arXiv subjects

Xing Wang

Publications and source records attributed to Xing Wang.

At least 235 records · Page 13Linked to original sources

arXiv2018

A Deep Dive into Blockchain Selfish Mining

This paper studies a fundamental problem regarding the security of blockchain on how the existence of multiple misbehaving pools influences the profitability of selfish mining. Each selfish miner maintains a private chain and makes it public opportunistically for the purpose of acquiring more rewards incommensurate to his Hashrate. We establish a novel Markov chain model to characterize all the state transitions of public and private chains. The minimum requirement of Hashrate together with the minimum delay of being profitable is derived in close-form. The former reduces to 21.48% with the symmetric selfish miners, while their competition with asymmetric Hashrates puts forward a higher requirement of the profitable threshold. The profitable delay increases with the decrease of the Hashrate of selfish miners, making the mining pools more cautious on performing selfish mining.

cs.CR↗

arXiv2018

Exploiting Deep Representations for Neural Machine Translation

Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures. However, only the top layers of encoder and decoder are leveraged in the subsequent process, which misses the opportunity to exploit the useful information embedded in other layers. In this work, we propose to simultaneously expose all of these signals with layer aggregation and multi-layer attention mechanisms. In addition, we introduce an auxiliary regularization term to encourage different layers to capture diverse information. Experimental results on widely-used WMT14 English-German and WMT17 Chinese-English translation data demonstrate the effectiveness and universality of the proposed approach.

cs.CL↗

arXiv2018

Network Modeling and Pathway Inference from Incomplete Data ("PathInf")

In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems. PathInf is a two-stages inference model. In the first stage, it applies a data summarization model based on maximum likelihood to deal with the massive distributed missing values by transforming the observation-wise items in the data into state matrix. In the second stage, transition pattern (i.e. pathway) among variables is inferred as a graph inference problem solved by greedy algorithm with constraints. The proposed method was validated and compared with the state-of-art Bayesian network method on the simulation data, and shown consistently superior performance. By applying the PathInf on the lymph vascular metastasis data, we obtained the holistic pathways of the lymph node metastasis with novel discoveries on the jumping metastasis among nodes that are physically apart. The discovery indicates the possible presence of sentinel node groups in the lung lymph nodes which have been previously speculated yet never found. The pathway map can also improve the current dissection examination protocol for better individualized treatment planning, for higher diagnostic accuracy and reducing the patients trauma.

stat.ML↗

arXiv2018

Resummation for (boosted) top-quark pair production at NNLO+NNLL' in QCD

We construct predictions for top quark pair differential distributions at hadron colliders that combine state-of-the-art NNLO QCD calculations with double resummation at NNLL' accuracy of threshold logarithms arising from soft gluon emissions and of small mass logarithms. This is the first time a resummed calculation at full NNLO+NNLL' accuracy in QCD for a process with non-trivial color structure has been completed at the differential level. Of main interest to us is the stability of the M_{tt} and top-quark p_T distributions in the boosted regime where fixed order calculations may become strongly dependent on the choice of dynamic scales. With the help of numeric and analytic arguments we confirm that the choice for the factorization and renormalization scales advocated recently by some of the authors is indeed optimal. We further derive a set of optimized kinematics-dependent scales for the matching functions which appear in the resummed calculations. Our NNLO+NNLL' prediction for the top-pair invariant mass is significantly less sensitive to the choice of factorization scale than the fixed order prediction, even at NNLO. Notably, the resummed and fixed order calculations are in nearly perfect agreement with each other in the full M_{tt} range when the optimal dynamic scale is used. For the top-quark p_T distribution the resummation performed here has less of an impact and instead we find that upgrading the matching with fixed-order from NLO+NNLL' to NNLO+NNLL' to be an important effect, a point to be kept in mind when using NLO-based Monte Carlo event generators to calculate this distribution.

hep-ph↗

arXiv2017

The relationship between the number of editorial board members and the scientific output of universities in the chemistry field

