arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 1,531 records · Page 85Linked to original sources

arXiv2018

A multigrid correction scheme for a new Steklov eigenvalue problem in inverse scattering

We propose a multigrid correction scheme to solve a new Steklov eigenvalue problem in inverse scattering. With this scheme, solving an eigenvalue problem in a fine finite element space is reduced to solve a series of boundary value problems in fine finite element spaces and a series of eigenvalue problems in the coarsest finite element space. And the coefficient matrices associated with those linear systems are constructed to be symmetric and positive definite. We prove error estimates of eigenvalues and eigenfunctions. Numerical results coincide in theoretical analysis and indicate our scheme is highly efficient in solving the eigenvalue problem.

math.NA↗

arXiv2018

Two-grid discretizations and a local finite element scheme for a non-selfadjoint Stekloff eigenvalue problem

In this paper, for a new Stekloff eigenvalue problem which is non-selfadjoint and not $H^1$-elliptic, we establish and analyze two kinds of two-grid discretization scheme and a local finite element scheme. We present the error estimates of approximations of two-grid discretizations. We also prove a local error estimate which is suitable for the case that the local refined region contains singular points lying on the boundary of domain. Numerical experiments are reported finally to show the efficiency of our schemes.

math.NA↗

arXiv2018

Learning to Multitask

Multitask learning has shown promising performance in many applications and many multitask models have been proposed. In order to identify an effective multitask model for a given multitask problem, we propose a learning framework called learning to multitask (L2MT). To achieve the goal, L2MT exploits historical multitask experience which is organized as a training set consists of several tuples, each of which contains a multitask problem with multiple tasks, a multitask model, and the relative test error. Based on such training set, L2MT first uses a proposed layerwise graph neural network to learn task embeddings for all the tasks in a multitask problem and then learns an estimation function to estimate the relative test error based on task embeddings and the representation of the multitask model based on a unified formulation. Given a new multitask problem, the estimation function is used to identify a suitable multitask model. Experiments on benchmark datasets show the effectiveness of the proposed L2MT framework.

cs.LG↗

arXiv2018

Image Co-segmentation via Multi-scale Local Shape Transfer

Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category. In real-world cases, however, common foreground objects often vary greatly in appearance, making their global shapes highly inconsistent across images and difficult to be segmented. To address this problem, this paper proposes a novel co-segmentation approach that transfers patch-level local object shapes which appear more consistent across different images. In our framework, a multi-scale patch neighbourhood system is first generated using proposal flow on arbitrary image-pair, which is further refined by Locally Linear Embedding. Based on the patch relationships, we propose an efficient algorithm to jointly segment the objects in each image while transferring their local shapes across different images. Extensive experiments demonstrate that the proposed method can robustly and effectively segment common objects from an image set. On iCoseg, MSRC and Coseg-Rep dataset, the proposed approach performs comparable or better than the state-of-thearts, while on a more challenging benchmark Fashionista dataset, our method achieves significant improvements.

cs.CV↗

arXiv2018

Parameter Transfer Unit for Deep Neural Networks

Parameters in deep neural networks which are trained on large-scale databases can generalize across multiple domains, which is referred as "transferability". Unfortunately, the transferability is usually defined as discrete states and it differs with domains and network architectures. Existing works usually heuristically apply parameter-sharing or fine-tuning, and there is no principled approach to learn a parameter transfer strategy. To address the gap, a parameter transfer unit (PTU) is proposed in this paper. The PTU learns a fine-grained nonlinear combination of activations from both the source and the target domain networks, and subsumes hand-crafted discrete transfer states. In the PTU, the transferability is controlled by two gates which are artificial neurons and can be learned from data. The PTU is a general and flexible module which can be used in both CNNs and RNNs. Experiments are conducted with various network architectures and multiple transfer domain pairs. Results demonstrate the effectiveness of the PTU as it outperforms heuristic parameter-sharing and fine-tuning in most settings.

