arXiv subjects

Jia Zhang

Publications and source records attributed to Jia Zhang.

At least 127 records · Page 7Linked to original sources

arXiv2022

Goal-Driven Context-Aware Next Service Recommendation for Mashup Composition

As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services. To create a mashup, it gets not only time-consuming but also error-prone for developers to find suitable services from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected to the current step as well as its mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of our approach.

cs.SE↗

arXiv2022

Efficient Ambient LoRa Backscatter with On-Off Keying Modulation

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in-band OOK modulated signals over the LoRa transmissions. Our design enables the backsactter signal to work in the same frequency band of the carrier signal, meanwhile achieving flexible data rate at different transmission range. The key contributions of Aloba include: (1) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals. (2) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. We further adopt link coding mechanism and interleave operation to enhance the reliability of backscatter signal decoding. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 199.4 Kbps data rate at various distances, 52.4 times higher than PLoRa.

cs.NI↗

arXiv2022

A dynamically discovered and characterized non-accreting neutron star -- M dwarf binary candidate

Optical time-domain surveys can unveil and characterize exciting but less-explored non-accreting and/or non-beaming neutron stars (NS) in binaries. Here we report the discovery of such a NS candidate using the LAMOST spectroscopic survey. The candidate, designated LAMOST J112306.9+400736 (hereafter J1123), is in a single-lined spectroscopic binary containing an optically visible M star. The star's large radial velocity variation and ellipsoidal variations indicate a relatively massive unseen companion. Utilizing follow-up spectroscopy from the Palomar 200-inch telescope and high-precision photometry from TESS, we measure a companion mass of $1.24_{-0.03}^{+0.03}~M_{\odot}$. Main-sequence stars with this mass are ruled out, leaving a NS or a massive white dwarf (WD). Although a massive WD cannot be ruled out, the lack of UV excess radiation from the companion supports the NS hypothesis. Deep radio observations with FAST yielded no detections of either pulsed or persistent emission. J1123 is not detected in numerous X-ray and gamma-ray surveys. These non-detections suggest that the NS candidate is not presently accreting and pulsing. Our work exemplifies the capability of discovering compact objects in non-accreting close binaries by synergizing the optical time-domain spectroscopy and high-cadence photometry.

astro-ph.SR↗

arXiv2022

Whole-Body Lesion Segmentation in 18F-FDG PET/CT

There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers. Recent advances in the medical image segmentation shows the nnUNET is feasible for diverse tasks. However, lesion segmentation in the PET images is not straightforward, because lesion and physiological uptake has similar distribution patterns. The Distinction of them requires extra structural information in the CT images. The present paper introduces a nnUNet based method for the lesion segmentation task. The proposed model is designed on the basis of the joint 2D and 3D nnUNET architecture to predict lesions across the whole body. It allows for automated segmentation of potential lesions. We evaluate the proposed method in the context of AutoPet Challenge, which measures the lesion segmentation performance in the metrics of dice score, false-positive volume and false-negative volume.

eess.IV↗

arXiv2022

Compact 459 nm Cs cell optical frequency standard with $2.1\times{10}^{-13}/\sqrtτ$ short-term stability

We achieve a compact optical frequency standard with an extended cavity diode laser locked to the 459 nm 6S$_{1/2}$ - 7P$_{1/2}$ transition of thermal $^{133}$Cs atoms in a $ϕ$ 10 mm $\times$ 50 mm glass cell, using modulation transfer spectroscopy (MTS). The self-estimated frequency stability of this laser is $1.4\times{10}^{-14}/\sqrtτ$. With heterodyne measurement, we verify the linewidth-narrowing effect of MTS locking and measure the frequency stability of the locked laser. The linewidth of each laser is reduced from the free-running 69.6 kHz to 10.3 kHz after MTS stabilization, by a factor of 6.75. The Allan deviation measured via beat detection is $2.1\times{10}^{-13}/\sqrtτ$ for each MTS-stabilized laser. In addition, we measure the hyperfine structure of the 7P$_{1/2}$ energy level based on the heterodyne measurements, and calculate the magnetic dipole constant $A$ of the Cs 7P$_{1/2}$ level to be 94.38(6) MHz, which agrees well with previous measurements. This compact optical frequency standard can also be used in other applications that require high-stability lasers, such as laser interferometry, laser cooling, geodesy, and so on.

physics.atom-ph↗

arXiv2022

Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition

As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition. This paper proposes a novel NLP-inspired approach to recommending services throughout a workflow development process, based on incrementally learning latent service representation from workflow provenance. A workflow composition process is formalized as a step-wise, context-aware service generation procedure, which is mapped to next-word prediction in a natural language sentence. Historical service dependencies are extracted from workflow provenance to build and enrich a knowledge graph. Each path in the knowledge graph reflects a scenario in a data analytics experiment, which is analogous to a sentence in a conversation. All paths are thus formalized as composable service sequences and are mined, using various patterns, from the established knowledge graph to construct a corpus. Service embeddings are then learned by applying deep learning model from the NLP field. Extensive experiments on the real-world dataset demonstrate the effectiveness and efficiency of the approach.

