arXiv subjects

Hongwei Liu

Publications and source records attributed to Hongwei Liu.

At least 91 records · Page 5Linked to original sources

arXiv2022

A class of constacyclic codes are generalized Reed-Solomon codes

Maximum distance separable (MDS) codes are optimal in the sense that the minimum distance cannot be improved for a given length and code size. The most prominent MDS codes are generalized Reed-Solomon (GRS) codes. The square $\mathcal{C}^{2}$ of a linear code $\mathcal{C}$ is the linear code spanned by the component-wise products of every pair of codewords in $\mathcal{C}$. For an MDS code $\mathcal{C}$, it is convenient to determine whether $\mathcal{C}$ is a GRS code by determining the dimension of $\mathcal{C}^{2}$. In this paper, we investigate under what conditions that MDS constacyclic codes are GRS. For this purpose, we first study the square of constacyclic codes. Then, we give a sufficient condition that a constacyclic code is GRS. In particular, We provide a necessary and sufficient condition that a constacyclic code of a prime length is GRS.

cs.IT↗

arXiv2022

An Insight-HXMT view of the mHz quasi-regular modulation phenomenon in the black hole X-ray binary 4U 1630-47

Here we report the spectral-timing results of the black hole X-ray binary 4U 1630-47 during its 2021 outburst using observations from the Hard X-ray Modulation Telescope. Type-C quasi-periodic oscillations (QPOs) in 1.6--4.2 Hz and quasi-regular modulation (QRM) near 60 mHz are detected during the outburst. The mHz QRM has a fractional rms of 10%--16% in the 8--35 keV energy band with a Q factor (frequency/width) of 2--4. Benefiting from the broad energy band of hxmt, we study the energy dependence of the 60 mHz QRM in 1--100 keV for the first time. We find that the fractional rms of the mHz QRM increases with photon energy, while the time lags of the mHz QRM are soft and decrease with photon energy. Fast recurrence of the mHz QRM, in a timescale of less than one hour, has been observed during the outburst. During this period, the corresponding energy spectra moderately change when the source transitions from the QRM state to the non-QRM state. The QRM phenomena also shows a dependence with the accretion rate. We suggest that the QRM could be caused by an unknown accretion instability aroused from the corona.

astro-ph.HE↗

arXiv2022

Generalized $b$-symbol weights of Linear Codes and $b$-symbol MDS Codes

Generalized pair weights of linear codes are generalizations of minimum symbol-pair weights, which were introduced by Liu and Pan \cite{LP} recently. Generalized pair weights can be used to characterize the ability of protecting information in the symbol-pair read wire-tap channels of type II. In this paper, we introduce the notion of generalized $b$-symbol weights of linear codes over finite fields, which is a generalization of generalized Hamming weights and generalized pair weights. We obtain some basic properties and bounds of generalized $b$-symbol weights which are called Singleton-like bounds for generalized $b$-symbol weights. As examples, we calculate generalized weight matrices for simplex codes and Hamming codes. We provide a necessary and sufficient condition for a linear code to be a $b$-symbol MDS code by using the generator matrix and the parity check matrix of this linear code. Finally, a necessary and sufficient condition of a linear isomorphism preserving $b$-symbol weights between two linear codes is obtained. As a corollary, we get the classical MacWilliams extension theorem when $b=1$.

cs.IT↗

arXiv2022

Quasi-periodic oscillations of the X-ray burst from the magnetar SGR J1935+2154 and associated with the fast radio burst FRB 200428

The origin(s) and mechanism(s) of fast radio bursts (FRBs), which are short radio pulses from cosmological distances, have remained a major puzzle since their discovery. We report a strong Quasi-Periodic Oscillation(QPO) of 40 Hz in the X-ray burst from the magnetar SGR J1935+2154 and associated with FRB 200428, significantly detected with the Hard X-ray Modulation Telescope (Insight-HXMT) and also hinted by the Konus-Wind data. QPOs from magnetar bursts have only been rarely detected; our 3.4 sigma (p-value is 2.9e-4) detection of the QPO reported here reveals the strongest QPO signal observed from magnetars (except in some very rare giant flares), making this X-ray burst unique among magnetar bursts. The two X-ray spikes coinciding with the two FRB pulses are also among the peaks of the QPO. Our results suggest that at least some FRBs are related to strong oscillation processes of neutron stars. We also show that we may overestimate the significance of the QPO signal and underestimate the errors of QPO parameters if QPO exists only in a fraction of the time series of a X-ray burst which we use to calculate the Leahy-normalized periodogram.

