arXiv subjects

Lin Wang

Publications and source records attributed to Lin Wang.

At least 397 records · Page 22Linked to original sources

arXiv2021

Surrogate-assisted cooperative signal optimization for large-scale traffic networks

Reasonable setting of traffic signals can be very helpful in alleviating congestion in urban traffic networks. Meta-heuristic optimization algorithms have proved themselves to be able to find high-quality signal timing plans. However, they generally suffer from performance deterioration when solving large-scale traffic signal optimization problems due to the huge search space and limited computational budget. Directing against this issue, this study proposes a surrogate-assisted cooperative signal optimization (SCSO) method. Different from existing methods that directly deal with the entire traffic network, SCSO first decomposes it into a set of tractable sub-networks, and then achieves signal setting by cooperatively optimizing these sub-networks with a surrogate-assisted optimizer. The decomposition operation significantly narrows the search space of the whole traffic network, and the surrogate-assisted optimizer greatly lowers the computational burden by reducing the number of expensive traffic simulations. By taking Newman fast algorithm, radial basis function and a modified estimation of distribution algorithm as decomposer, surrogate model and optimizer, respectively, this study develops a concrete SCSO algorithm. To evaluate its effectiveness and efficiency, a large-scale traffic network involving crossroads and T-junctions is generated based on a real traffic network. Comparison with several existing meta-heuristic algorithms specially designed for traffic signal optimization demonstrates the superiority of SCSO in reducing the average delay time of vehicles.

cs.NE↗

arXiv2021

An embedded multichannel sound acquisition system for drone audition

Microphone array techniques can improve the acoustic sensing performance on drones, compared to the use of a single microphone. However, multichannel sound acquisition systems are not available in current commercial drone platforms. To encourage the research in drone audition, we present an embedded sound acquisition and recording system with eight microphones and a multichannel sound recorder mounted on a quadcopter. In addition to recording and storing locally the sound from multiple microphones simultaneously, the embedded system can connect wirelessly to a remote terminal to transfer audio files for further processing. This will be the first stage towards creating a fully embedded solution for drone audition. We present experimental results obtained by state-of-the-art drone audition algorithms applied to the sound recorded by the embedded system.

eess.AS↗

arXiv2021

Locally symmetric lattices for storage ring light sources

In this paper, a new lattice concept called the locally symmetric lattice is proposed for storage ring light sources. In this new lattice, beta functions are made locally symmetric about two mirror planes of the lattice cell, and the phase advances between the two mirror planes satisfy the condition of nonlinear dynamics cancellation. There are two kinds of locally symmetric lattices, corresponding to two symmetric representations of lattice cell. In a locally symmetric lattice, main nonlinear effects caused by sextupoles can be effectively cancelled within one lattice cell, and generally there can also be many knobs of sextupoles available for further optimizing the nonlinear dynamics. Two kinds of locally symmetric lattices are designed for a 2.2 GeV diffraction-limited storage ring to demonstrate the lattice concept.

physics.acc-ph↗

arXiv2020

A Novel Graphic Bending Transformation on Benchmark

Classical benchmark problems utilize multiple transformation techniques to increase optimization difficulty, e.g., shift for anti centering effect and rotation for anti dimension sensitivity. Despite testing the transformation invariance, however, such operations do not really change the landscape's "shape", but rather than change the "view point". For instance, after rotated, ill conditional problems are turned around in terms of orientation but still keep proportional components, which, to some extent, does not create much obstacle in optimization. In this paper, inspired from image processing, we investigate a novel graphic conformal mapping transformation on benchmark problems to deform the function shape. The bending operation does not alter the function basic properties, e.g., a unimodal function can almost maintain its unimodality after bent, but can modify the shape of interested area in the search space. Experiments indicate the same optimizer spends more search budget and encounter more failures on the conformal bent functions than the rotated version. Several parameters of the proposed function are also analyzed to reveal performance sensitivity of the evolutionary algorithms.

cs.NE↗

arXiv2020

Lattice dynamics of topological Dirac semimetal LaAgSb$_{2}$ with charge density wave ordering

