arXiv subjects

Chao Wang

Publications and source records attributed to Chao Wang.

At least 649 records · Page 36Linked to original sources

arXiv2019

Practical quantum key distribution with non-phase-randomized coherent states

Quantum key distribution (QKD) based on coherent states is well known for its implementation simplicity, but it suffers from loss-dependent attacks based on optimal unambiguous state discrimination. Crucially, previous research has suggested that coherent-state QKD is limited to short distances, typically below 100 km assuming standard optical fiber loss and system parameters. In this work, we propose a six-coherent-state phase-encoding QKD protocol that is able to tolerate the total loss of up to 38 dB assuming realistic system parameters, and up to 56 dB loss assuming zero noise. The security of the protocol is calculated using a recently developed security proof technique based on semi-definite programming, which assumes only the inner-product information of the encoded coherent states, the expected statistics, and that the measurement is basis-independent. Our results thus suggest that coherent-state QKD could be a promising candidate for high-speed provably-secure QKD.

quant-ph↗

arXiv2019

Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training

In this paper, we present a compression approach based on the combination of low-rank matrix factorization and quantization training, to reduce complexity for neural network based acoustic event detection (AED) models. Our experimental results show this combined compression approach is very effective. For a three-layer long short-term memory (LSTM) based AED model, the original model size can be reduced to 1% with negligible loss of accuracy. Our approach enables the feasibility of deploying AED for resource-constraint applications.

eess.AS↗

arXiv2019

Semi-supervised Acoustic Event Detection based on tri-training

This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset. Recent acoustic event detectors are based on large-scale neural networks, which are typically trained with huge amounts of labeled data. Labels for acoustic events are expensive to obtain, and relevant acoustic event audios can be limited, especially for rare events. In this paper we leverage an Internet-scale unlabeled dataset with potential domain shift to improve the detection of acoustic events. Based on the classic tri-training approach, our proposed method shows accuracy improvement over both the supervised training baseline, and semisupervised self-training set-up, in all pre-defined acoustic event detection tasks. As our approach relies on ensemble models, we further show the improvements can be distilled to a single model via knowledge distillation, with the resulting single student model maintaining high accuracy of teacher ensemble models.

eess.AS↗

arXiv2019

A note on the regularity of the holes for permeability property through a perforated domain for the 2D Euler equations

For equations of order two with the Dirichlet boundary condition, as the Laplace problem, the Stokes and the Navier-Stokes systems, perforated domains were only studied when the distance between the holes $d_{\varepsilon}$ is equal or much larger than the size of the holes $\varepsilon$. Such a diluted porous medium is interesting because it contains some cases where we have a non-negligible effect on the solution when $(\varepsilon,d_{\varepsilon})\to (0,0)$. Smaller distance was avoided for mathematical reasons and for theses large distances, the geometry of the holes does not affect -- or few -- the asymptotic result. Very recently, it was shown for the 2D-Euler equations that a porous medium is non-negligible only for inter-holes distance much smaller than the size of the holes. For this result, the regularity of holes boundary plays a crucial role, and the permeability criterium depends on the geometry of the lateral boundary. In this paper, we relax slightly the regularity condition, allowing a corner, and we note that a line of irregular obstacles cannot slow down a perfect fluid in any regime such that $\varepsilon \ln d_{\varepsilon} \to 0$.

math.AP↗

arXiv2019

Direct CP violation in multi-body $B$ decays with the $a^0_0(980)$--$f_0(980)$ mixing

We predict that the $a_0^0(980)$-$f_0(980)$ mixing would lead to large CP violation. We calculate the localized direct CP asymmetry in the decays $B^\pm \to f_0(980) [a_0^0(980)] π^\pm \to π^+ π^- π^\pm $ via the $a_0^0(980)$-$f_0(980)$ mixing mechanism based on the hypothetical $q\bar q$ structures of $a^0_0(980)$ and $f_0(980)$ in the QCD factorization. It is shown that there is a peak for CP violation, which could be as large as 58%, when the invariance mass of $ππ$ is near the masses of $a^0_0(980)$ and $f_0(980)$. Since the CP asymmetry is sensitive to the $a_0^0(980)$-$f_0(980)$ mixing, measuring the CP violating parameter in the aforementioned decays could provide a new way to verify the existence of the $a_0^0(980)$-$f_0(980)$ mixing and be helpful in clarifying the configuration nature of the light scalar mesons.

