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Hui Wu

Hui Wu appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.mtrl-sciComputer Visioncond-mat.str-elcond-mat.supr-conMachine LearningInformation Theorymath.ITNetworking and Internet Architectureastro-ph.HEBiological PhysicsComputational Engineering, Finance, and Scienceeess.SPmath.FAmath.RANeural and Evolutionary Computingnlin.CDphysics.chem-phphysics.optics

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Published work

22 published item(s)

preprint2026arXiv

Delay-induced chimera transitions via mode selection in a multiplex FitzHugh Nagumo network

We investigate delay-induced collective dynamics in a two-layer multiplex FitzHugh Nagumo network with nonlocal intra layer coupling and delayed inter layer interactions. While delay effects are often treated as secondary, we show that deterministic inter-layer delay alone can act as a control mechanism for spatial coherence. Through systematic numerical simulations, we observe a clear transition as the delay parameter increases: fragmented incoherence evolves into chimera-like partial coherence, and eventually into a coherent traveling-wave state. This transition is consistently captured by spatial snapshots, space-time plots, and mean phase velocity profiles. To explain this behavior, we analyze the stability of spatial Fourier modes and show that the delay term introduces a mode-dependent exponential factor in the characteristic equation. This term induces non-monotonic changes in modal stability, effectively acting as a mode-selection mechanism: intermediate delays selectively destabilize a subset of modes, producing chimera-like coexistence, while larger delays suppress incoherent modes and restore global coherence. Our results demonstrate that inter-layer delay provides a simple and robust mechanism for controlling pattern formation in multiplex excitable networks, offering new insight into delay driven synchronization phenomena.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief

Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases. In this context, this tutorial brief presents a concise overview of concepts and design methodologies of neural recording, highlighting neural signal characteristics, system-level specifications and architectures, circuit-level implementation, and noise reduction techniques. Future trends and challenges of neural recording are finally discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SimVQA: Exploring Simulated Environments for Visual Question Answering

Existing work on VQA explores data augmentation to achieve better generalization by perturbing the images in the dataset or modifying the existing questions and answers. While these methods exhibit good performance, the diversity of the questions and answers are constrained by the available image set. In this work we explore using synthetic computer-generated data to fully control the visual and language space, allowing us to provide more diverse scenarios. We quantify the effect of synthetic data in real-world VQA benchmarks and to which extent it produces results that generalize to real data. By exploiting 3D and physics simulation platforms, we provide a pipeline to generate synthetic data to expand and replace type-specific questions and answers without risking the exposure of sensitive or personal data that might be present in real images. We offer a comprehensive analysis while expanding existing hyper-realistic datasets to be used for VQA. We also propose Feature Swapping (F-SWAP) -- where we randomly switch object-level features during training to make a VQA model more domain invariant. We show that F-SWAP is effective for enhancing a currently existing VQA dataset of real images without compromising on the accuracy to answer existing questions in the dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The m-core-EP inverse in Minkowski space

In this paper, we introduce the m-core-EP inverse in Minkowski space, consider its properties, and get several sufficient and necessary conditions for the existence of the m-core-EP inverse. We give the m-core-EP decomposition in Minkowski space, and note that not every square matrix has the decomposition. Furthermore, by applying the m-core-EP inverse and the m-core-EP decomposition, we introduce the m-core-EP order and give some characterizations of it.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most of the existing NAS methods makes it difficult to directly search the architectures on large-scale tasks. The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset. Despite a number of recent results that show the promise of transfer from proxy datasets, a comprehensive evaluation of different NAS methods studying the impact of different source datasets has not yet been addressed. In this work, we propose to analyze the architecture transferability of different NAS methods by performing a series of experiments on large scale benchmarks such as ImageNet1K and ImageNet22K. We find that: (i) The size and domain of the proxy set does not seem to influence architecture performance on the target dataset. On average, transfer performance of architectures searched using completely different small datasets (e.g., CIFAR10) perform similarly to the architectures searched directly on proxy target datasets. However, design of proxy sets has considerable impact on rankings of different NAS methods. (ii) While different NAS methods show similar performance on a source dataset (e.g., CIFAR10), they significantly differ on the transfer performance to a large dataset (e.g., ImageNet1K). (iii) Even on large datasets, random sampling baseline is very competitive, but the choice of the appropriate combination of proxy set and search strategy can provide significant improvement over it. We believe that our extensive empirical analysis will prove useful for future design of NAS algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Physical properties of a quasi-two-dimensional square lattice antiferromagnet Ba$_2$FeSi$_2$O$_7$

