arXiv subjects

Yan Zhang

Publications and source records attributed to Yan Zhang.

At least 577 records · Page 32Linked to original sources

arXiv2020

Socially-Aware Robot Planning via Bandit Human Feedback

In this paper, we consider the problem of designing collision-free, dynamically feasible, and socially-aware trajectories for robots operating in environments populated by humans. We define trajectories to be social-aware if they do not interfere with humans in any way that causes discomfort. In this paper, discomfort is defined broadly and, depending on specific individuals, it can result from the robot being too close to a human or from interfering with human sight or tasks. Moreover, we assume that human feedback is a bandit feedback indicating a complaint or no complaint on the part of the robot trajectory that interferes with the humans, and it does not reveal any contextual information about the locations of the humans or the reason for a complaint. Finally, we assume that humans can move in the obstacle-free space and, as a result, human utility can change. We formulate this planning problem as an online optimization problem that minimizes the social value of the time-varying robot trajectory, defined by the total number of incurred human complaints. As the human utility is unknown, we employ zeroth order, or derivative-free, optimization methods to solve this problem, which we combine with off-the-shelf motion planners to satisfy the dynamic feasibility and collision-free specifications of the resulting trajectories. To the best of our knowledge, this is a new framework for socially-aware robot planning that is not restricted to avoiding collisions with humans but, instead, focuses on increasing the social value of the robot trajectories using only bandit human feedback.

cs.RO↗

arXiv2020

Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms.

cs.IT↗

arXiv2020

Nonparametric Bayesian Deconvolution of a Symmetric Unimodal Density

We consider nonparametric measurement error density deconvolution subject to heteroscedastic measurement errors as well as symmetry about zero and shape constraints, in particular unimodality. The problem is motivated by applications where the observed data are estimated effect sizes from regressions on multiple factors, where the target is the distribution of the true effect sizes. We exploit the fact that any symmetric and unimodal density can be expressed as a mixture of symmetric uniform densities, and model the mixing density in a new way using a Dirichlet process location-mixture of Gamma distributions. We do the computations within a Bayesian context, describe a simple scalable implementation that is linear in the sample size, and show that the estimate of the unknown target density is consistent. Within our application context of regression effect sizes, the target density is likely to have a large probability near zero (the near null effects) coupled with a heavy-tailed distribution (the actual effects). Simulations show that unlike standard deconvolution methods, our Constrained Bayesian Deconvolution method does a much better job of reconstruction of the target density. Applications to a genome-wise association study (GWAS) and microarray data reveal similar results.

stat.ME↗

arXiv2020

Thermal energy harvesting using pyroelectric-electrochemical coupling in ferroelectric materials

Recently, the coupling of ferroelectrics with electrochemical reactions has attracted increasing interest for harvesting waste heat. The change of polarisation of a ferroelectric with temperature can be used to influence chemical reactions, especially when the material is cycled near its Curie temperature. In this perspective, we introduce the principle of pyroelectric controlled electrochemical processes by harvesting waste heat energy and explore their potential electrochemical applications, such as water treatment, air purificiation and hydrogen generation. As an emerging approach for driving electrochemical reactions, the presence of thermal fluctuations and/or transient waste heat in the environment has the potential to be the primary thermal input for driving the change in polarisation of a pyroelectric to release charge for such reactions. There are a number of avenues to explore and we summarize strategies for forming multi-functional or hybrid materials and future directions such as selecting pyroelectrics with low Curie temperature (< 100 °C), improved heat conductivity, enhanced surface area or porosity, tailored microstructures and systems capable of operating over a broader temperature range.

physics.app-ph↗

arXiv2020

Pulse-qubit interaction in a superconducting circuit under frictively dissipative environment

Microwave pulses are used ubiquitously to control and measure qubits fabricated on superconducting circuits. Due to continual environmental coupling, the qubits undergo decoherence both when it is free and during its interaction with the microwave pulse. As quantum logic gates are executed through pulse-qubit interaction, we study theoretically the decoherence-induced effects during the interaction, especially the variations of the pulse, under a dissipative environment with linear spectral distribution. We find that a transmissible pulse of finite width adopts an asymmetric multi-hump shape, due to the imbalanced pumping and emitting rates of the qubit during inversion when the environment is present. The pulse shape reduces to a solitonic pulse at vanishing dissipation and a pulse train at strong dissipation. We give detailed analysis of the environmental origin from both the perspectives of envelope and phase of the propagating pulse.

quant-ph↗

arXiv2020

Blockchain for Internet of Things: A Survey

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges such as decentralization, poor interoperability, privacy and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as Blockchain of Things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for 5G beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.

cs.NI↗

arXiv2020

Model-theoretic Characterizations of Existential Rule Languages

Existential rules, a.k.a. dependencies in databases, and Datalog+/- in knowledge representation and reasoning recently, are a family of important logical languages widely used in computer science and artificial intelligence. Towards a deep understanding of these languages in model theory, we establish model-theoretic characterizations for a number of existential rule languages such as (disjunctive) embedded dependencies, tuple-generating dependencies (TGDs), (frontier-)guarded TGDs and linear TGDs. All these characterizations hold for arbitrary structures, and most of them also work on the class of finite structures. As a natural application of these characterizations, complexity bounds for the rewritability of above languages are also identified.