astro-ph.HE↗

arXiv2022

Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents

Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation. Most state-of-the-art matching models, e.g., BERT, directly perform text comparison by processing each word uniformly. However, a query sentence generally comprises content that calls for different levels of matching granularity. Specifically, keywords represent factual information such as action, entity, and event that should be strictly matched, while intents convey abstract concepts and ideas that can be paraphrased into various expressions. In this work, we propose a simple yet effective training strategy for text semantic matching in a divide-and-conquer manner by disentangling keywords from intents. Our approach can be easily combined with pre-trained language models (PLM) without influencing their inference efficiency, achieving stable performance improvements against a wide range of PLMs on three benchmarks.

cs.CL↗

arXiv2022

Convergence Rate of Inertial Forward-Backward Algorithms Based on the Local Error Bound Condition

The "Inertial Forward-Backward algorithm" (IFB) is a powerful tool for convex nonsmooth minimization problems, it gives the well known "fast iterative shrinkage-thresholding algorithm " (FISTA), which enjoys $O\left( {\frac{1}{k^2}} \right)$ global convergence rate of function values, however, no convergence of iterates has been proved; by do a small modification, an accelerated IFB called "FISTA\_CD" improves the convergence rate of function values to $o\left( {\frac{1}{k^2}} \right)$ and shows the weak convergence of iterates. The local error bound condition is extremely useful in analyzing the convergence rates of a host of iterative methods for solving optimization problems, and in practical application, a large number of problems with special structure often satisfy the error bound condition. Naturally, using local error bound condition to derive or improve the convergence rate of IFB is a common means. In this paper, based on the local error bound condition, we exploit an new assumption condition for the important parameter $t_k$ in IFB, and establish the improved convergence rate of function values and strong convergence of the iterates generated by the IFB algorithms with six $t_k$ satisfying the above assumption condition in Hilbert space. It is remarkable that, under the local error bound condition, we establish the strong convergence of the iterates generated by the original FISTA, and prove that the convergence rates of function values for FISTA\_CD is actually related to the value of parameter $a,$ and show that the IFB algorithms with some $t_k$ mentioned above can achieve sublinear convergence rate $o\left( {\frac{1}{k^p}} \right)$ for any positive integer $p>1$. Some numerical experiments are conducted to illustrate our results.

math.OC↗

arXiv2022

Two efficient gradient methods with approximately optimal stepsizes based on regularization models for unconstrained optimization

It is widely accepted that the stepsize is of great significance to gradient method. Two efficient gradient methods with approximately optimal stepsizes mainly based on regularization models are proposed for unconstrained optimization. More exactly, if the objective function is not close to a quadratic function on the line segment between the current and latest iterates, regularization models are exploited carefully to generate approximately optimal stepsizes. Otherwise, quadratic approximation models are used. In particular, when the curvature is non-positive, special regularization models are developed. The convergence of the proposed methods is established under the weak conditions. Extensive numerical experiments indicated the proposed method is superior to the BBQ method (SIAM J. Optim. 2021,31(4), 3068-3096) and other efficient gradient methods, and is competitive to two famous and efficient conjugate gradient software packages CG$ \_ $DESCENT (5.0) (SIAM J. Optim. 16(1), 170-192, 2005) and CGOPT (1.0) (SIAM J. Optim. 23(1), 296-320, 2013) for the CUTEr library. Due to the surprising efficiency, we believe that gradient methods with approximately optimal stepsizes can become strong candidates for large-scale unconstrained optimization.

math.OC↗

arXiv2021

Spatial Location Constraint Prototype Loss for Open Set Recognition

One of the challenges in pattern recognition is open set recognition. Compared with closed set recognition, open set recognition needs to reduce not only the empirical risk, but also the open space risk, and the reduction of these two risks corresponds to classifying the known classes and identifying the unknown classes respectively. How to reduce the open space risk is the key of open set recognition. This paper explores the origin of the open space risk by analyzing the distribution of known and unknown classes features. On this basis, the spatial location constraint prototype loss function is proposed to reduce the two risks simultaneously. Extensive experiments on multiple benchmark datasets and many visualization results indicate that our methods is superior to most existing approaches.