cs.LG↗

arXiv2018

High-resolution Tunneling Spectroscopy of ABA-stacked Trilayer Graphene

ABA-stacked trilayer graphene (TLG), the simplest system consisting of both massless and massive Dirac fermions, is expected to exhibit many interesting broken-symmetry quantum Hall states and interaction-induced phenomena. However, difficulties in microscopically identifying the stacking order of the TLG and limited spectroscopic resolution have stymied experimental probes of these interesting states and phenomena in scanning tunneling microscopy (STM) studies. Here we studied the detailed features of the electronic structure in the ABA TLG by using high-resolution STM measurements. Landau-level (LL) crossings of the massless and massive Dirac fermions, and effective mass renormalization of the massive Dirac fermions were observed, indicating strong electron-electron interactions in the ABA TLG. Most unexpectedly, we observed unconventional splittings of the lowest LLs for both the massless and massive Dirac fermions in high perpendicular magnetic fields. These splittings of the LLs, which are beyond the description of tight-binding calculations, reveal unexplored broken-symmetry quantum Hall states in the ABA TLG induced by many-body effects.

cond-mat.mtrl-sci↗

arXiv2018

Expert Finding in Community Question Answering: A Review

The rapid development recently of Community Question Answering (CQA) satisfies users quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. Sparse data and new features violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify all the existing solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the field of expert finding in CQA. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boosting the performance. Further, we compare the performance of different models on different types of matching tasks, including text vs. text, graph vs. text, audio vs. text and video vs. text. The results can help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA.

cs.IR↗

arXiv2018

Domain Wall Motion Driven by Laplace Pressure in CoFeB-MgO Nanodots with Perpendicular Anisotropy

We have studied the magnetization reversal of CoFeB-MgO nanodots with perpendicular anisotropy for size ranging from w=400 nm to 1 μm. Contrary to previous experiments, the switching field distribution is shifted toward lower magnetic fields as the size of the elements is reduced with a mean switching field varying as 1/w. We show that this mechanism can be explained by the nucleation of a pinned magnetic domain wall (DW) at the edges of the nanodots where damages are introduced by the patterning process. As the surface tension (Laplace pressure) applied on the DW increases when reducing the size of the nanodots, we demonstrate that the depinning field to reverse the entire elements varies as 1/w. These results suggest that the presence of DWs has to be considered in the switching process of nanoscale elements and open a path toward scalable spintronic devices.

physics.app-ph↗

arXiv2018

LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text

Collaborative filtering (CF) is the key technique for recommender systems. Pure CF approaches exploit the user-item interaction data (e.g., clicks, likes, and views) only and suffer from the sparsity issue. Items are usually associated with content information such as unstructured text (e.g., abstracts of articles and reviews of products). CF can be extended to leverage text. In this paper, we develop a unified neural framework to exploit interaction data and content information seamlessly. The proposed framework, called LCMR, is based on memory networks and consists of local and centralized memories for exploiting content information and interaction data, respectively. By modeling content information as local memories, LCMR attentively learns what to exploit with the guidance of user-item interaction. On real-world datasets, LCMR shows better performance by comparing with various baselines in terms of the hit ratio and NDCG metrics. We further conduct analyses to understand how local and centralized memories work for the proposed framework.

cs.IR↗

arXiv2018

Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment

Dialogue policy transfer enables us to build dialogue policies in a target domain with little data by leveraging knowledge from a source domain with plenty of data. Dialogue sentences are usually represented by speech-acts and domain slots, and the dialogue policy transfer is usually achieved by assigning a slot mapping matrix based on human heuristics. However, existing dialogue policy transfer methods cannot transfer across dialogue domains with different speech-acts, for example, between systems built by different companies. Also, they depend on either common slots or slot entropy, which are not available when the source and target slots are totally disjoint and no database is available to calculate the slot entropy. To solve this problem, we propose a Policy tRansfer across dOMaIns and SpEech-acts (PROMISE) model, which is able to transfer dialogue policies across domains with different speech-acts and disjoint slots. The PROMISE model can learn to align different speech-acts and slots simultaneously, and it does not require common slots or the calculation of the slot entropy. Experiments on both real-world dialogue data and simulations demonstrate that PROMISE model can effectively transfer dialogue policies across domains with different speech-acts and disjoint slots.