cs.SE↗

arXiv2022

Culling the herd of moments with penalized empirical likelihood

Models defined by moment conditions are at the center of structural econometric estimation, but economic theory is mostly agnostic about moment selection. While a large pool of valid moments can potentially improve estimation efficiency, in the meantime a few invalid ones may undermine consistency. This paper investigates the empirical likelihood estimation of these moment-defined models in high-dimensional settings. We propose a penalized empirical likelihood (PEL) estimation and establish its oracle property with consistent detection of invalid moments. The PEL estimator is asymptotically normally distributed, and a projected PEL procedure further eliminates its asymptotic bias and provides more accurate normal approximation to the finite sample behavior. Simulation exercises demonstrate excellent numerical performance of these methods in estimation and inference.

econ.EM↗

arXiv2022

Cross-Technology Communication for the Internet of Things: A Survey

The ever-developing Internet of Things (IoT) brings the prosperity of wireless sensing and control applications. In many scenarios, different wireless technologies coexist in the shared frequency medium as well as the physical space. Such wireless coexistence may lead to serious cross-technology interference (CTI) problems, e.g. channel competition, signal collision, throughput degradation. Compared with traditional methods like interference avoidance, tolerance, and concurrency mechanism, direct and timely information exchange among heterogeneous devices is therefore a fundamental requirement to ensure the usability, inter-operability, and reliability of the IoT. Under this circumstance, Cross-Technology Communication (CTC) technique thus becomes a hot topic in both academic and industrial fields, which aims at directly exchanging data among heterogeneous devices that follow different standards. This paper comprehensively summarizes the CTC techniques and reveals that the key challenge for CTC lies in the heterogeneity of IoT devices, including the incompatibility of technical standards and the asymmetry of connection capability. Based on the above finding, we present a taxonomy of the existing CTC works (packet-level CTCs and physical-level CTCs) and compare the existing CTC techniques in terms of throughput, reliability, hardware modification, and concurrency.

cs.NI↗

arXiv2022

Tunneling Magnetoresistance in Noncollinear Antiferromagnetic Tunnel Junctions

Antiferromagnetic (AFM) spintronics has emerged as a subfield of spintronics driven by the advantages of antiferromagnets producing no stray fields and exhibiting ultrafast magnetization dynamics. The efficient method to detect an AFM order parameter, known as the Néel vector, by electric means is critical to realize concepts of AFM spintronics. Here, we demonstrate that non-collinear AFM metals, such as Mn3Sn, exhibit a momentum dependent spin polarization which can be exploited in AFM tunnel junctions to detect the Néel vector. Using first-principles calculations based on density functional theory, we predict a tunneling magnetoresistance (TMR) effect as high as 300% in AFM tunnel junctions with Mn3Sn electrodes, where the junction resistance depends on the relative orientation of their Néel vectors and exhibits four non-volatile resistance states. We argue that the spin-split band structure and the related TMR effect can also be realized in other non-collinear AFM metals like Mn3Ge, Mn3Ga, Mn3Pt, and Mn3GaN. Our work provides a robust method for detecting the Néel vector in non-collinear antiferromagnets via the TMR effect, which may be useful for their application in AFM spintronic devices.

cond-mat.mtrl-sci↗

arXiv2022

AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation

As a specific semantic segmentation task, aerial imagery segmentation has been widely employed in high spatial resolution (HSR) remote sensing images understanding. Besides common issues (e.g. large scale variation) faced by general semantic segmentation tasks, aerial imagery segmentation has some unique challenges, the most critical one among which lies in foreground-background imbalance. There have been some recent efforts that attempt to address this issue by proposing sophisticated neural network architectures, since they can be used to extract informative multi-scale feature representations and increase the discrimination of object boundaries. Nevertheless, many of them merely utilize those multi-scale representations in ad-hoc measures but disregard the fact that the semantic meaning of objects with various sizes could be better identified via receptive fields of diverse ranges. In this paper, we propose Adaptive Focus Framework (AF$_2$), which adopts a hierarchical segmentation procedure and focuses on adaptively utilizing multi-scale representations generated by widely adopted neural network architectures. Particularly, a learnable module, called Adaptive Confidence Mechanism (ACM), is proposed to determine which scale of representation should be used for the segmentation of different objects. Comprehensive experiments show that AF$_2$ has significantly improved the accuracy on three widely used aerial benchmarks, as fast as the mainstream method.

