arXiv subjects

Wei Tian

Publications and source records attributed to Wei Tian.

At least 55 records · Page 3Linked to original sources

arXiv2023

Modeling and Joint Optimization of Security, Latency, and Computational Cost in Blockchain-based Healthcare Systems

In the era of the Internet of Things (IoT), blockchain is a promising technology for improving the efficiency of healthcare systems, as it enables secure storage, management, and sharing of real-time health data collected by the IoT devices. As the implementations of blockchain-based healthcare systems usually involve multiple conflicting metrics, it is essential to balance them according to the requirements of specific scenarios. In this paper, we formulate a joint optimization model with three metrics, namely latency, security, and computational cost, that are particularly important for IoT-enabled healthcare. However, it is computationally intractable to identify the exact optimal solution of this problem for practical sized systems. Thus, we propose an algorithm called the Adaptive Discrete Particle Swarm Algorithm (ADPSA) to obtain near-optimal solutions in a low-complexity manner. With its roots in the classical Particle Swarm Optimization (PSO) algorithm, our proposed ADPSA can effectively manage the numerous binary and integer variables in the formulation. We demonstrate by extensive numerical experiments that the ADPSA consistently outperforms existing benchmark approaches, including the original PSO, exhaustive search and Simulated Annealing, in a wide range of scenarios.

cs.NE↗

arXiv2023

Polyhedral distortions and unusual magnetic order in spinel FeMn$_{2}$O$_{4}$

Spinel compounds AB$_{2}$X$_{4}$ consist of both tetrahedral (AX$_{4}$) and octahedral (BX$_{6}$) environments with the former forming a diamond lattice and the latter a geometrically frustrated pyrochlore lattice. Exploring the fascinating properties and their correlations with structural features is critical in understanding these materials. FeMn$_{2}$O$_{4}$ has been reported to exhibit one structural transition and two successive magnetic transitions. Here, we report the polyhedral distortions and their correlations to the structural and two magnetic transitions in FeMn$_{2}$O$_{4}$ by employing the high-resolution neutron powder diffraction. While a large trigonal distortion is found even in the high-temperature cubic phase, the first-order cubic-tetragonal structural transition associated with the elongation of both tetrahedra and octahedra along the $c$ axis occurs at $T_{S} \approx$ 750 K, driven by the Jahn-Teller effect of the orbital active B-site Mn$^{3+}$ cation. A strong magnetoelastic coupling is unveiled at $T_{N1}\approx 400$ K as manifested by the appearance of Nèel-type collinear ferrimagnetic order, an anomaly in both tetrahedral and octahedral distortions, as well as an anomalous decrease of the lattice constant $c$ and a weak anomaly of $a$. Upon cooling below $T_{N2}\approx65$ K, it evolves to a noncollinear ferrimagnetic order with a canting of half B-site $Mn^{3+}$/$Fe^{3+}$ spins in the pyrochlore lattice, which is a unique magnetic order among spinels. Such a noncollinear order induces modifications of the O-B-O bond angles in the octahedra without affecting much the bond lengths of the tetrahedra/octahedra. Our study indicates that FeMn$_{2}$O$_{4}$ is a wonderful platform to unveil interesting magnetic order and to investigate their correlations to polyhedral distortions and lattice.

physics.chem-ph↗

arXiv2023

SeqMapPDB: A Standalone Pipeline to Identify Representative Structures of Protein Sequences and Mapping Residue Indices in Real-Time at Proteome Scale

Motivation: 3D structures of proteins provide rich information for understanding their biochemical roles. Identifying the representative protein structures for protein sequences is essential for analysis of proteins at proteome scale. However, there are technical difficulties in identifying the representative structure of a given protein sequence and providing accurate mapping of residue indices. Existing databases of mapping between structures and sequences are usually static that are not suitable for studying proteomes with frequent gene model revisions. They often do not provide reliable and consistent representative structures that maximizes sequence coverage. Furthermore, proteins isomers are usually not properly resolved. Results: To overcome these difficulties, we have developed a computational pipeline called SeqMapPDB to provide high-quality representative PDB structures of given sequences. It provides mapping to structures that fully cover the sequences when available, or to the set of partial non-overlapping structural domains that maximally cover the query sequence. The residue indices are accurate mapped and isomeric proteins are resolved. SeqMapPDB is efficient and can rapidly carry out proteome-wide mapping to the selected version of reference genomes in real-time. Furthermore, SeqMapPDB provides the flexibility of a stand-alone pipeline for large scale mapping of in-house sequence and structure data. Availability: Our method is available at https://bitbucket.org/lianglabuic/seqmappdb with GNU GPL license.

