arXiv subjects

Qianqian Wang

Publications and source records attributed to Qianqian Wang.

84 records · Page 5Linked to original sources

arXiv2021

A Splitting-Detection Joint-Decision Receiver for Ultrasonic Intra-Body Communications

Ultrasonic intra-body communication (IBC) is a promising enabling technology for future healthcare applications, due to low attenuation and medical safety of ultrasonic waves for the human body. A splitting receiver, referred to as the splitting-detection separate-decision (SDSD) receiver, is introduced for ultrasonic pulse-based IBCs, and SDSD can significantly improve bit-error rate (BER) performance over the traditional coherent-detection (CD) and energy detection (ED) receivers. To overcome the high complexity and improve the BER performance of SDSD, a splitting-detection joint-decision (SDJD) receiver is proposed. The core idea of SDJD is to split the received signal into two steams that can be separately processed by CD and ED, and then summed up as joint decision variables to achieve diversity combining. The theoretical channel capacity and BER of the SDSD and SDJD are derived for M-ary pulse position modulation (M-PPM) and PPM with spreading codes. The derivation takes into account the channel noise, intra-body channel fading, and channel estimation error. Simulation results verify the theoretical analysis and show that both SDSD and SDJD can achieve higher channel capacity and lower BER than the CD and ED receivers with perfect channel estimation, while SDJD can achieve the lowest BER with imperfect channel estimation.

cs.IT↗

arXiv2020

Designing Efficient Metal Contacts to Two-Dimensional Semiconductors MoSi$_2$N$_4$ and WSi$_2$N$_4$ Monolayers

Metal contacts to two-dimensional (2D) semiconductors are ubiquitous in modern electronic and optoelectronic devices. Such contacts are, however, often plagued by strong Fermi level pinning (FLP) effect which reduces the tunability of the Schottky barrier height (SBH) and degrades the performance of 2D-semiconductor-based devices. In this work, we show that monolayer MoSi$_2$N$_4$ and WSi$_2$N$_4$ - a recently synthesized 2D material class with exceptional mechanical and electronic properties - exhibit strongly suppressed FLP and wide-range tunable SBH when contacted by metals. An exceptionally large SBH slope parameter of S=0.7 is obtained, which outperform the vast majority of other 2D semiconductors. Such surprising behavior arises from the unique morphology of MoSi$_2$N$_4$ and WSi$_2$N$_4$. The outlying Si-N layer forms a native atomic layer that protects the semiconducting inner-core from the perturbance of metal contacts, thus suppressing the FLP. Our findings reveal the potential of MoSi$_2$N$_4$ and WSi$_2$N$_4$ monolayers as a novel 2D material platform for designing high-performance and energy-efficient 2D nanodevices.

cond-mat.mes-hall↗

arXiv2020

Two-dimensional van der Waals electrical contact to monolayer MoSi$_2$N$_4$

Two-dimensional (2D) MoSi$_2$N$_4$ monolayer is an emerging class of air-stable 2D semiconductor possessing exceptional electrical and mechanical properties. Despite intensive recent research efforts devoted to uncover the material properties of MoSi$_2$N$_4$, the physics of electrical contacts to MoSi$_2$N$_4$ remains largely unexplored thus far. In this work, we study the van der Waals heterostructures composed of MoSi$_2$N$_4$ contacted by graphene and NbS$_2$ monolayers using first-principle density functional theory calculations. We show that the MoSi$_2$N$_4$/NbS$_2$ contact exhibits an ultralow Schottky barrier height (SBH), which is beneficial for nanoelectronics applications. For MoSi$_2$N$_4$/graphene contact, the SBH can be modulated via interlayer distance or via external electric fields, thus opening up an opportunity for reconfigurable and tunable nanoelectronic devices. Our findings provide insights on the physics of 2D electrical contact to MoSi$_2$N$_4$, and shall offer a critical first step towards the design of high-performance electrical contacts to MoSi$_2$N$_4$-based 2D nanodevices.