cs.CV↗

arXiv2021

Quantum emitter formation in carbon-doped monolayer hexagonal boron nitride

Single photon emitters (SPEs) in hexagonal boron nitride (hBN) are promising candidates for quantum light generation. Despite this, techniques to control the formation of hBN SPEs down to the monolayer limit are yet to be demonstrated. Recent experimental and theoretical investigations have suggested that the visible wavelength single photon emitters in hBN originate from carbon-related defects. Here we demonstrate a simple strategy for controlling SPE creation during the chemical vapor deposition growth of monolayer hBN via regulating surface carbon concentration. By increasing surface carbon concentration during hBN growth, we observe increases in carbon doping levels by 2.4 fold for B-C bonds and 1.6 fold for N-C bonds. For the same samples we observe an increase in SPE density from 0.13 to 0.30 emitters/um2. Our simple method enables the reliable creation of hBN SPEs in monolayer samples for the first time, opening the door to advanced 2D quantum state engineering.

cond-mat.mtrl-sci↗

arXiv2021

Adversarial Motorial Prototype Framework for Open Set Recognition

Open set recognition is designed to identify known classes and to reject unknown classes simultaneously. Specifically, identifying known classes and rejecting unknown classes correspond to reducing the empirical risk and the open space risk, respectively. First, the motorial prototype framework (MPF) is proposed, which classifies known classes according to the prototype classification idea. Moreover, a motorial margin constraint term is added into the loss function of the MPF, which can further improve the clustering compactness of known classes in the feature space to reduce both risks. Second, this paper proposes the adversarial motorial prototype framework (AMPF) based on the MPF. On the one hand, this model can generate adversarial samples and add these samples into the training phase; on the other hand, it can further improve the differential mapping ability of the model to known and unknown classes with the adversarial motion of the margin constraint radius. Finally, this paper proposes an upgraded version of the AMPF, AMPF++, which adds much more generated unknown samples into the training phase. In this paper, a large number of experiments prove that the performance of the proposed models is superior to that of other current works.

cs.CV↗

arXiv2021

Cyclic subspace codes via the sum of Sidon spaces

Subspace codes, especially cyclic constant subspace codes, are of great use in random network coding. Subspace codes can be constructed by subspaces and subspace polynomials. In particular, many researchers are keen to find special subspaces and subspace polynomials to construct subspace codes with the size and the minimum distance as large as possible. In [14], Roth, Raviv and Tamo constructed several subspace codes using Sidon spaces, and it is proved that subspace codes constructed by Sidon spaces has the largest size and minimum distance. In [12], Niu, Yue and Wu extended some results of [14] and obtained several new subspace codes. In this paper, we first provide a sufficient condition for the sum of Sidon spaces is again a Sidon space. Based on this result, we obtain new cyclic constant subspace codes through the sum of two and three Sidon spaces. Our results generalize the results in [14] and [12].

cs.IT↗

arXiv2021

Four-state reference-frame-independent quantum key distribution with non-qubit sources

The discrepancy between theory and experiment severely limits the development of quantum key distribution (QKD). Reference-frame-independent (RFI) protocol has been proposed to avoid alignment of the reference frame. However, multiple optical modes caused by Trojan horse attacks and equipment loopholes lead to the imperfect emitted signal unavoidably. In this paper, we analyzed the security of the RFI-QKD protocol with non-qubit sources based on generalizing loss-tolerant techniques. The simulation results show that our work can effectively defend against non-qubit sources including a misaligned reference frame, state preparation flaws, multiple optical modes, and Trojan horse attacks. Moreover, it only requires the preparation of four quantum states, which reduces the complexity of the experiment in the future.

quant-ph↗

arXiv2021

Insight-HXMT observations of jet-like corona in a black hole X-ray binary MAXI J1820+070

A black hole X-ray binary produces hard X-ray radiation from its corona and disk when the accreting matter heats up. During an outburst, the disk and corona co-evolves with each other. However, such an evolution is still unclear in both its geometry and dynamics. Here we report the unusual decrease of the reflection fraction in MAXI J1820+070, which is the ratio of the coronal intensity illuminating the disk to the coronal intensity reaching the observer, as the corona is observed to contrast during the decay phase. We postulate a jet-like corona model, in which the corona can be understood as a standing shock where the material flowing through. In this dynamical scenario, the decrease of the reflection fraction is a signature of the corona's bulk velocity. Our findings suggest that as the corona is observed to get closer to the black hole, the coronal material might be outflowing faster.