cs.CV↗

arXiv2022

Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting

Time series forecasting plays a key role in a variety of domains. In a lot of real-world scenarios, there exist multiple forecasting entities (e.g. power station in the solar system, stations in the traffic system). A straightforward forecasting solution is to mine the temporal dependency for each individual entity through 1d-CNN, RNN, transformer, etc. This approach overlooks the relations between these entities and, in consequence, loses the opportunity to improve performance using spatial-temporal relation. However, in many real-world scenarios, beside explicit relation, there could exist crucial yet implicit relation between entities. How to discover the useful implicit relation between entities and effectively utilize the relations for each entity under various circumstances is crucial. In order to mine the implicit relation between entities as much as possible and dynamically utilize the relation to improve the forecasting performance, we propose an attentional multi-graph neural network with automatic graph learning (A2GNN) in this work. Particularly, a Gumbel-softmax based auto graph learner is designed to automatically capture the implicit relation among forecasting entities. We further propose an attentional relation learner that enables every entity to dynamically pay attention to its preferred relations. Extensive experiments are conducted on five real-world datasets from three different domains. The results demonstrate the effectiveness of A2GNN beyond several state-of-the-art methods.

cs.LG↗

arXiv2022

Energy relaxation and electron-phonon coupling in laser-excited metals

The rate of energy transfer between electrons and phonons is investigated by a first principles framework for electron temperatures up to $T_e=50000$ K while considering the lattice at ground state. Two typical but differently complex metals are investigated, namely Aluminium and Copper. In order to reasonably take the electronic excitation effect into account, we adopt finite temperature density functional theory and linear response to determine the electron-temperature-dependent Eliashberg function and electron density of states. Of the three branch-dependent electron-phonon coupling strengths, the longitudinal acoustic mode plays a dominant role in the electron-phonon coupling for Aluminium for all temperatures considered here, but for Copper it only dominates above an electron temperature of $T_e=40000$ K. The second moment of the Eliashberg function and the electron phonon coupling constant at room temperature $T_e=315$ K show good agreement with other results. For increasing electron temperatures, we show the limits of the $T=0$ approximation for the Eliashberg function. Our present work provides a rich perspective on the phonon dynamics and this will help to improve insight into the underlying mechanism of energy flow in ultra-fast laser-metal interaction.

cond-mat.other↗

arXiv2021

Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation

Chronic wounds significantly impact quality of life. If not properly managed, they can severely deteriorate. Image-based wound analysis could aid in objectively assessing the wound status by quantifying important features that are related to healing. However, the high heterogeneity of the wound types, image background composition, and capturing conditions challenge the robust segmentation of wound images. We present Detect-and-Segment (DS), a deep learning approach to produce wound segmentation maps with high generalization capabilities. In our approach, dedicated deep neural networks detected the wound position, isolated the wound from the uninformative background, and computed the wound segmentation map. We evaluated this approach using one data set with images of diabetic foot ulcers. For further testing, 4 supplemental independent data sets with larger variety of wound types from different body locations were used. The Matthews' correlation coefficient (MCC) improved from 0.29 when computing the segmentation on the full image to 0.85 when combining detection and segmentation in the same approach. When tested on the wound images drawn from the supplemental data sets, the DS approach increased the mean MCC from 0.17 to 0.85. Furthermore, the DS approach enabled the training of segmentation models with up to 90% less training data while maintaining the segmentation performance.

cs.CV↗

arXiv2021

Current-Perpendicular-to-Plane Giant Magnetoresistance Effect in van der Waals Heterostructures

Spin-dependent transport in a full van der Waals (vdW) giant magnetoresistance (GMR) junctions with the structure of Fe3GeTe2/XTe2/Fe3GeTe2 (X = Pt, Pd) has been investigated by using first-principles calculations. The ballistic conductance, magnetoresistance (MR) and resistance-area product (RA) have been calculated in a current-perpendicular-to-plane (CPP) geometry. A giant magnetoresistance of around 2000% and RA less than 0.3 Ω μm2 have been found in the proposed vdW CPP GMR. In addition, the spin-orbit coupling effect on transport and anisotropy magnetoresistance (AMR) has also been investigated. The calculated AMR is found to be around 20% in Fe3GeTe2/trilayer-PdTe2/Fe3GeTe2 CPP GMR. Both GMR and AMR in the proposed vdW CPP GMR mainly originate from the bulk electronic structure properties of Fe3GeTe2. This work demonstrates a vdW CPP GMR with superior advantages including perpendicular magnetic anisotropy, large GMR, low RA as well as sizable AMR may stimulate future experimental explorations and should be appealing for their applications in spintronic devices including magnetic sensor and memory.