We report the magnetization ($χ$, $M$), specific heat ($C_{\text{P}}$), and neutron powder diffraction results on a quasi-two-dimensional $S$ = 2 square lattice antiferromagnet Ba$_2$FeSi$_2$O$_7$ consisting of FeO$_4$ tetragons with a large compressive distortion (27%). Despite of the quasi-two-dimensional lattice structure, both $χ$ and $C_{\text{P}}$ present three dimensional magnetic long-range order below the Néel temperature $T_{\text{N}}$ = 5.2 K. Neutron diffraction data shows a collinear $Q_{m}$ = (1,0,0.5) antiferromagnetic (AFM) structure with the in-plane ordered magnetic moment suppressed by 26% below $T_{\text{N}}$. Both the AFM structure and the suppressed moments are well explained by the Monte Carlo simulation with a large single-ion ab-plane anisotropy $D$ = 1.4 meV and a rather small in-plane Heisenberg exchange $J_{\text{intra}}$ = 0.15 meV. The characteristic two dimensional spin fluctuations can be recognized in the magnetic entropy release and diffuse scattering above $T_{\text{N}}$. This new quasi-2D magnetic system also displays unusual non-monotonic dependence of the $T_{\text{N}}$ as a function of magnetic field $H$.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning

During recent decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems for its low-cost and intelligence. This paper proposes two smart train operation algorithms by integrating the expert knowledge with reinforcement learning algorithms. Compared with previous works, the proposed algorithms can realize the control of continuous action for the subway system and optimize multiple critical objectives without using an offline speed profile. Firstly, through learning historical data of experienced subway drivers, we extract the expert knowledge rules and build inference methods to guarantee the riding comfort, the punctuality, and the safety of the subway system. Then we develop two algorithms for optimizing the energy efficiency of train operation. One is the smart train operation (STO) algorithm based on deep deterministic policy gradient named (STOD) and the other is the smart train operation algorithm based on normalized advantage function (STON). Finally, we verify the performance of proposed algorithms via some numerical simulations with the real field data from the Yizhuang Line of the Beijing Subway and illustrate that the developed smart train operation algorithm are better than expert manual driving and existing ATO algorithms in terms of energy efficiency. Moreover, STOD and STON can adapt to different trip times and different resistance conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Confirmed width-Eiso and width-Liso relations in GRB: comparison with the Amati and Yonetoku relations

In this paper, we select a sample including 141 BEST time-integrated F spectra and 145 BEST peak flux P spectra observed by the Konus-Wind with known redshift to recheck the connection between the spectral width and $E_{iso}$ as well as $L_{iso}$. We define six types of absolute spectral widths. It is found that all of the rest-frame absolute spectral widths are strongly positive correlated with $E_{iso}$ as well as $L_{iso}$ for the long burst for both the F and P spectra. All of the short bursts are the outliers for width-$E_{iso}$ relation and most of the short bursts are consistent with the long bursts for the width-$L_{iso}$ relation for both F and P spectra. Moreover, all of the location energy, $E_{2}$ and $E_{1}$, corresponding to various spectral widths are also positive correlated with $E_{iso}$ as well as $L_{iso}$. We compare all of the relations with the Amati and Yonetoku relations and find the width-$E_{iso}$ and width-$L_{iso}$ relations when the widths are at about 90\% maximum of the $EF_{E}$ spectra almost overlap with Amati relation and Yonetoku relation, respectively. The correlations of $E_{2}-E_{iso}$, $E_{1}-E_{iso}$ and $E_{2}-L_{iso}$, $E_{1}-L_{iso}$ when the location energies are at 99\% maximum of the $EF_{E}$ spectra are very close to the Amati and Yonetoku relations, respectively. Therefore, we confirm the existence of tight width-$E_{iso}$ and width-$L_{iso}$ relations for long bursts. We further show that the spectral shape is indeed related to $E_{iso}$ and $L_{iso}$. The Amati and Yonetoku relations are not necessarily the best relationships to relate the energy to the $E_{iso}$ and $L_{iso}$. They may be the special cases of the width-$E_{iso}$ and width-$L_{iso}$ relations or the energy-$E_{iso}$ and energy-$L_{iso}$ relations.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