cs.AI↗

arXiv2020

Learning Reinforced Attentional Representation for End-to-End Visual Tracking

Although numerous recent tracking approaches have made tremendous advances in the last decade, achieving high-performance visual tracking remains a challenge. In this paper, we propose an end-to-end network model to learn reinforced attentional representation for accurate target object discrimination and localization. We utilize a novel hierarchical attentional module with long short-term memory and multi-layer perceptrons to leverage both inter- and intra-frame attention to effectively facilitate visual pattern emphasis. Moreover, we incorporate a contextual attentional correlation filter into the backbone network to make our model trainable in an end-to-end fashion. Our proposed approach not only takes full advantage of informative geometries and semantics but also updates correlation filters online without fine-tuning the backbone network to enable the adaptation of variations in the target object's appearance. Extensive experiments conducted on several popular benchmark datasets demonstrate that our proposed approach is effective and computationally efficient.

cs.CV↗

arXiv2019

Deep Self-representative Concept Factorization Network for Representation Learning

In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features. To improve the representation and clustering abilities, DSCF-Net explicitly considers discovering hidden deep semantic features, enhancing the robustness proper-ties of the deep factorization to noise and preserving the local man-ifold structures of deep features. Specifically, DSCF-Net seamlessly integrates the robust deep concept factorization, deep self-expressive representation and adaptive locality preserving feature learning into a unified framework. To discover hidden deep repre-sentations, DSCF-Net designs a hierarchical factorization architec-ture using multiple layers of linear transformations, where the hierarchical representation is performed by formulating the prob-lem as optimizing the basis concepts in each layer to improve the representation indirectly. DSCF-Net also improves the robustness by subspace recovery for sparse error correction firstly and then performs the deep factorization in the recovered visual subspace. To obtain locality-preserving representations, we also present an adaptive deep self-representative weighting strategy by using the coefficient matrix as the adaptive reconstruction weights to keep the locality of representations. Extensive comparison results with several other related models show that DSCF-Net delivers state-of-the-art performance on several public databases.

cs.LG↗

arXiv2019

Siamese Attentional Keypoint Network for High Performance Visual Tracking

In this paper, we investigate the impacts of three main aspects of visual tracking, i.e., the backbone network, the attentional mechanism, and the detection component, and propose a Siamese Attentional Keypoint Network, dubbed SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese lightweight hourglass network is specially designed for visual tracking. It takes advantage of the benefits of the repeated bottom-up and top-down inference to capture more global and local contextual information at multiple scales. Secondly, a novel cross-attentional module is utilized to leverage both channel-wise and spatial intermediate attentional information, which can enhance both discriminative and localization capabilities of feature maps. Thirdly, a keypoints detection approach is invented to trace any target object by detecting the top-left corner point, the centroid point, and the bottom-right corner point of its bounding box. Therefore, our SATIN tracker not only has a strong capability to learn more effective object representations, but also is computational and memory storage efficiency, either during the training or testing stages. To the best of our knowledge, we are the first to propose this approach. Without bells and whistles, experimental results demonstrate that our approach achieves state-of-the-art performance on several recent benchmark datasets, at a speed far exceeding 27 frames per second.

cs.CV↗

arXiv2019

Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation

Recently, the embedding-based recommendation models (e.g., matrix factorization and deep models) have been prevalent in both academia and industry due to their effectiveness and flexibility. However, they also have such intrinsic limitations as lacking explainability and suffering from data sparsity. In this paper, we propose an end-to-end joint learning framework to get around these limitations without introducing any extra overhead by distilling structured knowledge from a differentiable path-based recommendation model. Through extensive experiments, we show that our proposed framework can achieve state-of-the-art recommendation performance and meanwhile provide interpretable recommendation reasons.

cs.IR↗

arXiv2019

Towards Universal Languages for Tractable Ontology Mediated Query Answering

An ontology language for ontology mediated query answering (OMQA-language) is universal for a family of OMQA-languages if it is the most expressive one among this family. In this paper, we focus on three families of tractable OMQA-languages, including first-order rewritable languages and languages whose data complexity of the query answering is in AC0 or PTIME. On the negative side, we prove that there is, in general, no universal language for each of these families of languages. On the positive side, we propose a novel property, the locality, to approximate the first-order rewritability, and show that there exists a language of disjunctive embedded dependencies that is universal for the family of OMQA-languages with locality. All of these results apply to OMQA with query languages such as conjunctive queries, unions of conjunctive queries and acyclic conjunctive queries.

cs.AI↗

arXiv2019

Evolution of 3D Printing Methods and Materials for Electrochemical Energy Storage