cond-mat.mtrl-sci↗

arXiv2021

Ferroelectric control of Néel vector in L10 type of antiferromagnetic films

How to efficiently manipulate the Néel vector of antiferromagnets (AFM) by electric methods is one of the major focuses in current antiferromagnetic spintronics. In this work, we investigated the ferroelectric control of magnetism in AFM L10-MnPt/BaTiO3 bilayers structures by using first-principles calculation. We studied the effect of ferroelectric polarization reversal on magnetic crystalline anisotropy (MCA) of L10-MnPt films with different interface structures. Our results predict a large perpendicular MCA in L10-MnPt films with Pt-O interface, while an in-plane MCA with Mn-O interface when they are interfaced with ferroelectric BaTiO3. In addition, the magnitude of MCA for both interfaces can be modulated efficiently by the polarization reversal of BaTiO3. The ferroelectric control of MCA has been analyzed based on second order perturbation theory, and it can be mainly attributed to the ferroelectric polarization driven redistribution of Pt-5d orbital occupation around Fermi energy. Especially, for Mn-O interface, the Néel vector can be switched between in-plane [100] and [110] directions, or even from in-plane to out-of-plane at certain film thickness by reversing ferroelectric polarization. Our results may provide a non-volatile concept for ferroelectric control of Néel vector in L10-antiferromagnets, which could stimulate experimental investigations on magnetoelectric effect of antiferromagnets and promote its applications in low-power consumption spintronic memory devices.

cond-mat.mtrl-sci↗

arXiv2021

Giant Piezospintronic Effect in a Noncollinear Antiferromagnetic Metal

One of the main bottleneck issues for room-temperature antiferromagnetic spintronic devices is the small signal read-out owing to the limited anisotropic magnetoresistance in antiferromagnets. However, this could be overcome by either utilizing the Berry-curvature-induced anomalous Hall resistance in noncollinear antiferromagnets or establishing tunnel junction devices based on effective manipulation of antiferromagnetic spins. In this work, we demonstrate the giant piezoelectric strain control of the spin structure and the anomalous Hall resistance in a noncollinear antiferromagnetic metal - D019 hexagonal Mn3Ga. Furthermore, we built tunnel junction devices with a diameter of 200 nm to amplify the maximum tunneling resistance ratio to more than 10% at room-temperature, which thus implies significant potential of noncollinear antiferromagnets for large signal-output and high-density antiferromagnetic spintronic device applications.

cond-mat.mes-hall↗

arXiv2020

Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations

Multi-agent reinforcement learning (MARL) has been increasingly explored to learn the cooperative policy towards maximizing a certain global reward. Many existing studies take advantage of graph neural networks (GNN) in MARL to propagate critical collaborative information over the interaction graph, built upon inter-connected agents. Nevertheless, the vanilla GNN approach yields substantial defects in dealing with complex real-world scenarios since the generic message passing mechanism is ineffective between heterogeneous vertices and, moreover, simple message aggregation functions are incapable of accurately modeling the combinational interactions from multiple neighbors. While adopting complex GNN models with more informative message passing and aggregation mechanisms can obviously benefit heterogeneous vertex representations and cooperative policy learning, it could, on the other hand, increase the training difficulty of MARL and demand more intense and direct reward signals compared to the original global reward. To address these challenges, we propose a new cooperative learning framework with pre-trained heterogeneous observation representations. Particularly, we employ an encoder-decoder based graph attention to learn the intricate interactions and heterogeneous representations that can be more easily leveraged by MARL. Moreover, we design a pre-training with local actor-critic algorithm to ease the difficulty in cooperative policy learning. Extensive experiments over real-world scenarios demonstrate that our new approach can significantly outperform existing MARL baselines as well as operational research solutions that are widely-used in industry.

cs.LG↗

arXiv2020

Van der Waals Multiferroic Tunnel Junctions

Multiferroic tunnel junctions (MFTJs) have aroused significant interest due to their functional properties useful for non-volatile memory devices. So far, however, all the existing MFTJs have been based on perovskite-oxide heterostructures limited by a relatively high resistance-area (RA) product unfavorable for practical applications. Here, using first-principles calculations, we explore spin-dependent transport properties of van der Waals (vdW) MFTJs which consist of two-dimensional (2D) ferromagnetic FenGeTe2 (n = 3, 4, 5) electrodes and 2D ferroelectric In2Se3 barrier layers. We demonstrate that such FemGeTe2/In2Se3/FenGeTe2 (m, n = 3, 4, 5) MFTJs exhibit multiple non-volatile resistance states associated with different polarization orientation of the ferroelectric In2Se3 layer and magnetization alignment of the two ferromagnetic FenGeTe2 layers. We find a remarkably low RA product which makes the proposed vdW MFTJs superior to the conventional MFTJs in terms of their promise for non-volatile memory applications.

cond-mat.mes-hall↗