A Mott insulator continuously connected to iron pnictide superconductors

Iron-based superconductivity develops near an antiferromagnetic order and out of a bad metal normal state, which has been interpreted as originating from a proximate Mott transition. Whether an actual Mott insulator can be realized in the phase diagram of the iron pnictides remains an open question. Here we use transport, transmission electron microscopy, X-ray absorption spectroscopy, and neutron scattering to demonstrate that NaFe$_{1-x}$Cu$_x$As near $x\approx 0.5$ exhibits real space Fe and Cu ordering, and are antiferromagnetic insulators with the insulating behavior persisting above the Néel temperature, indicative of a Mott insulator. Upon decreasing $x$ from $0.5$, the antiferromagnetic ordered moment continuously decreases, yielding to superconductivity around $x=0.05$. Our discovery of a Mott insulating state in NaFe$_{1-x}$Cu$_x$As thus makes it the only known Fe-based material in which superconductivity can be smoothly connected to the Mott insulating state, highlighting the important role of electron correlations in the high-$T_{\rm c}$ superconductivity.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Antiferromagnetic and Orbital Ordering on a Diamond Lattice Near Quantum Criticality

We present neutron scattering measurements on powder samples of the spinel FeSc2S4 that reveal a previously unobserved magnetic ordering transition occurring at 11.8(2)~K. Magnetic ordering occurs subsequent to a subtle cubic-to-tetragonal structural transition which distorts Fe coordinating sulfur tetrahedra lifting the orbital degeneracy. The application of 1~GPa hydrostatic pressure appears to destabilize this Néel state, reducing the transition temperature to 8.6(8)~K and redistributing magnetic spectral weight to higher energies. The relative magnitudes of ordered $\langle m \rangle^2\!=\!3.1(2)$ and fluctuating moments $\langle δm \rangle^2\!=\!13(1)$ show that the magnetically ordered ground state of FeSc2S4 is drastically renormalized and in proximity to criticality.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

S3Pool: Pooling with Stochastic Spatial Sampling

Feature pooling layers (e.g., max pooling) in convolutional neural networks (CNNs) serve the dual purpose of providing increasingly abstract representations as well as yielding computational savings in subsequent convolutional layers. We view the pooling operation in CNNs as a two-step procedure: first, a pooling window (e.g., $2\times 2$) slides over the feature map with stride one which leaves the spatial resolution intact, and second, downsampling is performed by selecting one pixel from each non-overlapping pooling window in an often uniform and deterministic (e.g., top-left) manner. Our starting point in this work is the observation that this regularly spaced downsampling arising from non-overlapping windows, although intuitive from a signal processing perspective (which has the goal of signal reconstruction), is not necessarily optimal for \emph{learning} (where the goal is to generalize). We study this aspect and propose a novel pooling strategy with stochastic spatial sampling (S3Pool), where the regular downsampling is replaced by a more general stochastic version. We observe that this general stochasticity acts as a strong regularizer, and can also be seen as doing implicit data augmentation by introducing distortions in the feature maps. We further introduce a mechanism to control the amount of distortion to suit different datasets and architectures. To demonstrate the effectiveness of the proposed approach, we perform extensive experiments on several popular image classification benchmarks, observing excellent improvements over baseline models. Experimental code is available at https://github.com/Shuangfei/s3pool.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Two new parent compounds for FeSe-based superconducting phases

It is well established that the occurrence of superconductivity in iron pnictides is closely related to the tetragonal to orthorhombic structural and antiferromagnetic (AFM) phase transitions. It, however, has not been clear whether the same scenario is appropriate for iron chalcogenide counterparts due to the absence of parent compounds for the latter family of superconductors. Here, we report the synthesis and structure determination of two novel phases in ethylenediamine intercalated FeSe, one is tetragonal and the other orthorhombic in room temperature, which can be stabilized with neutral spacer layers. Both phases can be regarded as the parent compounds for superconductivity as they are non-superconducting (non-SC) in pristine form and superconducting (SC) with Tc up to 38K and 46K, respectively after Na doping, and the switch between SC and no-SC is reversible. Moreover, the two non-SC parent compounds show no evidence of long-range magnetic ordering down to 2K, only with dynamic spin fluctuations at low temperatures, suggesting that no competition between SC and AFM ordering. Our results reveal that undoped iron selenides are quantum paramagnetic in ground state, implying that they are distinct from their pnictide counterparts in pairing mechanism.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Virtual-MIMO-Boosted Information Propagation on Highways

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel virtual-MIMO-enabled information dissemination scheme, in which the vehicles opportunistically form virtual antenna arrays to boost the transmission range and therefore accelerate information propagation along the highway. We model the information propagation process as a renewal reward process and investigate in detail the \emph{Information Propagation Speed} (IPS) of the proposed scheme. The corresponding closed-form IPS is derived, which shows that the IPS increases cubically with the vehicle density but will ultimately converge to a constant upper bound. Moreover, increased mobility also facilitates the information spreading by offering more communication opportunities. However, the limited network density essentially determines the bottleneck in information spreading. Extensive simulations are carried out to verify our analysis. We also show that the proposed scheme exhibits a significant IPS gain over its conventional counterpart.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Faster Information Propagation on Highways: a Virtual MIMO Approach

