arXiv subjects

Teng Long

Publications and source records attributed to Teng Long.

48 records · Page 3Linked to original sources

arXiv2021

Visible Watermark Removal via Self-calibrated Localization and Background Refinement

Superimposing visible watermarks on images provides a powerful weapon to cope with the copyright issue. Watermark removal techniques, which can strengthen the robustness of visible watermarks in an adversarial way, have attracted increasing research interest. Modern watermark removal methods perform watermark localization and background restoration simultaneously, which could be viewed as a multi-task learning problem. However, existing approaches suffer from incomplete detected watermark and degraded texture quality of restored background. Therefore, we design a two-stage multi-task network to address the above issues. The coarse stage consists of a watermark branch and a background branch, in which the watermark branch self-calibrates the roughly estimated mask and passes the calibrated mask to background branch to reconstruct the watermarked area. In the refinement stage, we integrate multi-level features to improve the texture quality of watermarked area. Extensive experiments on two datasets demonstrate the effectiveness of our proposed method.

cs.CV↗

arXiv2021

Decidability of Liveness on the TSO Memory Model

An important property of concurrent objects is whether they support progress -a special case of liveness-guarantees, which ensure the termination of individual method calls under system fairness assumptions. Liveness properties have been proposed for concurrent objects. Typical liveness properties includelock-freedom,wait-freedom,deadlock-freedom,starvation-freedom and obstruction-freedom. It is known that the five liveness properties above are decidable on the Sequential Consistency (SC) memory model for a bounded number of processes. However, the problem of decidability of liveness for finite state concurrent programs running on relaxed memory models remains open. In this paper we address this problem for the Total Store Order (TSO) memory model,as found in the x86 architecture. We prove that lock-freedom, wait-freedom,deadlock-freedom and starvation-freedom are undecidable on TSO for a bounded number of processes, while obstruction-freedom is decidable.

cs.FL↗

arXiv2021

Anomalously low thermal conductivity of two-dimensional GaP monolayers: A comparative study of the group GaX (X = N, P, As)

With the successful synthesis of the two-dimensional (2D) gallium nitride (GaN) in a planar honeycomb structure, the phonon transport properties of 2D GaN have been reported. However, it remains unclear for the thermal transport in Ga-based materials by substituting N to other elements in the same main group, which is of more broad applications. In this paper, based on first-principles calculations, we performed a comprehensive study on the phonon transport properties of 2D GaX (X = N, P, and As) with planar or buckled honeycomb structures. The thermal conductivity of GaP (1.52 Wm-1K-1) is found unexpectedly ultra-low, which is in sharp contrast to GaN and GaAs despite their similar honeycomb geometry structure. Based on PJTE theory, GaP and GaAs stabilize in buckling structure, different from the planar structure of GaN. Compared to GaN and GaAs, strong phonon-phonon scattering is found in GaP due to the strongest phonon anharmonicity. Given electronic structures, deep insight is gained into the phonon transport that the delocalization of electrons in GaP is restricted due to the buckling structure. Thus, non-bonding lone pair electrons of P atoms induce nonlinear electrostatic forces upon thermal agitation, leading to increased phonon anharmonicity in the lattice, thus reducing thermal conductivity. Our study offers a fundamental understanding of phonon transport in GaX monolayers with honeycomb structure, which will enrich future studies of nanoscale phonon transport in 2D materials

cond-mat.mtrl-sci↗

arXiv2021

Enhanced anomalous Nernst effects in ferromagnetic materials driven by Weyl nodes

Based on high-throughput first-principles calculations, we evaluated the anomalous Hall and anomalous Nernst conductivities of 266 transition-metal-based ferromagnetic compounds. Detailed analysis based on the symmetries and Berry curvatures reveals that the origin of singular-like behaviour of anomalous Hall/Nernst conductivities can be mostly attributed to the appearance of Weyl nodes or nodal lines located in the proximity of the Fermi energy, which can be further tailored by external stimuli such as biaxial strains and magnetic fields. Moreover, such calculations are enabled by the automated construction of Wannier functions with a success rate of 92%, which paves the way to perform accurate high-throughput evaluation of the physical properties such as the transport properties using the Wannier interpolation

cond-mat.mtrl-sci↗

arXiv2021

Inverse design of crystal structures for multicomponent systems

We developed an inverse design framework enabling automated generation of stable multi-component crystal structures by optimizing the formation energies in the latent space based on reversible crystal graphs with continuous representation. It is demonstrated that 9,160 crystal structures can be generated out of 50,000 crystal graphs, leading to 8,310 distinct cases using a training set of 52,615 crystal structures from Materials Project. Detailed analysis on 15 selected systems reveals that unreported crystal structures below the convex hull can be discovered in 6 material systems. Moreover, the generation efficiency can be further improved by considering extra hypothetical structures in the training. This paves the way to perform inverse design of multicomponent materials with possible multi-objective optimization.

cond-mat.mtrl-sci↗

arXiv2021

Joint Optimization for Coordinated Charging Control of Commercial Electric Vehicles Under Distributed Hydrogen Energy Supply

The transition to the zero-carbon power system is underway accelerating recently. Hydrogen energy and electric vehicles (EVs) are promising solutions on the supply and demand sides. This paper presents a novel architecture that includes hydrogen production stations (HPSs), fast charging stations (FCSs), and commercial EVs. The proposed architecture jointly optimizes the distributed hydrogen energy dispatch and the EV charging location selection, and is formulated by a time-varying bi-level bipartite graph (T-BBG) model for real-time operation. We develop a bi-level iteration optimization method combining linear programming (LP) and Kuhn-Munkres (KM) algorithm to solve the joint problem whose optimality is proved theoretically. The effectiveness of the proposed architecture on reducing the operating cost is verified via case studies in Shanghai. The proposed method outperforms other strategies and improves the performance by at least 13% which shows the potential economic benefits of the joint architecture. The convergence and impact of the pile number, battery capacity, EV speed and penalty factor are assessed.