q-bio.GN↗

arXiv2023

A Ring Topology-based Communication-Efficient Scheme for D2D Wireless Federated Learning

Federated learning (FL) is an emerging technique aiming at improving communication efficiency in distributed networks, where many clients often request to transmit their calculated parameters to an FL server simultaneously. However, in wireless networks, the above mechanism may lead to prolonged transmission time due to unreliable wireless transmission and limited bandwidth. This paper proposes a communication scheme to minimize the uplink transmission time for FL in wireless networks. The proposed approach consists of two major elements, namely a modified Ring All-reduce (MRAR) architecture that integrates D2D wireless communications to facilitate the communication process in FL, and a modified Ant Colony Optimization algorithm to identify the optimal composition of the MRAR architecture. Numerical results show that our proposed approach is robust and can significantly reduce the transmission time compared to the conventional star topology. Notably, the reduction in uplink transmission time compared to baseline policies can be substantial in scenarios applicable to large-scale FL, where client devices are densely distributed.

math.OC↗

arXiv2023

Simulation study on the optical processes at deep-sea neutrino telescope sites

The performance of a large-scale water Cherenkov neutrino telescope relies heavily on the transparency of the surrounding water, quantified by its level of light absorption and scattering. A pathfinder experiment was carried out to measure the optical properties of deep seawater in South China Sea with light-emitting diodes (LEDs) as light sources, photon multiplier tubes (PMTs) and cameras as photon sensors. Here, we present an optical simulation program employing the Geant4 toolkit to understand the absorption and scattering processes in the deep seawater, which helps to extract the underlying optical properties from the experimental data. The simulation results are compared with the experimental data and show good agreements. We also verify the analysis methods that utilize various observables of the PMTs and the cameras with this simulation program, which can be easily adapted by other neutrino telescope pathfinder experiments and future large-scale detectors.

astro-ph.IM↗

arXiv2022

An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams

Bitcoin is the most common cryptocurrency involved in cyber scams. Cybercriminals often utilize pseudonymity and privacy protection mechanism associated with Bitcoin transactions to make their scams virtually untraceable. The Ponzi scheme has attracted particularly significant attention among Bitcoin fraudulent activities. This paper considers a multi-class classification problem to determine whether a transaction is involved in Ponzi schemes or other cyber scams, or is a non-scam transaction. We design a specifically designed crawler to collect data and propose a novel Attention-based Long Short-Term Memory (A-LSTM) method for the classification problem. The experimental results show that the proposed model has better efficiency and accuracy than existing approaches, including Random Forest, Extra Trees, Gradient Boosting, and classical LSTM. With correctly identified scam features, our proposed A-LSTM achieves an F1-score over 82% for the original data and outperforms the existing approaches.

cs.CR↗

arXiv2022

Role of Magnetic Defects and Defect-engineering of Magnetic Topological Insulators

Magnetic defects play an important, but poorly understood, role in magnetic topological insulators (TIs). For example, topological surface transport and bulk magnetic properties are controlled by magnetic defects in Bi$_2$Se$_3$-based dilute ferromagnetic (FM) TIs and MnBi$_2$Te$_4$ (MBT)-based antiferromagnetic (AFM) TIs. Despite its nascent ferromagnetism, our inelastic neutron scattering data show that a fraction of the Mn defects in Sb$_2$Te$_3$ form strong AFM dimer singlets within a quintuple block. The AFM superexchange coupling occurs via Mn-Te-Mn linear bonds and is identical to the AFM coupling between antisite defects and the FM Mn layer in MBT, establishing common interactions in the two materials classes. We also find that the FM correlations in (Sb$_{1-x}$Mn$_x$)$_2$Te$_3$ are likely driven by magnetic defects in adjacent quintuple blocks across the van der Waals gap. In addition to providing answers to long-standing questions about the evolution of FM order in dilute TI, these results also show that the evolution of global magnetic order from AFM to FM in Sb-substituted MBT is controlled by defect engineering of the intrablock and interblock coupling.