hep-ph↗

arXiv2019

Replication-Aware Linearizability

Geo-distributed systems often replicate data at multiple locations to achieve availability and performance despite network partitions. These systems must accept updates at any replica and propagate these updates asynchronously to every other replica. Conflict-Free Replicated Data Types (CRDTs) provide a principled approach to the problem of ensuring that replicas are eventually consistent despite the asynchronous delivery of updates. We address the problem of specifying and verifying CRDTs, introducing a new correctness criterion called Replication-Aware Linearizability. This criterion is inspired by linearizability, the de-facto correctness criterion for (shared-memory) concurrent data structures. We argue that this criterion is both simple to understand, and it fits most known implementations of CRDTs. We provide a proof methodology to show that a CRDT satisfies replication-aware linearizability which we apply on a wide range of implementations. Finally, we show that our criterion can be leveraged to reason modularly about the composition of CRDTs.

cs.PL↗

arXiv2019

Mitigating Power Side Channels during Compilation

The code generation modules inside modern compilers such as GCC and LLVM, which use a limited number of CPU registers to store a large number of program variables, may introduce side-channel leaks even in software equipped with state-of-the-art countermeasures. We propose a program analysis and transformation based method to eliminate this side channel. Our method has a type-based technique for detecting leaks, which leverages Datalog-based declarative analysis and domain-specific optimizations to achieve high efficiency and accuracy. It also has a mitigation technique for the compiler's backend, more specifically the register allocation modules, to ensure that potentially leaky intermediate computation results are always stored in different CPU registers or spilled to memory with isolation. We have implemented and evaluated our method in LLVM for the x86 instruction set architecture. Our experiments on cryptographic software show that the method is effective in removing the side channel while being efficient, i.e., our mitigated code is more compact and runs faster than code mitigated using state-of-the-art techniques.

cs.CR↗

arXiv2018

Parameters optimization and real-time calibration of Measurement-Device-Independent Quantum Key Distribution Network based on Back Propagation Artificial Neural Network

The parameters choosing (such as probabilities of choosing X-basis or Z-basis, intensity of signal state and decoy state, etc.) and system calibrating will be more challenging when the number of users of a measurement-device-independent quantum key distribution(MDI-QKD) network becomes larger. At present, people usually use optimization algorithms to search the best parameters. This method can find the optimized parameters accurately but may cost lots of time and hardware resources. It's a big problem in large scale MDI-QKD network. Here, we present a new method, using Back Propagation Artificial Neural Network(BPNN) to predict, rather than searching the optimized parameters. Compared with optimization algorithms, our BPNN is faster and more lightweight, it can save system resources. Another big problem brought by large scale MDI-QKD network is system recalibration. BPNN can support this work in real time, and it only needs to use some discarded data generated from communication process, rather than require us to add additional devices or scan the system

quant-ph↗

arXiv2018

A Universal Receiver for Uplink NOMA Systems

Given its capability in efficient radio resource sharing, non-orthogonal multiple access (NOMA) has been identified as a promising technology in 5G to improve the system capacity, user connectivity, and scheduling latency. A dozen of uplink NOMA schemes have been proposed recently and this paper considers the design of a universal receiver suitable for all potential designs of NOMA schemes. Firstly, a general turbo-like iterative receiver structure is introduced, under which, a universal expectation propagation algorithm (EPA) detector with hybrid parallel interference cancellation (PIC) is proposed (EPA in short). Link-level simulations show that the proposed EPA receiver can achieve superior block error rate (BLER) performance with implementation friendly complexity and fast convergence, and is always better than the traditional codeword level MMSE-PIC receiver for various kinds of NOMA schemes.

cs.IT↗

arXiv2018

Turbo-like Iterative Multi-user Receiver Design for 5G Non-orthogonal Multiple Access

Non-orthogonal multiple access (NoMA) as an efficient way of radio resource sharing has been identified as a promising technology in 5G to help improving system capacity, user connectivity, and service latency in 5G communications. This paper provides a brief overview of the progress of NoMA transceiver study in 3GPP, with special focus on the design of turbo-like iterative multi-user (MU) receivers. There are various types of MU receivers depending on the combinations of MU detectors and interference cancellation (IC) schemes. Link-level simulations show that expectation propagation algorithm (EPA) with hybrid parallel interference cancellation (PIC) is a promising MU receiver, which can achieve fast convergence and similar performance as message passing algorithm (MPA) with much lower complexity.

