Remote state preparation in higher dimension and the parallelizable manifold Sn−1S^{n-1}

This paper proves that the remote state preparation (RSP) scheme in real Hilbert space can only be implemented when the dimension of the space is 2,4 or 8. This fact is shown to be related to the parallelazablity of the $n$-1 dimensional sphere $S^{n-1}$. When the dimension is 4 and 8 the generalized scheme is explicitly presented. It is also shown that for a given state with components having the same norm, RSP can be generalized to arbitrary dimension case.Comment: 8pages, no figures, late

Enhancing the long-term performance of recommender system

Recommender system is a critically important tool in online commercial system and provide users with personalized recommendation on items. So far, numerous recommendation algorithms have been made to further improve the recommendation performance in a single-step recommendation, while the long-term recommendation performance is neglected. In this paper, we proposed an approach called Adjustment of Recommendation List (ARL) to enhance the long-term recommendation accuracy. In order to observe the long-term accuracy, we developed an evolution model of network to simulate the interaction between the recommender system and user's behaviour. The result shows that not only long-term recommendation accuracy can be enhanced significantly but the diversity of item in online system maintains healthy. Notably, an optimal parameter n* of ARL existed in long-term recommendation, indicating that there is a trade-off between keeping diversity of item and user's preference to maximize the long-term recommendation accuracy. Finally, we confirmed that the optimal parameter n* is stable during evolving network, which reveals the robustness of ARL method.Comment: 16 pages, 10 figure

A Gauss-Seidel Iterative Thresholding Algorithm for lq Regularized Least Squares Regression

In recent studies on sparse modeling, $l_q$ ($0<q<1$) regularized least squares regression ($l_q$LS) has received considerable attention due to its superiorities on sparsity-inducing and bias-reduction over the convex counterparts. In this paper, we propose a Gauss-Seidel iterative thresholding algorithm (called GAITA) for solution to this problem. Different from the classical iterative thresholding algorithms using the Jacobi updating rule, GAITA takes advantage of the Gauss-Seidel rule to update the coordinate coefficients. Under a mild condition, we can justify that the support set and sign of an arbitrary sequence generated by GAITA will converge within finite iterations. This convergence property together with the Kurdyka-{\L}ojasiewicz property of ($l_q$LS) naturally yields the strong convergence of GAITA under the same condition as above, which is generally weaker than the condition for the convergence of the classical iterative thresholding algorithms. Furthermore, we demonstrate that GAITA converges to a local minimizer under certain additional conditions. A set of numerical experiments are provided to show the effectiveness, particularly, much faster convergence of GAITA as compared with the classical iterative thresholding algorithms.Comment: 35 pages, 11 figure

Practical security of continuous-variable quantum key distribution with reduced optical attenuation

In a practical CVQKD system, the optical attenuator can adjust the Gaussian-modulated coherent states and the local oscillator signal to an optimal value for guaranteeing the security of the system and optimizing the performance of the system. However, the performance of the optical attenuator may deteriorate due to the intentional and unintentional damage of the device. In this paper, we investigate the practical security of a CVQKD system with reduced optical attenuation. We find that the secret key rate of the system may be overestimated based on the investigation of parameter estimation under the effects of reduced optical attenuation. This opens a security loophole for Eve to successfully perform an intercept-resend attack in a practical CVQKD system. To close this loophole, we add an optical fuse at Alice's output port and design a scheme to monitor the level of optical attenuation in real time, which can make the secret key rate of the system evaluated precisely. The analysis shows that these countermeasures can effectively resist this potential attack.Comment: 9 pages, 8 figure

Low-density locality-sensitive hashing boosts metagenomic binning

Metagenomic binning is an essential task in analyzing metagenomic sequence datasets. To analyze structure or function of microbial communities from environmental samples, metagenomic sequence fragments are assigned to their taxonomic origins. Although sequence alignment algorithms can readily be used and usually provide high-resolution alignments and accurate binning results, the computational cost of such alignment-based methods becomes prohibitive as metagenomic datasets continue to grow. Alternative compositional-based methods, which exploit sequence composition by profiling local short k-mers in fragments, are often faster but less accurate than alignment-based methods. Inspired by the success of linear error correcting codes in noisy channel communication, we introduce Opal, a fast and accurate novel compositional-based binning method. It incorporates ideas from Gallager's low-density parity-check code to design a family of compact and discriminative locality-sensitive hashing functions that encode long-range compositional dependencies in long fragments. By incorporating the Gallager LSH functions as features in a simple linear SVM, Opal provides fast, accurate and robust binning for datasets consisting of a large number of species, even with mutations and sequencing errors. Opal not only performs up to two orders of magnitude faster than BWA, an alignment-based binning method, but also achieves improved binning accuracy and robustness to sequencing errors. Opal also outperforms models built on traditional k-mer profiles in terms of robustness and accuracy. Finally, we demonstrate that we can effectively use Opal in the "coarse search" stage of a compressive genomics pipeline to identify a much smaller candidate set of taxonomic origins for a subsequent alignment-based method to analyze, thus providing metagenomic binning with high scalability, high accuracy and high resolution.Comment: RECOMB 2016. Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract appearing here is slightly shorter than the one in the PDF fil