cond-mat.mtrl-sci↗

arXiv2020

Hidden Footprints: Learning Contextual Walkability from 3D Human Trails

Predicting where people can walk in a scene is important for many tasks, including autonomous driving systems and human behavior analysis. Yet learning a computational model for this purpose is challenging due to semantic ambiguity and a lack of labeled data: current datasets only tell you where people are, not where they could be. We tackle this problem by leveraging information from existing datasets, without additional labeling. We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints. However, this augmented data is still sparse. We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss. Using this strategy, we demonstrate a model that learns to predict a walkability map from a single image. We evaluate our model on the Waymo and Cityscapes datasets, demonstrating superior performance compared to baselines and state-of-the-art models.

cs.CV↗

arXiv2020

Generative Partial Multi-View Clustering

Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis. However, existing MVC methods mainly assume that each sample appears in all the views, without considering the incomplete view case due to data corruption, sensor failure, equipment malfunction, etc. In this study, we design and build a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem by explicitly generating the data of missing views. The main idea of GP-MVC lies at two-fold. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views. Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views. These two steps could be promoted mutually, where learning common representations facilitates data imputation and the generated data could further explores the view consistency. Moreover, an weighted adaptive fusion scheme is implemented to exploit the complementary information among different views. Experimental results on four benchmark datasets are provided to show the effectiveness of the proposed GP-MVC over the state-of-the-art methods.

cs.CV↗

arXiv2019

Lifelong Spectral Clustering

In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we aim to explore the problem of spectral clustering in a lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC). Its goal is to efficiently learn a model for a new spectral clustering task by selectively transferring previously accumulated experience from knowledge library. Specifically, the knowledge library of L2SC contains two components: 1) orthogonal basis library: capturing latent cluster centers among the clusters in each pair of tasks; 2) feature embedding library: embedding the feature manifold information shared among multiple related tasks. As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks. Meanwhile, a general online update formulation is derived to alternatively update the basis library and feature library. Finally, the empirical experiments on several real-world benchmark datasets demonstrate that our L2SC model can effectively improve the clustering performance when comparing with other state-of-the-art spectral clustering algorithms.

cs.LG↗

arXiv2019

Visual Tactile Fusion Object Clustering

Object clustering, aiming at grouping similar objects into one cluster with an unsupervised strategy, has been extensivelystudied among various data-driven applications. However, most existing state-of-the-art object clustering methods (e.g., single-view or multi-view clustering methods) only explore visual information, while ignoring one of most important sensing modalities, i.e., tactile information which can help capture different object properties and further boost the performance of object clustering task. To effectively benefit both visual and tactile modalities for object clustering, in this paper, we propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework for visual-tactile fusion clustering. Specifically, deep matrix factorization constrained by an under-complete Auto-Encoder-like architecture is employed to jointly learn hierarchical expression of visual-tactile fusion data, and preserve the local structure of data generating distribution of visual and tactile modalities. Meanwhile, a graph regularizer is introduced to capture the intrinsic relations of data samples within each modality. Furthermore, we propose a modality-level consensus regularizer to effectively align thevisual and tactile data in a common subspace in which the gap between visual and tactile data is mitigated. For the model optimization, we present an efficient alternating minimization strategy to solve our proposed model. Finally, we conduct extensive experiments on public datasets to verify the effectiveness of our framework.

cs.LG↗

arXiv2019

Nearly-degenerate $p_x+ip_y$ and $d_{x^2-y^2}$ pairing symmetry in the heavy fermion superconductor YbRh$_2$Si$_2$