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safe and reliable transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel cluster-based cooperative information forwarding scheme, in which the vehicles opportunistically form virtual antenna arrays to boost one-hop transmission range and therefore accelerate information propagation along the highway. Both closed-form results of the transmission range gain and the improved Information Propagation Speed (IPS) are derived and verified by simulations. It is observed that the proposed scheme demonstrates the most significant IPS gain in moderate traffic scenarios, whereas too dense or too sparse vehicle density results in less gain. Moreover, it is also shown that increased mobility offers more contact opportunities and thus facilitates information propagation.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Spatial control of surface plasmon polariton excitation at planar metal surface

We illustrate that the surface plasmon polariton (SPP) excitation through the prism coupling method is fundamentally limited by destructive interference of spatial light components. We propose that the destructive interference can be canceled out by tailoring the relative phase for the different spatial components. As a numerical demonstration, we show that through the phase modulation the excited SPP field is concentrated to a hot energy spot, and the SPP field intensity is dramatically enhanced about three folds in comparison with a conventional Gaussian beam illumination.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

k-DAG Based Lifetime Aware Data Collection in Wireless Sensor Networks

Wireless Sensor Networks need to be organized for efficient data collection and lifetime maximization. In this paper, we propose a novel routing structure, namely k-DAG, to balance the load of the base station's neighbours while providing the worst-case latency guarantee for data collection, and a distributed algorithm for construction a k-DAG based on a SPD (Shortest Path DAG). In a k-DAG, the lengths of the longest path and the shortest path of each sensor node to the base station differ by at most k. By adding sibling edges to a SPD, our distributed algorithm allows critical nodes to have more routing choices. The simulation results show that our approach significantly outperforms the SPD-based data collection approach in both network lifetime and load balance.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Structure and Properties of α-NaFeO2-type Ternary Sodium Iridates

The synthesis, structure, and elementary magnetic and electronic properties are reported for layered compounds of the type Na3-xMIr2O6 and Na3-xM2IrO6, where M is a transition metal from the 3d series (M=Zn, Cu, Ni, Co, Fe and Mn). The rhombohedral structures, in space group R-3m, were determined by refinement of neutron and synchrotron powder diffraction data. No clear evidence for long range 2:1 or 1:2 honeycomb-like M/Ir ordering was found in the neutron powder diffraction patterns except in the case of M = Zn, thus in general the compounds are best designated as sodium deficient α-NaFeO2-type phases with formulas Na1-xM1/3Ir2/3O2 or Na1-xM2/3Ir1/3O2. Synchrotron powder diffraction patterns indicate that several of the compounds likely have honeycomb in-plane metal-iridium ordering with disordered stacking of the layers. All the compounds are sodium deficient under our synthetic conditions and are black and insulating. Weiss constants derived from magnetic susceptibility measurements indicate that Na0.62Mn0.61Ir0.39O2, Na0.80Fe2/3Ir1/3O2, Na0.92Ni1/3Ir2/3O2, Na0.86Cu1/3Ir2/3O2, and Na0.89Zn1/3Ir2/3O2 display dominant antiferromagnetic interactions. For Na0.90Co1/3Ir2/3O2 the dominant magnetic interactions at low temperature are ferromagnetic while at high temperatures they are antiferromagnetic; there is also a change in the effective moment. Low temperature specific heat measurements (to 2 K) on Na0.92Ni1/3Ir2/3O2 indicate the presence of a broad magnetic ordering transition. X-ray absorption spectroscopy shows that iridium is at or close to the 4+ oxidation state in all compounds. 23Na nuclear magnetic resonance measurements comparing Na2IrO3 to Na0.92Ni1/3Ir2/3O2 and Na0.89Zn1/3Ir2/3O2 provide strong indications that the electron spins are short-range ordered in the latter two materials. All of the compounds are spin glasses.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Sustainable high critical temperature in a hydrocarbon superconductor