cs.IT↗

arXiv2018

Footprints of New Physics in $b\to cτν$ Transitions

In this work, we perform a combined analysis of the $R(D)$, $R(D^*)$, and $R(J/ψ)$ anomalies in a model-independent manner based on the general framework of the four-fermion effective field theory, paying special attention to the use of the hadronic form factors. For the $B\to D(D^*)$ transition form factors, we use the HQET parametrization that includes the higher order corrections of $\mathcal{O}(α_s,Λ_{\mathrm{QCD}}/m_{b,c})$ and was determined recently from a fit to lattice QCD and light-cone sum rule results in complementary kinematical regions of the momentum transfer. For the $B_c\to J/ψ(η_c)$ transitions, we use the form factors calculated in the covariant light-front quark model, which are found to be well consistent with the preliminary lattice results. With this particular treatment of hadronic matrix elements, in our analysis the two classes of vector operators are shown to be the most favored single new physics (NP) operators by the current experimental constraints within $2σ$ and the LEP1 data on $Br(B_c\to τν)$ as well as the minimum $χ^2$ fit, while the tensor operator is also allowed but severely constrained, and the scalar ones are excluded. Using the favored ranges and fitted values of the Wilson coefficients of the single NP operators, we also give a prognosis for the physical observables such as the ratios of decay rates ($R(D(D^*)), R(J/ψ(η_c))$) and other polarized observables as well as the $q^2$ distributions.

hep-ph↗

arXiv2018

Study of $CP$ Violation in $B^-\rightarrow K^- π^+π^-$ and $B^-\rightarrow K^- σ(600)$ decays in the QCD factorization approach

In this work, we study the localized $CP$ violation in $B^-\rightarrow K^-π^+π^-$ and $B^-\rightarrow K^- σ(600)$ decays by employing the quasi two-body QCD factorization approach. Both the resonance and the nonresonance contributions are studied for the $B^-\rightarrow K^-π^+π^-$ decay. The resonance contributions include those not only from $[ππ]$ channels including $σ(600)$, $ρ^0(770)$ and $ω(782)$ but also from $[Kπ]$ channels including $K^*(892)$, $K_0^*(1430)$, $K^*(1410)$, $K^*(1680)$ and $K_2^*(1430)$. By fitting the experimental data $\mathcal{A_{CP}}(K^-π^+π^-)=0.678\pm0.078\pm0.0323\pm0.007$ for $m_{K^-π^+}^2<15$ $\mathrm{GeV}^2$ and $0.08<m_{π^+π^-}^2<0.66$ $\mathrm{GeV}^2$, we get the end-point divergence parameters in our model, $ϕ_S \in [4.75, 5.95]$ and $ρ_S\in[4.2, 8]$. Using these results for $ρ_S$ and $ϕ_S$, we predict that the $CP$ asymmetry parameter $\mathcal{A_{CP}} \in [-0.094, -0.034]$ and the branching fraction $\mathcal{B} \in [1.82, 20.0]\times10^{-5}$ for the $B^-\rightarrow K^-σ(600)$ decay. In addition, we also analyse contributions to the localized $CP$ asymmetry $\mathcal{A_{CP}}(B^-\rightarrow K^-π^+π^-)$ from $[ππ]$, $[Kπ]$ channel resonances and nonresonance individually, which are found to be $\mathcal{A_{CP}}(B^-\rightarrow K^-[π^+π^-] \rightarrow K^-π^+π^-)=0.585\pm0.045$, $\mathcal{A_{CP}}(B^-\rightarrow [K^-π^+] π\rightarrow K^-π^+π^-)=0.086\pm0.021$ and $\mathcal{A_{CP}}^{NR}(B^-\rightarrow K^-π^+π^-)=0.061\pm0.0042$, respectively. Comparing these results, we can see that the localized $CP$ asymmetry in the $B^-\rightarrow K^-π^+π^-$ decay is mainly induced by the $[ππ]$ channel resonances while contributions from the $[Kπ]$ channel resonances and nonresonance are both very small.

hep-ph↗

arXiv2018

Semileptonic $B$ and $B_s$ decays involving scalar and axial-vector mesons

We report our theoretical calculations on the branching fractions for the semileptonic $B$ and $B_s$ decays, i.e., $B (B_s) \to (P,\, V,\, S,\,A) \ell ν_\ell$, where $P$ and $V$ denote the pseudoscalar and vector mesons, respectively, while $S$ denotes the scalar meson with mass above 1 GeV and $A$ the axial-vector meson. The branching fractions for the semileptonic $B\to P$ and $V$ modes have been measured very well in experiment and our theoretical values are in good agreement with them. The ones for $B\to S$ and $A$ modes are our theoretical predictions. There is little experimental information on the semileptonic $B_s$ decays although much theoretical effort has been done. In addition, we predict the branching fractions of $B\to D^*_0(2400) \ell \barν_\ell$ and $B_s\to D^{*-}_{s0}(2317) \ell \barν_\ell$ as $(2.31\pm 0.25)\times 10^{-3}$ and $(3.07\pm0.34)\times 10^{-3}$, in order, assuming them as the conventional mesons with quark-antiquark configuration. The high luminosity $e^+e^-$ collider SuperKEKB/Belle-II is running, with the data sample enhanced by a factor of 40 compared to Belle, which will provide huge opportunity for the test of the theoretical predictions and further help understand the inner structure of these scalar and axial vector mesons, e.g., the glueball content of $f_0(1710)$ and the mixing angles for the axial-vector mesons. These decay channels can also be accessed by the LHCb experiment.