cond-mat.mtrl-sci↗

arXiv2022

Detecting and Fixing Data Loss Issues in Android Apps

Android apps are event-driven, and their execution is often interrupted by external events. This interruption can cause data loss issues that annoy users. For instance, when the screen is rotated, the current app page will be destroyed and recreated. If the app state is improperly preserved, user data will be lost. In this work, we present an approach and tool iFixDataloss that automatically detects and fixes data loss issues in Android apps. To achieve this, we identify scenarios in which data loss issues may occur, develop strategies to reveal data loss issues, and design patch templates to fix them. Our experiments on 66 Android apps show iFixDataloss detected 374 data loss issues (284 of them were previously unknown) and successfully generated patches for 188 of the 374 issues. Out of 20 submitted patches, 16 have been accepted by developers. In comparison with state-of-the-art techniques, iFixDataloss performed significantly better in terms of the number of detected data loss issues and the quality of generated patches.

cs.SE↗

arXiv2022

Thermal cycling induced alteration of the stacking order and spin-flip in the room temperature van der Waals magnet Fe$_5$GeTe$_2$

The magnetic properties of the quasi-two-dimensional van der Waals magnet Fe$_{5-δ}$GeTe$_2$ (F5GT), which has a high ferromagnetic ordering temperature $T_{\text{C}}$ $\sim$ 315 K, remains to be better understood. It has been demonstrated that the magnetization of F5GT is sensitive to both the Fe deficiency $δ$ and the thermal cycling history. Here, we investigate the structural and magnetic properties of F5GT with a minimal Fe deficiency ($|δ|$ $\le$ 0.1), utilizing combined x-ray and neutron scattering techniques. Our study reveals that the quenched F5GT single crystals experience an irreversible, first-order transition at $T_{\text{S}}$ $\sim$ 110 K upon first cooling, where the stacking order partly or entirely converts from ABC-stacking to AA-stacking order. Importantly, the magnetic properties, including the magnetic moment direction and the enhanced $T_{\text{C}}$ after the thermal cycling, are intimately related to the alteration of the stacking order. Our work highlights the significant influence of the lattice symmetry to the magnetism in F5GT.

cond-mat.str-el↗

arXiv2022

EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, so that 2D-3D point correspondences can be partly learned by backpropagating the gradient w.r.t. object pose. Yet, learning the entire set of unrestricted 2D-3D points from scratch fails to converge with existing approaches, since the deterministic pose is inherently non-differentiable. In this paper, we propose the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose on the SE(3) manifold, essentially bringing categorical Softmax to the continuous domain. The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution. The underlying principle unifies the existing approaches and resembles the attention mechanism. EPro-PnP significantly outperforms competitive baselines, closing the gap between PnP-based method and the task-specific leaders on the LineMOD 6DoF pose estimation and nuScenes 3D object detection benchmarks.

cs.CV↗

arXiv2022

Weak itinerant magnetic phases of La2Ni7

La2Ni7 is an intermetallic compound that is thought to have itinerant magnetism with a small moment ordering below 65 K. A recent study of single crystal samples by Ribeiro et. al. [Phys. Rev. B 105, 014412 (2022)] determined detailed anisotropic H-T phase diagrams and revealed three zero-field magnetic phase transitions at T1 ~ 61.0 K, T2 ~ 56.5K, and T3 ~ 42 K. In that study only the highest temperature phase is shown to have a clear ferromagnetic component. Here we present a single crystal neutron diffraction study determining the propagation vector and magnetic moment direction of the three magnetically ordered phases, two incommensurate and one commensurate, as a function of temperature. The higher temperature phases have similar, incommensurate propagation vectors, but with different ordered moment directions. At lower temperatures the magnetic order becomes commensurate with magnetic moments along the c direction as part of a first-order magnetic phase transition. We find that the low-temperature commensurate magnetic order is consistent with a proposal from earlier DFT calculations.