Organic superconductors are unique materials with a crystal structure made primarily of a complex carbon based network, an element associated directly with life, which were postulated to have a high critical temperature, $T_{C}$, even above room temperature, from a theoretical viewpoint. Pressure plays an essential role in the study of superconductivity in such organic materials, including creation of the first organic superconductor as well as the achievement of the highest $T_{C}$ of 14.2 K for charge transfer salts and 38 K for metal-doped fullerides. However, superconductivity in these organic systems is only sustainable within a very narrow pressure range (a few GPa) and is readily destroyed upon further compression. Here we report high-pressure magnetic susceptibility and structure measurements on a newly discovered superconductor, LaPhenanthrene. It is found that the application of pressure not only significantly increases $T_{C}$ from its ambient-pressure value of 4.8 K to 12.3 K at 18.4 GPa but also stabilizes the high $T_{C}$ over the entire pressure range (61 GPa) of the study. The evolution of $T_{C}$ with pressure is closely correlated with the angle $β$ of the monoclinic unit cell. Both $T_{C}$ and $β$ change sharply with increasing pressure initially but remain constant above 40 GPa, indicating that molecule orientation is essential to superconductivity. These behaviors can be understood in terms of pressure tuning of electron correlations, illustrating the unconventional nature of superconductivity in these hydrocarbon superconductors.

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preprint2012arXiv

The magnetic structure of bixbyite a-Mn2O3: a combined density functional theory DFT+U and neutron diffraction study

First principles density functional theory DFT+U calculations and experimental neutron diffraction structure analyses were used to determine the low-temperature crystallographic and magnetic structure of bixbyite Mn2O3. The energies of various magnetic arrangements, calculated from first principles, were fit to a cluster-expansion model using a Bayesian method that overcomes a problem of underfitting caused by the limited number of input magnetic configurations. The model was used to predict the lowest-energy magnetic states. Experimental determination of magnetic structure benefited from optimized sample synthesis, which produced crystallite sizes large enough to yield a clear splitting of peaks in the neutron powder diffraction patterns, thereby enabling magnetic-structure refinements under the correct orthorhombic symmetry. The refinements employed group theory to constrain magnetic models. Computational and experimental analyses independently converged to similar ground states, with identical antiferromagnetic ordering along a principal magnetic axis and secondary ordering along a single orthogonal axis, differing only by a phase factor in the modulation patterns. The lowest-energy magnetic states are compromise solutions to frustrated antiferromagnetic interactions between certain corner-sharing MnO6 octahedra.

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preprint2011arXiv

Reconfigurable nanoelectronics using graphene based spintronic logic gates

This paper presents a novel design concept for spintronic nanoelectronics that emphasizes a seamless integration of spin-based memory and logic circuits. The building blocks are magneto-logic gates based on a hybrid graphene/ferromagnet material system. We use network search engines as a technology demonstration vehicle and present a spin-based circuit design with smaller area, faster speed, and lower energy consumption than the state-of-the-art CMOS counterparts. This design can also be applied in applications such as data compression, coding and image recognition. In the proposed scheme, over 100 spin-based logic operations are carried out before any need for a spin-charge conversion. Consequently, supporting CMOS electronics requires little power consumption. The spintronic-CMOS integrated system can be implemented on a single 3-D chip. These nonvolatile logic circuits hold potential for a paradigm shift in computing applications.

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preprint2006arXiv

An Empirical Approach to the Bond Additivity Model in Quantitative Interpretation of Sum Frequency Generation Vibrational Spectra

A complete empirical approach from known Raman and IR spectra is used to make corrections to the bond additivity model for quantitative interpretation of Sum Frequency generation Vibrational Spectra (SFG-VS) from molecular interfaces. This empirical correction successfully addresses the failures of the simple bond additivity model. This empirical approach not only provides new understandings of the effectiveness and limitations of the bond additivity model, but also provides a practical roadmap for its application in SFG-VS studies of molecular interfaces.

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preprint2006arXiv

Structure and dielectric response in the high $T_c$ ferroelectric Bi(Zn,Ti)O$_3$-PbTiO$_3$ solid solutions

Theoretical {\em ab initio} and experimental methods were used to investigate the $x$Bi(Zn,Ti)O$_3$-(1-$x$)PbTiO$_3$ (BZT-PT) solid solution. We find that hybridization between Zn 4$p$ and O 2$p$ orbitals allows the formation of short, covalent Zn-O bonds, enabling favorable coupling between A-site and B-site displacements. This leads to large polarization, strong tetragonality and an elevated ferroelectric to paraelectric phase transition temperature. nhomogeneities in local structure near the 90$^\circ$ domain boundaries can be deduced from the asymetric peak broadening in the neutron and x-ray diffraction spectra. These extrinsic effects make the ferroelectric to paraelectric phase transition diffuse in BZT-PT solid solutions.

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