eess.SY↗

arXiv2020

Thermal conductivity of h-BN monolayers using machine learning interatomic potential

Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands the evaluation of thermal conductivities which are numerically expensive. In this work, we applied the recently developed machine learning interatomic potential (MLIP) to evaluate the thermal conductivity of hexagonal boron nitride monolayers. The MLIP is obtained using the Gaussian approximation potential (GAP) method, and the resulting lattice dynamical properties and thermal conductivity are compared with those obtained from explicit frozen phonon calculations. It is observed that accurate thermal conductivity can be obtained based on MLIP constructed with about 30% representative configurations, and the high-order force constants provide a more reliable benchmark on the quality of MLIP than the harmonic approximation.

cond-mat.mtrl-sci↗

arXiv2020

On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs

Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of natural language. Practically, however, VAEs with autoregressive decoders often suffer from posterior collapse, a phenomenon where the model learns to ignore the latent variables, causing the sequence VAE to degenerate into a language model. In this paper, we argue that posterior collapse is in part caused by the lack of dispersion in encoder features. We provide empirical evidence to verify this hypothesis, and propose a straightforward fix using pooling. This simple technique effectively prevents posterior collapse, allowing model to achieve significantly better data log-likelihood than standard sequence VAEs. Comparing to existing work, our proposed method is able to achieve comparable or superior performances while being more computationally efficient.

cs.LG↗

arXiv2019

A Cross-Domain Transferable Neural Coherence Model

Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.

cs.CL↗

arXiv2017

Radial Velocity Retrieval for Multichannel SAR Moving Targets with Time-Space Doppler De-ambiguity

In this paper, with respect to multichannel synthetic aperture radars (SAR), we first formulate the problems of Doppler ambiguities on the radial velocity (RV) estimation of a ground moving target in range-compressed domain, range-Doppler domain and image domain, respectively. It is revealed that in these problems, a cascaded time-space Doppler ambiguity (CTSDA) may encounter, i.e., time domain Doppler ambiguity (TDDA) in each channel arises first and then spatial domain Doppler ambiguity (SDDA) among multi-channels arises second. Accordingly, the multichannel SAR systems with different parameters are investigated in three different cases with diverse Doppler ambiguity properties, and a multi-frequency SAR is then proposed to obtain the RV estimation by solving the ambiguity problem based on Chinese remainder theorem (CRT). In the first two cases, the ambiguity problem can be solved by the existing closed-form robust CRT. In the third case, it is found that the problem is different from the conventional CRT problems and we call it a double remaindering problem in this paper. We then propose a sufficient condition under which the double remaindering problem, i.e., the CTSDA, can also be solved by the closed-form robust CRT. When the sufficient condition is not satisfied for a multi-channel SAR, a searching based method is proposed. Finally, some results of numerical experiments are provided to demonstrate the effectiveness of the proposed methods.

cs.IT↗

arXiv2016

Does $\ell_p$-minimization outperform $\ell_1$-minimization?

In many application areas we are faced with the following question: Can we recover a sparse vector $x_o \in \mathbb{R}^N$ from its undersampled set of noisy observations $y \in \mathbb{R}^n$, $y=A x_o+w$. The last decade has witnessed a surge of algorithms and theoretical results addressing this question. One of the most popular algorithms is the $\ell_p$-regularized least squares (LPLS) given by the following formulation: \[ \hat{x}(γ,p )\in \arg\min_x \frac{1}{2}\|y - Ax\|_2^2+γ\|x\|_p^p, \] where $p \in [0,1]$. Despite the non-convexity of these problems for $p<1$, they are still appealing because of the following folklores in compressed sensing: (i) $\hat{x}(γ,p )$ is closer to $x_o$ than $\hat{x}(γ,1)$. (ii) If we employ iterative methods that aim to converge to a local minima of LPLS, then under good initialization these algorithms converge to a solution that is closer to $x_o$ than $\hat{x}(γ,1)$. In spite of the existence of plenty of empirical results that support these folklore theorems, the theoretical progress to establish them has been very limited. This paper aims to study the above folklore theorems and establish their scope of validity. Starting with approximate message passing algorithm as a heuristic method for solving LPLS, we study the impact of initialization on the performance of AMP. Then, we employ the replica analysis to show the connection between the solution of AMP and $\hat{x}(γ, p)$ in the asymptotic settings. This enables us to compare the accuracy of $\hat{x}(γ,p)$ for $p \in [0,1]$. In particular, we will characterize the phase transition and noise sensitivity of LPLS for every $0\leq p\leq 1$ accurately. Our results in the noiseless setting confirm that LPLS exhibits the same phase transition for every $0\leq p <1$ and this phase transition is much higher than that of LASSO.

cs.IT↗

arXiv2016

Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data

Recent work in learning vector-space embeddings for multi-relational data has focused on combining relational information derived from knowledge bases with distributional information derived from large text corpora. We propose a simple approach that leverages the descriptions of entities or phrases available in lexical resources, in conjunction with distributional semantics, in order to derive a better initialization for training relational models. Applying this initialization to the TransE model results in significant new state-of-the-art performances on the WordNet dataset, decreasing the mean rank from the previous best of 212 to 51. It also results in faster convergence of the entity representations. We find that there is a trade-off between improving the mean rank and the hits@10 with this approach. This illustrates that much remains to be understood regarding performance improvements in relational models.

cs.CL↗