cond-mat.str-el↗

arXiv2022

Nanometric modulations of the magnetic structure of the element Nd

The rare earth neodymium arguably exhibits the most complex magnetic ordering and series of magnetic phase transitions of the elements. Here we report the results of small-angle neutron scattering (SANS) measurements as a function of temperature and applied magnetic field to study magnetic correlations on nanometer length scales in Nd. The SANS measurements reveal the presence of previously unreported modulation vectors characterizing the ordered spin configuration which exhibit changes in magnitude and direction that are phase dependent. Between 5.9 and 7.6 K the additional modulation vector has a magnitude $Q$ =0.12 Å$^{-1}$ and is primarily due to order of the Nd layers which contain a center of inversion. In this region of the phase diagram, the SANS measurements also identify a phase boundary at $\approx$1 T. An important feature of these modulation vectors is that they indicate the presence of nanometer length scale spin textures which are likely stabilized by frustrated Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions rather than a Dzyaloshinskii-Moriya (DM) exchange interaction.

cond-mat.str-el↗

arXiv2021

Posterior Analysis on IceCube Double Pulse Tau Neutrino Candidates

The IceCube Neutrino Observatory at the South Pole detects Cherenkov light emitted by charged secondary particles created by primary neutrino interactions. Double pulse waveforms can arise from charged current interactions of astrophysical tau neutrinos with nucleons in the ice and the subsequent decay of tau leptons. The previous 8-year tau double pulse analysis found three tau neutrino candidate events. Among them, the most promising one observed in 2014 is located very near the dust layer in the middle of the detector. A posterior analysis on this event will be presented in this paper, using a new ice model treatment with continuously varying nuisance parameters to do the targeted Monte Carlo re-simulation for tau and other background neutrino ensembles. The impact of different ice models on the expected signal and background statistics will also be discussed.

astro-ph.HE↗

arXiv2021

Event-driven Two-stage Solution to Non-intrusive Load Monitoring

Existing methods of non-intrusive load monitoring (NILM) in literatures generally suffer from high computational complexity and/or low accuracy in identifying working household appliances. This paper proposes an event-driven Factorial Hidden Markov model (eFHMM) for multiple appliances with multiple states in a household, aiming for low computational complexity and high load disaggregation accuracy. The proposed eFHMM decreases the computational complexity to be linear to the event number, which ensures online load disaggregation. Furthermore, the eFHMM is solved in two stages, where the first stage identifies state-changing appliance using transient signatures and the second stage confirms the inferred states using steady-state signatures. The combination of transient and steady-state signatures, which are extracted from transient and steady periods segmented by detected events, enhances the uniqueness of each state transition and associated appliances, which ensures accurate load disaggregation. The event-driven two-stage NILM solution, termed as eFHMM-TS, is naturally fit into an edge-cloud framework, which makes possible the real-world application of NILM. The proposed eFHMM-TS method is validated on the LIFTED and synD datasets. Results demonstrate that the eFHMM-TS method outperforms other methods and can be applied in practice.

eess.SP↗

arXiv2021

Magnetic Excitations of the Hybrid Multiferroic (ND4)2FeCl5D2O

We report a comprehensive inelastic neutron scattering study of the hybrid molecule-based multiferroic compound (ND4)2FeCl5D2O in the zero-field incommensurate cycloidal phase and the high-field quasi-collinear phase. The spontaneous electric polarization changes its direction concurrently with the field-induced magnetic transition, from mostly aligned with the crystallographic a-axis to the c-axis. To account for such change of polarization direction, the underlying multiferroic mechanism was proposed to switch from the spin-current model induced via the inverse Dzyalloshinskii-Moriya interaction to the p-d hybridization model. We perform a detailed analysis of the inelastic neutron data of (ND4)2FeCl5D2O using linear spin-wave theory to quantify magnetic interaction strengths and investigate possible impact of different multiferroic mechanisms on the magnetic couplings. Our result reveals that the spin dynamics of both multiferroic phases can be well-described by a Heisenberg Hamiltonian with an easy-plane anisotropy. We do not find notable differences between the optimal model parameters of the two phases. The hierarchy of exchange couplings and the balance among frustrated interactions remain the same between two phases, suggesting that magnetic interactions in (ND4)2FeCl5D2O are much more robust than the electric polarization in response to delicate reorganizations of the electronic degrees of freedom in an applied magnetic field.