Editorial board members, who are considered the gatekeepers of scientific journals, play an important role in academia, and may directly or indirectly affect the scientific output of a university. In this article, we used the quantile regression method among a sample of 1,387 university in chemistry to characterize the correlation between the number of editorial board members and the scientific output of their universities. Furthermore, we used time-series data and the Granger causality test to explore the causal relationship between the number of editorial board members and the number of articles of some top universities. Our results suggest that the number of editorial board members is positively and significantly related to the scientific output (as measured by the number of articles, total number of citations, citations per paper, and h index) of their universities. However, the Granger causality test results suggest that the causal relationship between the number of editorial board members and the number of articles of some top universities is not obvious. Combining these findings with the results of qualitative interviews with editorial board members, we discuss the causal relationship between the number of editorial board members and the scientific output of their universities.

cs.DL↗

arXiv2017

Radiative Decays of the Higgs Boson to a Pair of Fermions

We revisit the radiative decays of the Higgs boson to a fermion pair $h\rightarrow f\bar{f}γ$ where $f$ denotes a fermion in the Standard Model (SM). We include the chirality-flipping diagrams via the Yukawa couplings at the order $\mathcal{O}(y_f^2 α)$, the chirality-conserving contributions via the top-quark loops of the order $\mathcal{O}(y_t^2 α^3)$, and the electroweak loops at the order $\mathcal{O}(α^4)$. The QED correction is about $Q_f^2\times {\cal O}(1\%)$ and contributes to the running of fermion masses at a similar level, which should be taken into account for future precision Higgs physics. The chirality-conserving electroweak-loop processes are interesting from the observational point of view. First, the branching fraction of the radiative decay $h \to μ^+μ^- γ$ is about a half of that of $h \to μ^+μ^-$, and that of $h \to e^+ e^- γ$ is more than four orders of magnitude larger than that of $h \to e^+ e^-$, both of which reach about $10^{-4}$. The branching fraction of $h \to τ^+τ^- γ$ is of the order $10^{-3}$. All the leptonic radiative decays are potentially observable at the LHC Run 2 or the HL-LHC. The kinematic distributions for the photon energy or the fermion pair invariant mass provide non-ambiguous discrimination for the underlying mechanisms of the Higgs radiative decay. We also study the process $h \to c\bar c γ$ and evaluate the observability at the LHC. We find it comparable to the other related studies and better than the $h \to J/ψ γ$ channel in constraining the charm-Yukawa coupling.

hep-ph↗

arXiv2017

Translating Phrases in Neural Machine Translation

Phrases play an important role in natural language understanding and machine translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is difficult to integrate them into current neural machine translation (NMT) which reads and generates sentences word by word. In this work, we propose a method to translate phrases in NMT by integrating a phrase memory storing target phrases from a phrase-based statistical machine translation (SMT) system into the encoder-decoder architecture of NMT. At each decoding step, the phrase memory is first re-written by the SMT model, which dynamically generates relevant target phrases with contextual information provided by the NMT model. Then the proposed model reads the phrase memory to make probability estimations for all phrases in the phrase memory. If phrase generation is carried on, the NMT decoder selects an appropriate phrase from the memory to perform phrase translation and updates its decoding state by consuming the words in the selected phrase. Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does. Experiment results on the Chinese to English translation show that the proposed model achieves significant improvements over the baseline on various test sets.

cs.CL↗

arXiv2017

Continuum model for hydrogen pickup in Zirconium alloys of LWR fuel cladding

A continuum model for calculating the time-dependent hydrogen pickup fractions in various Zirconium alloys under steam and pressured water oxidation has been developed in this study. Using only one fitting parameter, the effective hydrogen gas partial pressure at the oxide surface, a qualitative agreement is obtained between the predicted and previously measured hydrogen pickup fractions. The calculation results therefore demonstrate that H diffusion through the dense oxide layer plays an important role in the hydrogen pickup process. The limitations and possible improvement of the model are also discussed.

cond-mat.mtrl-sci↗

arXiv2016

Neural Machine Translation Advised by Statistical Machine Translation

Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years. However, recent studies show that NMT generally produces fluent but inadequate translations (Tu et al. 2016b; Tu et al. 2016a; He et al. 2016; Tu et al. 2017). This is in contrast to conventional Statistical Machine Translation (SMT), which usually yields adequate but non-fluent translations. It is natural, therefore, to leverage the advantages of both models for better translations, and in this work we propose to incorporate SMT model into NMT framework. More specifically, at each decoding step, SMT offers additional recommendations of generated words based on the decoding information from NMT (e.g., the generated partial translation and attention history). Then we employ an auxiliary classifier to score the SMT recommendations and a gating function to combine the SMT recommendations with NMT generations, both of which are jointly trained within the NMT architecture in an end-to-end manner. Experimental results on Chinese-English translation show that the proposed approach achieves significant and consistent improvements over state-of-the-art NMT and SMT systems on multiple NIST test sets.

