ${\cal O}(N^6)$ scaling, and d) it includes reference relaxation effects. The DSRG-MRPT3 scheme is benchmarked on the potential energy curves of F$_2$, H$_2$O$_2$, C$_2$H$_6$, and N$_2$ along the F-F, O-O, C-C, and N-N bond dissociation coordinates, respectively. The nonparallelism errors of DSRG-MRPT3 are consistent to those of CASPT3 and MRCISD, and show significant improvements over those obtained from DSRG second-order multireference perturbation theory. Our efficient implementation of the DSRG-MRPT3 based on factorized electron repulsion integrals enables studies of medium-sized open-shell organic compounds. This point is demonstrated with computations of the singlet-triplet splitting ($Δ_{\rm ST} = E_{\rm T} - E_{\rm S}$) of 9,10-anthracyne. At the DSRG-MRPT3 level of theory, our best estimate of the adiabatic $Δ_{\rm ST}$ is 3.9 kcal mol$^{-1}$, a value that is within 0.1 kcal mol$^{-1}$ from multireference coupled cluster results.

physics.chem-ph↗

arXiv2016

An integral-factorized implementation of the driven similarity renormalization group second-order multireference perturbation theory

We report an efficient implementation of a second-order multireference perturbation theory based on the driven similarity renormalization group (DSRG-MRPT2) [C. Li and F. A. Evangelista, J. Chem. Theory Comput. 11, 2097 (2015)]. Our implementation employs factorized two-electron integrals to avoid storage of large four-index intermediates. It also exploits the block structure of the reference density matrices to reduce the computational cost to that of second-order Møller$-$Plesset perturbation theory. Our new DSRG-MRPT2 implementation is benchmarked on ten naphthyne isomers using basis sets up to quintuple-$ζ$ quality. We find that the singlet-triplet splittings ($Δ_\text{ST}$) of the naphthyne isomers strongly depend on the equilibrium structures. For a consistent set of geometries, the $Δ_\text{ST}$ values predicted by the DSRG-MRPT2 are in good agreements with those computed by the reduced multireference coupled cluster theory with singles, doubles, and perturbative triples.

physics.chem-ph↗

arXiv2016

Towards numerically robust multireference theories: The driven similarity renormalization group truncated to one- and two-body operators

The first nonperturbative version of the multireference driven similarity renormalization group (MR-DSRG) theory [C. Li and F. A. Evangelista, J. Chem. Theory Comput. $\mathbf{11}$, 2097 (2015)] is introduced. The renormalization group structure of the MR-DSRG equations ensures numerical robustness and avoidance of the intruder state problem, while the connected nature of the amplitude and energy equations guarantees size consistency and extensivity. We approximate the MR-DSRG equations by keeping only one- and two-body operators and using a linearized recursive commutator approximation of the Baker--Campbell--Hausdorff expansion [T. Yanai and G. K.-L. Chan, J. Chem. Phys. $\mathbf{124}$, 194106 (2006)]. The resulting MR-LDSRG(2) equations contain only 39 terms and scales as ${\cal O}(N^2 N_{\rm P}^2 N_{\rm H}^2)$ where $N_{\rm H}$, $N_{\rm P}$, and $N$ correspond to the number of hole, particle, and total orbitals, respectively. Benchmark MR-LDSRG(2) computations on the hydrogen fluoride and molecular nitrogen binding curves and the singlet-triplet splitting of $p$-benzyne yield results comparable in accuracy to those from multireference configuration interaction, Mukherjee multireference coupled cluster theory, and internally-contracted multireference coupled cluster theory.

physics.chem-ph↗

arXiv2015

Stability and scalability of piezoelectric flags

We investigate the effect of piezoelectric (PZT) material on the flutter speed, vibration mode and frequency, and energy harvesting power and efficiency of a flexible flag in various fluids. We develop a fully coupled fluid-solid-electric model by combining the inviscid vortex sheet model with a linear electro-mechanical coupling model. A resistance only circuit and a resonant resistance-inductance (RL) circuit are considered. For a purely resistive circuit, an increased electro-mechanical coupling factor results in an increased flutter speed, vibration frequency, averaged electric power and efficiency. A consistent optimal resistance is found that maximizes the flutter speed and the energy harvesting power. For a resonant RL circuit, by tuning the inductance to match the circuit frequency to the flag's vibration frequency, the flutter speed can be greatly decreased, and a larger averaged power and efficiency are obtained. We also consider a model scale set-up with several commonly used commercial materials for operating in air and water. Typical ranges of dimensionless parameters are obtained for four types of material that span a wide range of solid density and rigidity values. We find that the resistance only circuit is more effective when the flag is placed in a lighter fluid (e.g. air), while the RL circuit is able to reduce the flutter speed when the flag is placed in a heavier fluid (e.g. water).

physics.flu-dyn↗

arXiv2012

An Adaptive Online Ad Auction Scoring Algorithm for Revenue Maximization

Sponsored search becomes an easy platform to match potential consumers' intent with merchants' advertising. Advertisers express their willingness to pay for each keyword in terms of bids to the search engine. When a user's query matches the keyword, the search engine evaluates the bids and allocates slots to the advertisers that are displayed along side the unpaid algorithmic search results. The advertiser only pays the search engine when its ad is clicked by the user and the price-per-click is determined by the bids of other competing advertisers.

cs.GT↗