Weighted finite impulse response filter for chromatic dispersion equalization in coherent optical fiber communication systems

Time-domain chromatic dispersion (CD) equalization using finite impulse response (FIR) filter is now a common approach for coherent optical fiber communication systems. The complex weights of FIR filter taps are calculated from a truncated impulse response of the CD transfer function, and the modulus of the complex weights is constant. In our work, we take the limited bandwidth of a single channel signal into account and propose weighted FIR filters to improve the performance of CD equalization. A raised cosine FIR filter and a Gaussian FIR filter are investigated in our work. The optimization of raised cosine FIR filter and Gaussian FIR filter are made in terms of the EVM of QPSK, 16QAM and 32QAM coherent detection signal. The results demonstrate that the optimized parameters of the weighted filters are independent of the modulation format, symbol rate and the length of transmission fiber. With the optimized weighted FIR filters, the EVM of CD equalization signal is decreased significantly. The principle of weighted FIR filter can also be extended to other symmetric functions as weighted functions

Implement Liquid Democracy on Ethereum: A Fast Algorithm for Realtime Self-tally Voting System

We study the liquid democracy problem, where each voter can either directly vote to a candidate or delegate his voting power to a proxy. We consider the implementation of liquid democracy on the blockchain through Ethereum smart contract and to be compatible with the realtime self-tallying property, where the contract itself can record ballots and update voting status upon receiving each voting massage. A challenge comes due to the gas fee limitation of Ethereum mainnet, that the number of instruction for processing a voting massage can not exceed a certain amount, which restrict the application scenario with respect to algorithms whose time complexity is linear to the number of voters. We propose a fast algorithm to overcome the challenge, such that i) shifts the on-chain initialization to off-chain and ii) the on-chain complexity for processing each voting massage is O(\log n), where n is the number of voters

Pose-adaptive Hierarchical Attention Network for Facial Expression Recognition

Multi-view facial expression recognition (FER) is a challenging task because the appearance of an expression varies in poses. To alleviate the influences of poses, recent methods either perform pose normalization or learn separate FER classifiers for each pose. However, these methods usually have two stages and rely on good performance of pose estimators. Different from existing methods, we propose a pose-adaptive hierarchical attention network (PhaNet) that can jointly recognize the facial expressions and poses in unconstrained environment. Specifically, PhaNet discovers the most relevant regions to the facial expression by an attention mechanism in hierarchical scales, and the most informative scales are then selected to learn the pose-invariant and expression-discriminative representations. PhaNet is end-to-end trainable by minimizing the hierarchical attention losses, the FER loss and pose loss with dynamically learned loss weights. We validate the effectiveness of the proposed PhaNet on three multi-view datasets (BU-3DFE, Multi-pie, and KDEF) and two in-the-wild FER datasets (AffectNet and SFEW). Extensive experiments demonstrate that our framework outperforms the state-of-the-arts under both within-dataset and cross-dataset settings, achieving the average accuracies of 84.92\%, 93.53\%, 88.5\%, 54.82\% and 31.25\% respectively.Comment: 12 pages, 15 figure

Sound transmission of periodic composite structure lined with porous core: rib-stiffened double panel case

Porous materials are effective for the isolation of sound with medium to high frequencies, while periodic structures are promising for low to medium frequencies. In the present work, we study the sound insulation of a periodically rib-stiffened double-panel with porous lining to reveal the effect of combining the two characters above. The theoretical development of the periodic composite structure, which is based on the space harmonic series and Biot theory, is included. The system equations are subsequently solved numerically by employing a precondition method with a truncation procedure. This theoretical and numerical framework is validated with results from both theoretical and finite element methods. The parameter study indicates that the presence of ribs can lower the overall sound insulation, although a direct transfer path is absent. Despite the unexpected model results, the method proposed here, which combines poroelastic modeling and periodic structures semi-analytically, can be promising in broadband sound modulation.Comment: 36 pages, 17 figures and 3 appendixe

Data Augmentation for Spoken Language Understanding via Pretrained Models

The training of spoken language understanding (SLU) models often faces the problem of data scarcity. In this paper, we put forward a data augmentation method with pretrained language models to boost the variability and accuracy of generated utterances. Furthermore, we investigate and propose solutions to two previously overlooked scenarios of data scarcity in SLU: i) Rich-in-Ontology: ontology information with numerous valid dialogue acts are given; ii) Rich-in-Utterance: a large number of unlabelled utterances are available. Empirical results show that our method can produce synthetic training data that boosts the performance of language understanding models in various scenarios.Comment: 6 pages, 1 figur