cs.CL↗

arXiv2018

Tuning the Dzyaloshinskii-Moriya Interaction in Pt/Co/MgO heterostructures through MgO thickness

The interfacial Dzyaloshinskii-Moriya interaction (DMI) in the ferromagnetic/heavy metal ultra-thin film structures , has attracted a lot of attention thanks to its capability to stabilize Neel-type domain walls (DWs) and magnetic skyrmions for the realization of non-volatile memory and logic devices. In this study, we demonstrate that magnetic properties in perpendicularly magnetized Ta/Pt/Co/MgO/Pt heterostructures, such as magnetization and DMI, can be significantly influenced through both the MgO and the Co ultrathin film thickness. By using a field-driven creep regime domain expansion technique, we find that non-monotonic tendencies of DMI field appear when changing the thickness of MgO and the MgO thickness corresponding to the largest DMI field varies as a function of the Co thicknesses. We interpret this efficient control of DMI as subtle changes of both Pt/Co and Co/MgO interfaces, which provide a method to investigate ultra-thin structures design to achieve skyrmion electronics.

physics.app-ph↗

arXiv2018

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system. The embeddings are trained with no explicit labels, yet learn to model a large range of acoustic expressiveness. GSTs lead to a rich set of significant results. The soft interpretable "labels" they generate can be used to control synthesis in novel ways, such as varying speed and speaking style - independently of the text content. They can also be used for style transfer, replicating the speaking style of a single audio clip across an entire long-form text corpus. When trained on noisy, unlabeled found data, GSTs learn to factorize noise and speaker identity, providing a path towards highly scalable but robust speech synthesis.

cs.CL↗

arXiv2018

Heterogeneous Memristive Devices Enabled by Magnetic Tunnel Junction Nanopillars Surrounded by Resistive Silicon Switches

Emerging non-volatile memories (NVMs) have currently attracted great interest for their potential applications in advanced low-power information storage and processing technologies. Conventional NVMs, such as magnetic random access memory (MRAM) and resistive random access memory (RRAM) suffer from limitations of low tunnel magnetoresistance (TMR), low access speed or finite endurance. NVMs with synergetic advantages are still highly desired for future computer architectures. Here, we report a heterogeneous memristive device composed of a magnetic tunnel junction (MTJ) nanopillar surrounded by resistive silicon switches, named resistively enhanced MTJ (Re-MTJ), that may be utilized for novel memristive memories, enabling new functionalities that are inaccessible for conventional NVMs. The Re-MTJ device features a high ON/OFF ratio of >1000% and multilevel resistance behaviour by combining magnetic switching together with resistive switching mechanisms. The magnetic switching originates from the MTJ, while the resistive switching is induced by a point-switching filament process that is related to the mobile oxygen ions. Microscopic evidence of silicon aggregated as nanocrystals along the edges of the nanopillars verifies the synergetic mechanism of the heterogeneous memristive device. This device may provide new possibilities for advanced memristive memory and computing architectures, e.g., in-memory computing and neuromorphics.

physics.app-ph↗

arXiv2018

Skyrmions in magnetic tunnel junctions

In this work, we demonstrate that skyrmions can be nucleated in the free layer of a magnetic tunnel junction (MTJ) with Dzyaloshinskii-Moriya interactions (DMI) by a spin-polarized current with the assistance of stray fields from the pinned layer. The size, stability and number of created skyrmions can be tuned by either the DMI strength or the stray field distribution. The interaction between the stray field and the DMI effective field is discussed. A device with multi-level tunneling magnetoresistance is proposed, which could pave the ways for skyrmion-MTJ-based multi-bit storage and artificial neural network computation. Our results may facilitate the efficient nucleation and electrical detection of skyrmions.