astro-ph.HE↗

arXiv2021

Predicting nanocrystal morphology governed by interfacial strain

The shape dependence for the technologically important nickel oxide (NiO) nanocrystals on (001) strontium titanate substrates is investigated under the generalized Wulff-Kaichew (GWK) theorem framework. It is found that the shape of the NiO nanocrystals is primarily governed by the existence (or absence) of interfacial strain. Nanocrystals that have a fully pseudomorphic interface with the substrate (i.e. the epitaxial strain is not relaxed) form an embedded smooth ball-crown morphology with {001}, {011}, {111} and high-index {113} exposed facets with a negative Wulff point. On the other hand, when the interfacial strain is relaxed by misfit dislocations, the nanocrystals take on a truncated pyramidal shape, bounded by {111} faces and a {001} flat top, with a positive Wulff point. Our quantitative model is able to predict both experimentally observed shapes and sizes with good accuracy. Given the increasing demand for hetero-epitaxial nanocrystals in various physio-chemical and electro-chemical functional devices, these results lay the important groundwork in exploiting the GWK theorem as a general analytical approach to explain hetero-epitaxial nanocrystal growth on oxide substrates governed by interface strain.

cond-mat.mtrl-sci↗

arXiv2020

Non-Invertible-Element Constacyclic Codes over Finite PIRs

In this paper we introduce the notion of $λ$-constacyclic codes over finite rings $R$ for arbitary element $λ$ of $R$. We study the non-invertible-element constacyclic codes (NIE-constacyclic codes) over finite principal ideal rings (PIRs). We determine the algebraic structures of all NIE-constacyclic codes over finite chain rings, give the unique form of the sets of the defining polynomials and obtain their minimum Hamming distances. A general form of the duals of NIE-constacyclic codes over finite chain rings is also provided. In particular, we give a necessary and sufficient condition for the dual of an NIE-constacyclic code to be an NIE-constacyclic code. Using the Chinese Remainder Theorem, we study the NIE-constacyclic codes over finite PIRs. Furthermore, we construct some optimal NIE-constacyclic codes over finite PIRs in the sense that they achieve the maximum possible minimum Hamming distances for some given lengths and cardinalities.

cs.IT↗

arXiv2020

Variational Temporal Deep Generative Model for Radar HRRP Target Recognition

We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP. The proposed rGBN adopts a hierarchy of gamma distributions to build its temporal deep generative model. For scalable training and fast out-of-sample prediction, we propose the hybrid of a stochastic-gradient Markov chain Monte Carlo (MCMC) and a recurrent variational inference model to perform posterior inference. To utilize the label information to extract more discriminative latent representations, we further propose supervised rGBN to jointly model the HRRP samples and their corresponding labels. Experimental results on synthetic and measured HRRP data show that the proposed models are efficient in computation, have good classification accuracy and generalization ability, and provide highly interpretable multi-stochastic-layer latent structure.

stat.ML↗

arXiv2020

Generalized Pair Weights of Linear Codes and Linear Isomorphisms Preserving Pair Weights

In this paper, we first introduce the notion of generalized pair weights of an $[n, k]$-linear code over the finite field $\mathbb{F}_q$ and the notion of pair $r$-equiweight codes, where $1\le r\le k-1$. Some basic properties of generalized pair weights of linear codes over finite fields are derived. Then we obtain a necessary and sufficient condition for an $[n,k]$-linear code to be a pair equiweight code, and we characterize pair $r$-equiweight codes for any $1\le r\le k-1$. Finally, a necessary and sufficient condition for a linear isomorphism preserving pair weights between two linear codes is obtained.

cs.IT↗

arXiv2020

Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations

Recommender systems have played an increasingly important role in providing users with tailored suggestions based on their preferences. However, the conventional offline recommender systems cannot handle the ubiquitous data stream well. To address this issue, Streaming Recommender Systems (SRSs) have emerged in recent years, which incrementally train recommendation models on newly received data for effective real-time recommendations. Focusing on new data only benefits addressing concept drift, i.e., the changing user preferences towards items. However, it impedes capturing long-term user preferences. In addition, the commonly existing underload and overload problems should be well tackled for higher accuracy of streaming recommendations. To address these problems, we propose a Stratified and Time-aware Sampling based Adaptive Ensemble Learning framework, called STS-AEL, to improve the accuracy of streaming recommendations. In STS-AEL, we first devise stratified and time-aware sampling to extract representative data from both new data and historical data to address concept drift while capturing long-term user preferences. Also, incorporating the historical data benefits utilizing the idle resources in the underload scenario more effectively. After that, we propose adaptive ensemble learning to efficiently process the overloaded data in parallel with multiple individual recommendation models, and then effectively fuse the results of these models with a sequential adaptive mechanism. Extensive experiments conducted on three real-world datasets demonstrate that STS-AEL, in all the cases, significantly outperforms the state-of-the-art SRSs.

cs.IR↗