LaAgSb$_{2}$ is a rare material, which offers the opportunity to investigate the complex interplay between charge density wave (CDW) ordering and topology protected electronic band structure. As both of these phenomena are governed by the structural symmetries, a comprehensive study of the lattice dynamics is highly desirable. In this report, we present the results of temperature and pressure dependent Raman spectroscopy and x-ray diffraction in single crystalline LaAgSb$_{2}$. Our results confirm that Raman spectroscopy is a highly sensitive tool to probe CDW ordering phenomenon, particularly the low-temperature second CDW transition in LaAgSb$_{2}$, which appears as a very weak anomaly in most experiments. The crystal orientation-dependent measurements provide the evolution of Raman modes with crystallographic symmetries and can be further studied through group symmetry analysis. The low-temperature x-ray diffraction data show the emergence of structural modulations corresponding to the CDW instability. The combined high-pressure Raman spectroscopy and synchrotron x-ray diffraction reveal multiple structural phase transitions through lowering of crystalline symmetries, which are also expected to lead to electronic topological transitions.

cond-mat.mtrl-sci↗

arXiv2020

Disentangling Action Sequences: Discovering Correlated Samples

Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning. This improves interpretability, enables the performance of down-stream tasks, and enables controllable generative models. However, this domain is challenged by the abstract notion and incomplete theories to support unsupervised disentanglement learning. We demonstrate the data itself, such as the orientation of images, plays a crucial role in disentanglement and instead of the factors, and the disentangled representations align the latent variables with the action sequences. We further introduce the concept of disentangling action sequences which facilitates the description of the behaviours of the existing disentangling approaches. An analogy for this process is to discover the commonality between the things and categorizing them. Furthermore, we analyze the inductive biases on the data and find that the latent information thresholds are correlated with the significance of the actions. For the supervised and unsupervised settings, we respectively introduce two methods to measure the thresholds. We further propose a novel framework, fractional variational autoencoder (FVAE), to disentangle the action sequences with different significance step-by-step. Experimental results on dSprites and 3D Chairs show that FVAE improves the stability of disentanglement.

cs.LG↗

arXiv2020

Spin-orbit coupling and linear crossings of dipolar magnons in van der Waals antiferromagnets

A magnon spin-orbit coupling, induced by the dipole-dipole interaction, is derived in monoclinic-stacked bilayer honeycomb spin lattice with perpendicular magnetic anisotropy and antiferromagnetic interlayer coupling. Linear crossings are predicted in the magnon spectrum around the band minimum in G valley, as well as in the high frequency range around the zone boundary. The linear crossings in K and K' valleys, which connect the acoustic and optical bands, can be gapped when the intralayer dipole-dipole or Kitaev interactions exceed the interlayer dipole-dipole interaction, resulting in a phase transition from semimetal to insulator. Our results are useful for analyzing the magnon spin dynamics and transport properties in van der Waals antiferromagnet.

cond-mat.mes-hall↗

arXiv2020

A Class of Multilevel Nonregular Designs for Studying Quantitative Factors

Fractional factorial designs are widely used for designing screening experiments. Nonregular fractional factorial designs can have better properties than regular designs, but their construction is challenging. Current research on the construction of nonregular designs focuses on two-level designs. We provide a novel class of multilevel nonregular designs by permuting levels of regular designs. We develop a theory illustrating how levels can be permuted without computer search and accordingly propose a sequential method for constructing nonregular designs. Compared to regular designs, these nonregular designs can provide more accurate estimations on factorial effects and more efficient screening for experiments with quantitative factors. We further explore the space-filling property of the obtained designs and demonstrate their superiority.

stat.ME↗

arXiv2020

Predicting Network Controllability Robustness: A Convolutional Neural Network Approach

Network controllability measures how well a networked system can be controlled to a target state, and its robustness reflects how well the system can maintain the controllability against malicious attacks by means of node-removals or edge-removals. The measure of network controllability is quantified by the number of external control inputs needed to recover or to retain the controllability after the occurrence of an unexpected attack. The measure of the network controllability robustness, on the other hand, is quantified by a sequence of values that record the remaining controllability of the network after a sequence of attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming. In this paper, a method to predict the controllability robustness based on machine learning using a convolutional neural network is proposed, motivated by the observations that 1) there is no clear correlation between the topological features and the controllability robustness of a general network, 2) the adjacency matrix of a network can be regarded as a gray-scale image, and 3) the convolutional neural network technique has proved successful in image processing without human intervention. Under the new framework, a fairly large number of training data generated by simulations are used to train a convolutional neural network for predicting the controllability robustness according to the input network-adjacency matrices, without performing conventional attack simulations. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting controllability robustness of different network configurations is accurate and reliable with very low overheads.