cond-mat.str-el↗

arXiv2021

Adaptive Event Detection for Representative Load Signature Extraction

Event detection is the first step in event-based non-intrusive load monitoring (NILM) and it can provide useful transient information to identify appliances. However, existing event detection methods with fixed parameters may fail in case of unpredictable and complicated residential load changes such as high fluctuation, long transition, and near simultaneity. This paper proposes a dynamic time-window approach to deal with these highly complex load variations. Specifically, a window with adaptive margins, multi-timescale window screening, and adaptive threshold (WAMMA) method is proposed to detect events in aggregated home appliance load data with high sampling rate (>1Hz). The proposed method accurately captures the transient process by adaptively tuning parameters including window width, margin width, and change threshold. Furthermore, representative transient and steady-state load signatures are extracted and, for the first time, quantified from transient and steady periods segmented by detected events. Case studies on a 20Hz dataset, the 50Hz LIFTED dataset, and the 60Hz BLUED dataset show that the proposed method can robustly outperform other state-of-art event detection methods. This paper also shows that the extracted load signatures can improve NILM accuracy and help develop other applications such as load reconstruction to generate realistic load data for NILM research.

eess.SP↗

arXiv2021

Understanding Magnetic Phase Coexistence in Ru$_2$Mn$_{1-x}$Fe$_x$Sn Heusler Alloys: A Neutron Scattering, Thermodynamic, and Phenomenological Analysis

The random substitutional solid solution between the antiferromagnetic (AFM) full-Heusler alloy Ru$_2$MnSn and the ferromagnetic (FM) full-Heusler alloy Ru$_2$FeSn provides a rare opportunity to study FM-AFM phase competition in a near-lattice-matched, cubic system, with full solubility. At intermediate $x$ in Ru$_2$Mn$_{1-x}$Fe$_x$Sn this system displays suppressed magnetic ordering temperatures, spatially coexisting FM and AFM order, and strong coercivity enhancement, despite rigorous chemical homogeneity. Here, we construct the most detailed temperature- and $x$-dependent understanding of the magnetic phase competition and coexistence in this system to date, combining wide-temperature-range neutron diffraction and small-angle neutron scattering with magnetometry and specific heat measurements on thoroughly characterized polycrystals. A complete magnetic phase diagram is generated, showing FM-AFM coexistence between $x \approx 0.30$ and $x \approx 0.70$. Important new insight is gained from the extracted length scales for magnetic phase coexistence (25-100 nm), the relative magnetic volume fractions and ordering temperatures, in addition to remarkable $x$-dependent trends in magnetic and electronic contributions to specific heat. An unusual feature in the magnetic phase diagram (an intermediate FM phase) is also shown to arise from an extrinsic effect related to a minor Ru-rich secondary phase. The established magnetic phase diagram is then discussed with the aid of phenomenological modeling, clarifying the nature of the mesoscale phase coexistence with respect to the understanding of disordered Heisenberg models.

cond-mat.str-el↗

arXiv2021

MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation

Object localization in 3D space is a challenging aspect in monocular 3D object detection. Recent advances in 6DoF pose estimation have shown that predicting dense 2D-3D correspondence maps between image and object 3D model and then estimating object pose via Perspective-n-Point (PnP) algorithm can achieve remarkable localization accuracy. Yet these methods rely on training with ground truth of object geometry, which is difficult to acquire in real outdoor scenes. To address this issue, we propose MonoRUn, a novel detection framework that learns dense correspondences and geometry in a self-supervised manner, with simple 3D bounding box annotations. To regress the pixel-related 3D object coordinates, we employ a regional reconstruction network with uncertainty awareness. For self-supervised training, the predicted 3D coordinates are projected back to the image plane. A Robust KL loss is proposed to minimize the uncertainty-weighted reprojection error. During testing phase, we exploit the network uncertainty by propagating it through all downstream modules. More specifically, the uncertainty-driven PnP algorithm is leveraged to estimate object pose and its covariance. Extensive experiments demonstrate that our proposed approach outperforms current state-of-the-art methods on KITTI benchmark.

cs.CV↗