cs.CL↗

arXiv2016

Pointwise convergence of solution to Schrodinger equation on manifolds

Let $(M^n,g)$ be a Riemannian manifold without boundary. We study the amount of initial regularity is required so that the solution to free Schrödinger equation converges pointwisely to its initial data. Assume the initial data is in $H^α(M)$. For Hyperbolic Space, standard Sphere and the 2 dimensional Torus, we prove that $α>\frac{1}{2}$ is enough. For general compact manifolds, due to lacking of local smoothing effect, it is hard to beat the bound $α>1$ from interpolation. We managed to go below 1 for dimension $\leq 3$. The more interesting thing is that, for 1 dimensional compact manifold, $α>\frac{1}{3}$ is sufficient.

math.AP↗

arXiv2016

Scalable Compression of Deep Neural Networks

Deep neural networks generally involve some layers with mil- lions of parameters, making them difficult to be deployed and updated on devices with limited resources such as mobile phones and other smart embedded systems. In this paper, we propose a scalable representation of the network parameters, so that different applications can select the most suitable bit rate of the network based on their own storage constraints. Moreover, when a device needs to upgrade to a high-rate network, the existing low-rate network can be reused, and only some incremental data are needed to be downloaded. We first hierarchically quantize the weights of a pre-trained deep neural network to enforce weight sharing. Next, we adaptively select the bits assigned to each layer given the total bit budget. After that, we retrain the network to fine-tune the quantized centroids. Experimental results show that our method can achieve scalable compression with graceful degradation in the performance.

cs.CV↗

arXiv2016

One Bump or Two Peaks? The 750 GeV Diphoton Excess and Dark Matter with a Complex Mediator

We consider the possibility that the recently observed excess in the diphoton invariant mass spectrum around 750 GeV is due to two narrow scalars. We demonstrate that there is no need to introduce invisible decay modes to enlarge the widths as long as the two scalars exhibit an appropriate mass splitting. Nevertheless we investigate the possible connection of these two scalars to the dark matter in a toy model with a Majorana fermionic dark matter candidate. We explore how large the widths of the two scalar widths could be taking into account various constraints from collider and dark matter experiments. Introducing two scalars alleviates the tension between the diphoton production and dark matter constraints.

hep-ph↗

arXiv2016

Resummed differential cross sections for top-quark pairs at the LHC

We present state of the art resummation predictions for differential cross sections in top-quark pair production at the LHC. They are derived from a formalism which allows the simultaneous resummation of both soft and small-mass logarithms, which endanger the convergence of fixed-order perturbative series in the boosted regime, where the partonic center-of-mass energy is much larger than the mass to the top quark. We combine such a double resummation at NNLL$'$ accuracy with standard soft-gluon resummation at NNLL accuracy and with NLO calculations, so that our results are applicable throughout the whole phase space. We find that the resummation effects on the differential distributions are significant, bringing theoretical predictions into better agreement with experimental data compared to fixed-order calculations. Moreover, such effects are not well described by the NNLO approximation of the resummation formula, especially in the high-energy tails of the distributions, highlighting the importance of all-orders resummation in dedicated studies of boosted top production.

hep-ph↗

arXiv2016

Multi-Resolution Compressed Sensing via Approximate Message Passing

In this paper, we consider the problem of multi-resolution compressed sensing (MR-CS) reconstruction, which has received little attention in the literature. Instead of always reconstructing the signal at the original high resolution (HR), we enable the reconstruction of a low-resolution (LR) signal when there are not enough CS samples to recover a HR signal. We propose an approximate message passing (AMP)-based framework dubbed MR-AMP, and derive its state evolution, phase transition, and noise sensitivity, which show that in addition to reduced complexity, our method can recover a LR signal with bounded noise sensitivity even when the noise sensitivity of the conventional HR reconstruction is unbounded. We then apply the MR-AMP to image reconstruction using either soft-thresholding or total variation denoiser, and develop three pairs of up-/down-sampling operators in transform or spatial domain. The performance of the proposed scheme is demonstrated by both 1D synthetic data and 2D images.