eess.SY↗

arXiv2020

Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition

To better understand the correlation between network topological features and the robustness of network controllability in a general setting, this paper suggests a practical approach to searching for optimal network topologies with given numbers of nodes and edges. Since theoretical analysis seems impossible at least in the present time, exhaustive search based on optimization techniques is employed, firstly for a group of small-sized networks that are realistically workable, where \textit{exhaustive} means 1) all possible network structures with the given numbers of nodes and edges are computed and compared, and 2) all possible node-removal sequences are considered. A main contribution of this paper is the observation of an empirical necessary condition (ENC) from the results of exhaustive search, which shrinks the search space to quickly find an optimal solution. ENC shows that the maximum and minimum in- and out-degrees of an optimal network structure should be almost identical, or within a very narrow range, i.e., the network should be extremely homogeneous. Edge rectification towards the satisfaction of the ENC is then designed and evaluated. Simulation results on large-sized synthetic and real-world networks verify the effectiveness of both the observed ENC and the edge rectification scheme. As more operations of edge rectification are performed, the network is getting closer to exactly satisfying the ENC, and consequently the robustness of the network controllability is enhanced towards optimum.

eess.SY↗

arXiv2020

Comparison of Modeling SPARC spiral galaxies' rotation curves: halo models vs MOND

We investigate a sub-sample of the rotation curves consisting of 45 HSB non-bulgy spiral galaxies selected from SPARC (Spitzer Photometry and Accurate Rotation Curves) database by using two dark halo models (NFW and Burkert) and MOdified Newtonian Dynamics (MOND) theory. Among these three models, the core-dominated Burkert halo model provides a better description of the observed data ($χ_ν^2$ = 0.33) than Navarro, Frenk and White (NFW, $χ_ν^2$= 0.45) and MOND model ($χ_ν^2$ = 0.58). So our results show that, for dark halo models, the selected 45 HSB non-bulgy spiral galaxies prefer a cored density profile to the cuspy one (NFW); We also positively find that there is a correlation between $ρ_0$ and $r_0$ in Burkert model. For MOND fits, when we take $a_0$ as a free parameter, there is no obvious correlation between $a_0$ and disk central surface brightness at 3.6 $μm$ of these HSB spiral galaxies, which is in line with the basic assumption of MOND that $a_0$ should be a universal constant. Interestingly, our fittings gives $a_0$ an average value of $(0.74 \pm 0.45) \times 10^{- 8}\rm {cm\ s^{- 2}}$ if we exclude the three highest values in the sample, which is smaller than the standard value ($1.21 \times 10^{-8}\rm {cm\ s^{- 2}}$).

astro-ph.GA↗

arXiv2020

A Framework of Hierarchical Attacks to Network Controllability

Network controllability robustness reflects how well a networked dynamical system can maintain its controllability against destructive attacks. This paper investigates the network controllability robustness from the perspective of a malicious attack. A framework of hierarchical attack is proposed, by means of edge- or node-removal attacks. Edges (or nodes) in a target network are classified hierarchically into categories, with different priorities to attack. The category of critical edges (or nodes) has the highest priority to be selected for attack. Extensive experiments on nine synthetic networks and nine real-world networks show the effectiveness of the proposed hierarchical attack strategies for destructing the network controllability. From the protection point of view, this study suggests that the critical edges and nodes should be hidden from the attackers. This finding helps better understand the network controllability and better design robust networks.

physics.soc-ph↗

arXiv2020

On the vanishing discount problem from the negative direction

It has been proved in [10] that the unique viscosity solution of \begin{equation}\label{abs}\tag{*} λu_λ+H(x,d_x u_λ)=c(H)\qquad\hbox{in $M$}, \end{equation} uniformly converges, for $λ\rightarrow 0^+$, to a specific solution $u_0$ of the critical equation \[ H(x,d_x u)=c(H)\qquad\hbox{in $M$}, \] where $M$ is a closed and connected Riemannian manifold and $c(H)$ is the critical value. In this note, we consider the same problem for $λ\rightarrow 0^-$. In this case, viscosity solutions of equation \eqref{abs} are not unique, in general, so we focus on the asymptotics of the minimal solution $u_λ^-$ of \eqref{abs}. Under the assumption that constant functions are subsolutions of the critical equation, we prove that the $u_λ^-$ also converges to $u_0$ as $λ\rightarrow 0^-$. Furthermore, we exhibit an example of $H$ for which equation \eqref{abs} admits a unique solution for $λ<0$ as well.

math.AP↗

arXiv2020

A wolbachia infection model with free boundary

Scientists have been seeking ways to use Wolbachia to eliminate the mosquitoes that spread human diseases. Could Wolbachia be the determining factor in controlling the mosquito-borne infectious diseases? To answer this question mathematically, we develop a reaction-diffusion model with free boundary in a one-dimensional environment. We divide the female mosquito population into two groups: one is the uninfected mosquito population that grows in the whole region while the other is the mosquito population infected with Wolbachia that occupies a finite small region and invades the environment with a spreading front governed by a free boundary satisfying the well-known one-phase Stefan condition. For the resulting free boundary problem, we establish criteria under which spreading and vanishing occur. Our results provide useful insights on designing a feasible mosquito releasing strategy to invade the whole mosquito population with Wolbachia infection and thus eventually eradicate the mosquito-borne diseases.