Recent discovery of superconductivity in YbRh$_2$Si$_2$ has raised particular interest in its pairing mechanism and gap symmetry. Here we propose a phenomenological theory of its superconductivity and investigate possible gap structures by solving the multiband Eliashberg equations combining realistic Fermi surfaces from first-principles calculations and a quantum critical form of magnetic pairing interactions. The resulting gap symmetry shows sensitive dependence on the in-plane propagation wave vector of the quantum critical fluctuations, suggesting that superconductivity in YbRh$_2$Si$_2$ is located on the border of $(p_x+ip_y)$ and $d_{x^2-y^2}$-wave solutions. This leads to two candidate phase diagrams: one has only a spin-triplet $(p_x+ip_y)$-wave superconducting phase; the other contains multiple phases with a spin-singlet $d_{x^2-y^2}$-wave state at zero field and a field-induced spin-triplet $(p_x+ip_y)$-wave state. In addition, the electron pairing is found to be dominated by the `jungle-gym' Fermi surface rather than the `doughnut'-like one, in contrast to previous thought. This requests a more elaborate and renewed understanding of the electronic properties of YbRh$_2$Si$_2$.

cond-mat.supr-con↗

arXiv2019

Representative Task Self-selection for Flexible Clustered Lifelong Learning

Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are with prescribed size, and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL3). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our proposed FCL3model firstly transfers knowledge from these libraries to encode the new task, i.e.,effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then, 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time; or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multi-task datasets, and the experimental results demonstrate that our FCL3 model can achieve better performance than most lifelong learning frameworks, even batch clustered multi-task learning models.

cs.LG↗

arXiv2018

Magnetic Navigation of a Rotating Colloidal Swarm Using Ultrasound Images

Microrobots are considered as promising tools for biomedical applications. However, the imaging of them becomes challenges in order to be further applied on in vivo environments. Here we report the magnetic navigation of a paramagnetic nanoparticle based swarm using ultrasound images. The swarm can be generated using simple rotating magnetic fields, resulting in a region containing particles with a high area density. Ultrasound images of the swarm shows a periodic changing of imaging contrast. The reason for such dynamic contrast has been analyzed and experimental results are presented. Moreover, this swarm exhibits enhanced ultrasound imaging in comparison to that formed by individual nanoparticles with a low area density, and the relationship between imaging contrast and area density is testified. Furthermore, the microrobotic swarm can be navigated near a solid surface at different velocities, and the imaging contrast show negligible changes. This method allows us to localize and navigate a microrobotic swarm with enhanced ultrasound imaging indicating a promising approach for imaging of microrobots.

cs.RO↗

arXiv2018

Anisotropic hybridization in a new Kondo lattice compound CeCoInGa$_3$

We report a detailed and comparative study of the single crystal CeCoInGa$_3$ in both experiment and theory. Resistivity measurements reveal the typical behavior of Kondo lattice with the onset temperature of coherence, $T^*\approx 50\,$K. The magnetic specific heat can be well fitted using a spin-fluctuation model at low temperatures, yielding a large Sommerfeld coefficient, $γ\approx172\,$mJ/mol K$^2$ at 6 K, suggesting that this is a heavy-fermion compound with a pronounced coherence effect. The magnetic susceptibility exhibits a broad field-independent peak at $T_χ$ and shows an obvious anisotropy within the $bc$ plane, reflecting the anisotropy of the coherence effect at high temperatures. These are compared with strongly correlated calculations combining first-principles band structure calculations and dynamical mean-field theory. Our results confirm the onset of coherence at about 50 K and reveal a similar anisotropy in the hybridization gap, pointing to a close connection between the hybridization strength of the low-temperature Fermi-liquid state and the high-temperature coherence effect.

cond-mat.str-el↗

arXiv2018

Multi-Image Semantic Matching by Mining Consistent Features

This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the proposed method is able to prune nonrepeatable features and also highly scalable to handle thousands of images. We additionally propose a low-rank constraint to ensure the geometric consistency of feature correspondences over the whole image collection. Besides the competitive performance on multi-graph matching and semantic flow benchmarks, we also demonstrate the applicability of the proposed method for reconstructing object-class models and discovering object-class landmarks from images without using any annotation.

cs.CV↗