cs.IT↗

arXiv2015

Spin and Chirality Effects in Antler-Topology Processes at High Energy $e^+e^-$ Colliders

We perform a model-independent investigation of spin and chirality correlation effects in the antler-topology processes $e^+e^-\to\mathcal{P}^+\mathcal{P}^-\to (\ell^+ \mathcal{D}^0) (\ell^-\mathcal{\bar{D}}^0)$ at high energy $e^+e^-$ colliders with polarized beams. Generally the production process $e^+e^-\to\mathcal{P}^+\mathcal{P}^-$ can occur not only through the $s$-channel exchange of vector bosons, $\mathcal{V}^0$, including the neutral Standard Model (SM) gauge bosons, $γ$ and $Z$, but also through the $s$- and $t$-channel exchanges of new neutral states, $\mathcal{S}^0$ and $\mathcal{T}^0$, and the $u$-channel exchange of new doubly-charged states, $\mathcal{U}^{--}$. The general set of (non-chiral) three-point couplings of the new particles and leptons allowed in a renormalizable quantum field theory is considered. The general spin and chirality analysis is based on the threshold behavior of the excitation curves for $\mathcal{P}^+\mathcal{P}^-$ pair production in $e^+e^-$ collisions with longitudinal and transverse polarized beams, the angular distributions in the production process and also the production-decay angular correlations. In the first step, we present the observables in the helicity formalism. Subsequently, we show how a set of observables can be designed for determining the spins and chiral structures of the new particles without any model assumptions. Finally, taking into account a typical set of approximately chiral invariant scenarios, we demonstrate how the spin and chirality effects can be probed experimentally at a high energy $e^+e^-$ collider.

hep-ph↗

arXiv2015

A lower bound for the nodal sets of Steklov eigenfunctions

We consider the lower bound of nodal sets of Steklov eigenfunctions on smooth Riemannian manifolds with boundary--the eigenfunctions of the Dirichlet-to-Neumann map. Let $N_λ$ be its nodal set. Assume that zero is a regular value of Steklov eigenfunctions. We show that $$H^{n-1}(N_λ)\geq Cλ^{\frac{3-n}{2}}$$ for some positive constant $C$ depending only on the manifold.

math.AP↗

arXiv2015

Characterizing Invisible Electroweak Particles through Single-Photon Processes at High Energy $e^+e^-$ Colliders

We explore the scenarios where the only accessible new states at the electroweak scale consist of a pair of color-singlet electroweak particles, whose masses are degenerate at the tree level and split only by electroweak symmetry breaking at the loop level. For the sake of illustration, we consider a supersymmetric model and study the following three representative cases with the lower-lying states as (a) two spin-1/2 Higgsino SU(2)$_L$ doublets, (b) a spin-1/2 wino SU(2)$_L$ triplet and (c) a spin-0 left-handed slepton SU(2)$_L$ doublet. Due to the mass-degeneracy, those lower-lying electroweak states are difficult to observe at the LHC and rather challenging to detect at the $e^+ e^-$ collider as well. We exploit the pair production in association with a hard photon radiation in high energy $e^+ e^-$ collisions. If kinematically accessible, such single-photon processes at $e^+e^-$ colliders with polarized beams enable us to characterize each scenario by measuring the energy and scattering angle of the associated hard photon, and to determine the spin of the nearly invisible particles unambiguously through the threshold behavior in the photon energy distribution.

hep-ph↗

arXiv2015

Lower bounds for interior nodal sets of Steklov eigenfunctions

We study the interior nodal sets, $Z_λ$ of Steklov eigenfunctions in an $n$-dimensional relatively compact manifolds $M$ with boundary and show that one has the lower bounds $|Z_λ|\ge cλ^{\frac{2-n}2}$ for the size of its $(n-1)$-dimensional Hausdorff measure. The proof is based on a Dong-type identity and estimates for the gradient of Steklov eigenfunctions, similar to those in \cite{SZ1} and \cite{SZ2}, respectively.

math.AP↗