cond-mat.mes-hall↗

arXiv2018

Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions

This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize timedomain waveforms from those spectrograms. Our model achieves a mean opinion score (MOS) of $4.53$ comparable to a MOS of $4.58$ for professionally recorded speech. To validate our design choices, we present ablation studies of key components of our system and evaluate the impact of using mel spectrograms as the input to WaveNet instead of linguistic, duration, and $F_0$ features. We further demonstrate that using a compact acoustic intermediate representation enables significant simplification of the WaveNet architecture.

cs.CL↗

arXiv2018

Segregation growth and self-organization of ordered S atomic superlattices confined at interface between graphene and substrates

Ordered atomic-scale superlattices on surface hold great interest both for basic science and for potential applications in advanced technology. However, controlled fabrication of superlattices down to atomic scale has proven exceptionally challenging. Here we demonstrate the segregation-growth and self-organization of ordered S atomic superlattices confined at the interface between graphene and S-rich Cu substrates. Scanning tunneling microscope (STM) studies show that, by finely controlling the growth temperature, we obtain well-ordered S (sub)nanometer-cluster superlattice and monoatomic superlattices with various periods at the interface. These atomic superlattices are stable in atmospheric environment and robust even after high-temperature annealing (~ 350 oC). Our experiments demonstrate that the S monoatomic superlattice can drive graphene into the electronic Kekulé distortion phase when the period of the ordered S adatoms is commensurate with graphene lattice. Our results not only open a road to realize atomic-scale superlattices at interfaces, but also provide a new route to realize exotic electronic states in graphene.

cond-mat.mtrl-sci↗

arXiv2018

To Relay or not to Relay: Open Distance and Optimal Deployment for Linear Underwater Acoustic Networks

Existing works have widely studied relay-aided underwater acoustic networks under some specialized relay distributions, e.g., equidistant and rectangular-grid. In this paper, we investigate two fundamental problems that under which conditions a relay should be deployed and where to deploy it if necessary in terms of the energy and delay performance in linear underwater acoustic networks. To address these two problems, we first accurately approximate the complicated effective bandwidth and transmit power in the logarithm domain to formulate an energy minimization problem. By analyzing the formulation, we discover a critical transmission distance, defined as open distance, and explicitly show that a relay should not be deployed if the transmission distance is less than the open distance and should be otherwise. Most importantly, we derive a closed-form and easy-to-calculate expression for the open distance and also strictly prove that the optimal placing position is at the middle point of the link when a relay should be introduced. Moreover, although this paper considers a linear two-hop relay network as the first step, our derived results can be applied to construct energy-efficient and delay-friendly multi-hop networks. Simulation results validate our theoretical analysis and show that properly introducing a relay can dramatically reduce the network energy consumption almost without increasing the end-to-end delay.

cs.IT↗

arXiv2017

Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning

Extracting relations from text corpora is an important task in text mining. It becomes particularly challenging when focusing on weakly-supervised relation extraction, that is, utilizing a few relation instances (i.e., a pair of entities and their relation) as seeds to extract more instances from corpora. Existing distributional approaches leverage the corpus-level co-occurrence statistics of entities to predict their relations, and require large number of labeled instances to learn effective relation classifiers. Alternatively, pattern-based approaches perform bootstrapping or apply neural networks to model the local contexts, but still rely on large number of labeled instances to build reliable models. In this paper, we study integrating the distributional and pattern-based methods in a weakly-supervised setting, such that the two types of methods can provide complementary supervision for each other to build an effective, unified model. We propose a novel co-training framework with a distributional module and a pattern module. During training, the distributional module helps the pattern module discriminate between the informative patterns and other patterns, and the pattern module generates some highly-confident instances to improve the distributional module. The whole framework can be effectively optimized by iterating between improving the pattern module and updating the distributional module. We conduct experiments on two tasks: knowledge base completion with text corpora and corpus-level relation extraction. Experimental results prove the effectiveness of our framework in the weakly-supervised setting.

cs.CL↗