hep-ph↗

arXiv2018

Novel Sparse Recovery Algorithms for 3D Debris Localization using Rotating Point Spread Function Imagery

An optical imager that exploits off-center image rotation to encode both the lateral and depth coordinates of point sources in a single snapshot can perform 3D localization and tracking of space debris. When actively illuminated, unresolved space debris, which can be regarded as a swarm of point sources, can scatter a fraction of laser irradiance back into the imaging sensor. Determining the source locations and fluxes is a large-scale sparse 3D inverse problem, for which we have developed efficient and effective algorithms based on sparse recovery using non-convex optimization. Numerical simulations illustrate the efficiency and stability of the algorithms.

astro-ph.IM↗

arXiv2018

Non-convex optimization for 3D point source localization using a rotating point spread function

We consider the high-resolution imaging problem of 3D point source image recovery from 2D data using a method based on point spread function (PSF) engineering. The method involves a new technique, recently proposed by S.~Prasad, based on the use of a rotating PSF with a single lobe to obtain depth from defocus. The amount of rotation of the PSF encodes the depth position of the point source. Applications include high-resolution single molecule localization microscopy as well as the problem addressed in this paper on localization of space debris using a space-based telescope. The localization problem is discretized on a cubical lattice where the coordinates of nonzero entries represent the 3D locations and the values of these entries the fluxes of the point sources. Finding the locations and fluxes of the point sources is a large-scale sparse 3D inverse problem. A new nonconvex regularization method with a data-fitting term based on Kullback-Leibler (KL) divergence is proposed for 3D localization for the Poisson noise model. In addition, we propose a new scheme of estimation of the source fluxes from the KL data-fitting term. Numerical experiments illustrate the efficiency and stability of the algorithms that are trained on a random subset of image data before being applied to other images. Our 3D localization algorithms can be readily applied to other kinds of depth-encoding PSFs as well.

eess.SP↗

arXiv2018

R-CRNN: Region-based Convolutional Recurrent Neural Network for Audio Event Detection

This paper proposes a Region-based Convolutional Recurrent Neural Network (R-CRNN) for audio event detection (AED). The proposed network is inspired by Faster-RCNN, a well known region-based convolutional network framework for visual object detection. Different from the original Faster-RCNN, a recurrent layer is added on top of the convolutional network to capture the long-term temporal context from the extracted high level features. While most of the previous works on AED generate predictions at frame level first, and then use post-processing to predict the onset/offset timestamps of events from a probability sequence; the proposed method generates predictions at event level directly and can be trained end-to-end with a multitask loss, which optimizes the classification and localization of audio events simultaneously. The proposed method is tested on DCASE 2017 Challenge dataset. To the best of our knowledge, R-CRNN is the best performing single-model method among all methods without using ensembles both on development and evaluation sets. Compared to the other region-based network for AED (R-FCN) with an event-based error rate (ER) of 0.18 on the development set, our method reduced the ER to half.

cs.SD↗

arXiv2018

A simple model for detection of rare sound events

We propose a simple recurrent model for detecting rare sound events, when the time boundaries of events are available for training. Our model optimizes the combination of an utterance-level loss, which classifies whether an event occurs in an utterance, and a frame-level loss, which classifies whether each frame corresponds to the event when it does occur. The two losses make use of a shared vectorial representation the event, and are connected by an attention mechanism. We demonstrate our model on Task 2 of the DCASE 2017 challenge, and achieve competitive performance.

cs.SD↗

arXiv2018

Information-Directed Random Walk for Rare Event Detection in Hierarchical Processes

The problem of detecting a few anomalous processes among a large number of data streams is considered. At each time, aggregated observations can be taken from a chosen subset of the processes, where the chosen subset conforms to a given tree structure. The random observations are drawn from a general distribution that may depend on the size of the chosen subset and the number of anomalous processes in the subset. We propose a sequential search strategy by devising an information-directed random walk (IRW) on the tree-structured observation hierarchy. Subject to a reliability constraint, the proposed policy is shown to be asymptotically optimal with respect to the detection accuracy. Furthermore, it achieves the optimal logarithmic-order sample complexity with respect to the size of the search space provided that the Kullback-Leibler divergence between aggregated observations in the presence and the absence of anomalous processes are bounded away from zero at all levels of the tree structure as the size of the search space approaches infinity. Sufficient conditions on the decaying rate of the aggregated observations to pure noise under which a sublinear scaling in the size of the search space is preserved are also identified for the Bernoulli case.

cs.IT↗