Additive manufacturing has revolutionized the building of materials direct from design, allowing high resolution rapid prototyping in complex 3D designs with many materials. 3D printing hasenabled high strength damage-tolerant structures, bioprinted artificial organs and tissues, ultralight metals, medicine, education, prosthetics, architecture, consumer electronics,and as a prototyping tool for engineers and hobbyists alike. 3D printing has emerged as a useful tool for complex electrode and material assembly method for batteries and supercapacitors in recent years. The field initially grew from extrusion-based methods such as fused deposition modelling, and evolved to photopolymerization printing of intricate composites, while supercapacitor technologies less sensitive to solvents more often involved material jetting processes. Underpinning every part of a 3D printable battery and many other devices is the printing method and the nature of the feed material. Material purity, printing fidelity, accuracy, complexity, and the ability to form conductive, ceramic, glassy, or solvent-stable plastics relies on the nature of the feed material or composite to such an extent, that the future of 3D printable batteries and electrochemical energy storage devices will depend on materials and printing methods that are co-operatively informed by the requirements of the device and how it is fabricated. In this Perspective, we address the materials and methods requirements in 3D printable batteries and supercapacitors and outline requirements for the future of the field by linking existing performance limitations to the requirements of printable energy storage materials, casing materials and the direct printing of electrodes and electrolytes. We also look to the future by taking inspiration from additive manufacturing, to posit links between materials and printing methods to allow new form factor cells.

physics.app-ph↗

arXiv2019

The Nematic Energy Scale and the Missing Electron Pocket in FeSe

Superconductivity emerges in proximity to a nematic phase in most iron-based superconductors. It is therefore important to understand the impact of nematicity on the electronic structure. Orbital assignment and tracking across the nematic phase transition prove to be challenging due to the multiband nature of iron-based superconductors and twinning effects. Here, we report a detailed study of the electronic structure of fully detwinned FeSe across the nematic phase transition using angle-resolved photoemission spectroscopy. We clearly observe a nematicity-driven band reconstruction involving dxz, dyz, and dxy orbitals. The nematic energy scale between dxz and dyz bands reaches a maximum of 50 meV at the Brillouin zone corner. We are also able to track the dxz electron pocket across the nematic transition and explain its absence in the nematic state. Our comprehensive data of the electronic structure provide an accurate basis for theoretical models of the superconducting pairing in FeSe.

cond-mat.supr-con↗

arXiv2019

Quantifying the effect of electrical conductivity on the rate-performance of nanocomposite battery electrodes

While it is well-known that electrode conductivity has a critical impact on rate-performance in battery electrodes, this relationship has been quantified only by computer simulations. Here we investigate the relationship between electrode conductivity and rate-performance in Lithium-Nickel-Manganese-Cobalt-Oxide (NMC) cathodes filled with various quantities of carbon black, single-walled carbon nanotubes and graphene. The electrode conductivity is always extremely anisotropic with the out-of-plane conductivity, which is most relevant to rate-performance, roughly 1000 smaller than the in-plane conductivity. For all fillers the conductivity increases with filler loading although the nanotube-filled electrodes show by far the most rapid increase. Fitting capacity versus rate curves yielded the characteristic time associated with charge/discharge. This parameter increased linearly with the inverse of the out-of-plane conductivity, with all data points falling on the same master curve. Using a simple mechanistic model for the characteristic time, we develop an equation which matches the experimental data almost perfectly with no adjustable parameters. This implies that increasing the electrode conductivity improves the rate-performance by decreasing the RC charging time of the electrode. This model shows the effect of electrode resistance on the rate-performance to become negligible in almost all cases once the out-of-plane conductivity of the electrode exceeds 1 S/m. Our results show that this can be achieved by including <1wt% single-walled carbon nanotubes in the electrode.

physics.chem-ph↗

arXiv2019

Energy Autonomous Wearable Sensors for Smart Healthcare: A Review

Energy Autonomous Wearable Sensors (EAWS) have attracted a large interest due to their potential to provide reliable measurements and continuous bioelectric signals, which help to reduce health risk factors early on, ongoing assessment for disease prevention, and maintaining optimum, lifelong health quality. This review paper presents recent developments and state-of-the-art research related to three critical elements that enable an EAWS. The first element is wearable sensors, which monitor human body physiological signals and activities. Emphasis is given on explaining different types of transduction mechanisms presented, and emerging materials and fabrication techniques. The second element is the flexible and wearable energy storage device to drive low-power electronics and the software needed for automatic detection of unstable physiological parameters. The third is the flexible and stretchable energy harvesting module to recharge batteries for continuous operation of wearable sensors. We conclude by discussing some of the technical challenges in realizing energy-autonomous wearable sensing technologies and possible solutions for overcoming them.

eess.SP↗

arXiv2019

Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.

cs.AI↗

arXiv2019

A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy

An image dataset of 10 different size molecules, where each molecule has 2,000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajectories. The purpose of this dataset is to provide a benchmark dataset for the increasing need of machine learning, deep learning and image processing on the study of scattering, imaging and microscopy.

eess.IV↗