math.AP↗

arXiv2020

Weak KAM solutions of Hamilton-Jacobi equations with decreasing dependence on unknown functions

We consider the Hamilton-Jacobi equation \[{H}(x,u,Du)=0,\quad x\in M, \] where $M$ is a connected, closed and smooth Riemannian manifold, ${H}(x,u,p)$ satisfies Tonelli conditions with respect to $p$ and certain decreasing condition with respect to $u$. Based on a dynamical approach developed in \cite{WWY,WWY1,WWY2}, we obtain a series of properties for weak KAM solutions (equivalently, viscosity solutions) of the stationary equation and the long time behavior of viscosity solutions of the evolutionary equation on the Cauchy problem \begin{equation*} \begin{cases} w_t+{H}(x,w,w_x)=0,\quad (x,t)\in M\times (0,+\infty),\\ w(x,0)=φ(x), \quad x\in M. \end{cases} \end{equation*}

math.DS↗

arXiv2020

Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features

In sentiment classification, the enormous amount of textual data, its immense dimensionality, and inherent noise make it extremely difficult for machine learning classifiers to extract high-level and complex abstractions. In order to make the data less sparse and more statistically significant, the dimensionality reduction techniques are needed. But in the existing dimensionality reduction techniques, the number of components needs to be set manually which results in loss of the most prominent features, thus reducing the performance of the classifiers. Our prior work, i.e., Term Presence Count (TPC) and Term Presence Ratio (TPR) have proven to be effective techniques as they reject the less separable features. However, the most prominent and separable features might still get removed from the initial feature set despite having higher distributions among positive and negative tagged documents. To overcome this problem, we have proposed a new framework that consists of two-dimensionality reduction techniques i.e., Sentiment Term Presence Count (SentiTPC) and Sentiment Term Presence Ratio (SentiTPR). These techniques reject the features by considering term presence difference for SentiTPC and ratio of the distribution distinction for SentiTPR. Additionally, these methods also analyze the total distribution information. Extensive experimental results exhibit that the proposed framework reduces the feature dimension by a large scale, and thus significantly improve the classification performance.

cs.IR↗

arXiv2020

CoAug-MR: An MR-based Interactive Office Workstation Design System via Augmented Multi-Person Collaboration

Digital prototyping and evaluation using 3D modeling and digital human models are becoming more practical for customizing products to the preference of a user. However, the 3D modeling is less accessible to casual users, and digital human models suffer from insufficient body data and less intuitive illustration on how people use the product or how it accommodates to their body. Recently, VR-supported 'Do It Yourself' design has achieved real-time ergonomic evaluation with users themselves by capturing their poses, however, it lacks reliability and quality of design. In this paper, we explore a multi-person interactive design approach that enables designers, users, and even ergonomists to collaborate to achieve effective and reliable design and prototyping tasks. Mixed Reality that utilizes Hololens and motion tracking devices had been developed to provide instant design feedback and evaluation and to experience prototyping in physical space. We evaluate the system based on the usability study, where casual users and designers are engaged in the interactive process of designing items with respect to the body information, the preference, and the environment.

cs.HC↗

arXiv2020

Dipolar spin waves in uniaxial easy-axis antiferromagnets: A natural topological nodal-line semimetal

The existence of the magnetostatic surface spin waves in ferromagnets, known as Damon-Eshbach mode, was recently demonstrated to originate from the topology of the dipole-dipole interaction. In this work, we study the topological characteristics of magnons in easy-axis antiferromagnets with uniaxial anisotropy. The dipolar spin waves are found to be, driven by the dipole-dipole interaction, in a topological nodal-line semimetal phase, which hosts Damon-Eshbach-type surface modes due to the bulk-edge correspondence. The long wavelength character of dipolar spin waves makes our proposal valid for any natural uniaxial easy-axis antiferromagnet, and thus enriches the candidates of topological magnonic materials. In contrast to the nonreciprocal property in ferromagnetic case, the surface modes with opposite momentum coexist at each surface, but with different chiralities. Such a chirality-momentum or spin-momentum locking, similar to that of electronic surface states in topological insulators, offers the opportunity to design novel chirality-based magnonic devices in antiferromagnets.

cond-mat.mes-hall↗