arXiv subjects

Si Wu

Publications and source records attributed to Si Wu.

At least 73 records · Page 4Linked to original sources

arXiv2020

Crystalline and magnetic structures, magnetization, heat capacity and anisotropic magnetostriction effect in a yttrium-chromium oxide

We have studied a nearly stoichiometric insulating Y$_{0.97(2)}$Cr$_{0.98(2)}$O$_{3.00(2)}$ single crystal by performing measurements of magnetization, heat capacity, and neutron diffraction. Albeit that the YCrO$_3$ compound behaviors like a soft ferromagnet with a coersive force of $\sim$ 0.05 T, there exist strong antiferromagnetic (AFM) interactions between Cr$^{3+}$ spins due to a strongly negative paramagnetic Curie-Weiss temperature, i.e., -433.2(6) K. The coexistence of ferromagnetism and antiferromagnetism may indicate a canted AFM structure. The AFM phase transition occurs at $T_\textrm{N} =$ 141.5(1) K, which increases to $T_\textrm{N}$(5T) = 144.5(1) K at 5 T. Within the accuracy of the present neuron-diffraction studies, we determine a G-type AFM structure with a propagation vector \textbf{k} = (1 1 0) and Cr$^{3+}$ spin directions along the crystallographic \emph{c} axis of the orthorhombic structure with space group \emph{Pnma} below $T_\textrm{N}$. At 12 K, the refined moment size is 2.45(6) $μ_\textrm{B}$, $\sim$ 82\% of the theoretical saturation value 3 $μ_\textrm{B}$. The Cr$^{3+}$ spin interactions are probably two-dimensional Ising like within the reciprocal (1 1 0) scattering plane. Below $T_\textrm{N}$, the lattice configuration (\emph{a}, \emph{b}, \emph{c}, and \emph{V}) deviates largely downward from the Gr$\ddot{\textrm{u}}$neisen law, displaying an anisotropic magnetostriction effect and a magnetoelastic effect. Especially, the sample contraction upon cooling is enhanced below the AFM transition temperature. There is evidence to suggest that the actual crystalline symmetry of YCrO$_3$ compound is probably lower than the currently assumed one. Additionally, we compared the $t_{2\textrm{g}}$ YCrO$_3$ and the $e_\textrm{g}$ La$_{7/8}$Sr$_{1/8}$MnO$_3$ single crystals for a further understanding of the reason for the possible symmetry lowering.

cond-mat.str-el↗

arXiv2020

Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways

Object recognition is often viewed as a feedforward, bottom-up process in machine learning, but in real neural systems, object recognition is a complicated process which involves the interplay between two signal pathways. One is the parvocellular pathway (P-pathway), which is slow and extracts fine features of objects; the other is the magnocellular pathway (M-pathway), which is fast and extracts coarse features of objects. It has been suggested that the interplay between the two pathways endows the neural system with the capacity of processing visual information rapidly, adaptively, and robustly. However, the underlying computational mechanisms remain largely unknown. In this study, we build a computational model to elucidate the computational advantages associated with the interactions between two pathways. Our model consists of two convolution neural networks: one mimics the P-pathway, referred to as FineNet, which is deep, has small-size kernels, and receives detailed visual inputs; the other mimics the M-pathway, referred to as CoarseNet, which is shallow, has large-size kernels, and receives low-pass filtered or binarized visual inputs. The two pathways interact with each other via a Restricted Boltzmann Machine. We find that: 1) FineNet can teach CoarseNet through imitation and improve its performance considerably; 2) CoarseNet can improve the noise robustness of FineNet through association; 3) the output of CoarseNet can serve as a cognitive bias to improve the performance of FineNet. We hope that this study will provide insight into understanding visual information processing and inspire the development of new object recognition architectures.

cs.CV↗

arXiv2020

Colossal Negative Magnetoresistance Effect in A La$_{1.37}$Sr$_{1.63}$Mn$_2$O$_7$ Single Crystal Grown by Laser-Diode-Heated Floating-Zone Technique

We have grown La$_{1.37}$Sr$_{1.63}$Mn$_2$O$_7$ single crystals with a laser-diode-heated floating-zone furnace and studied the crystallinity, structure, and magnetoresistance (MR) effect by in-house X-ray Laue diffraction, X-ray powder diffraction, and resistance measurements. The La$_{1.37}$Sr$_{1.63}$Mn$_2$O$_7$ single crystal crystallizes into a tetragonal structure with space group \emph{I}4{/}\emph{mmm} at room temperature. At 0 T, the maximum resistance centers around $\sim$166.9 K. Below $\sim$35.8 K, it displays an insulating character with an increase in resistance upon cooling. An applied magnetic field of \emph{B}~=~7~T strongly suppresses the resistance indicative of a negative MR effect. The minimum MR value equals $-$91.23\% at 7 T and 128.7 K. The magnetic-field-dependent resistance shows distinct features at 1.67, 140, and 322 K, from which we calculated the corresponding MR values. At 14 T and 140 K, the colossal negative MR value is down to $-$94.04(5)\%. We schematically fit the MR values with different models for an ideal describing of the interesting features of the MR value versus \emph{B} curves.

cond-mat.str-el↗

arXiv2020

Super-Necking Crystal Growth and Structural and Magnetic Properties of SrTb$_2$O$_4$ Single Crystals

We report on single-crystal growths of the SrTb$_2$O$_4$ compound by a super-necking technique with a laser-floating-zone furnace and study the stoichiometry, growth mode, and structural and magnetic properties by scanning electronic microscopy, neutron Laue, X-ray powder diffraction, and the physical property measurement system. We optimized the growth parameters, mainly the growth speed, atmosphere, and the addition of a Tb$_4$O$_7$ raw material. Neutron Laue diffraction displays the characteristic feature of a single crystal. Our study reveals an atomic ratio of Sr:Tb $ = 0.97(2){:}2.00(1)$ and a possible layer by layer crystal growth mode. Our X-ray powder diffraction study determines the crystal structure, lattice constants and atomic positions. The paramagnetic (PM) Curie--Weiss (CW) temperature $θ_{\texttt{CW}} =$ 5.00(4) K, and the effective PM moment $M^{\texttt{eff}}_{\texttt{mea}} =$ 10.97(1) $μ_\texttt{B}$ per Tb$^{3+}$ ion. The data of magnetization versus temperature can be divided into three regimes, showing a coexistence of antiferromagnetic and ferromagnetic interactions. This probably leads to the magnetic frustration in the SrTb$_2$O$_4$ compound. The magnetization at 2 K and 14 T originates from both the Tb1 and Tb2 sites and is strongly frustrated with an expected saturation field at $\sim$41.5 T, displaying an intricate phase diagram with three ranges.

cond-mat.mtrl-sci↗

arXiv2020

High-temperature magnetism and crystallography of a YCrO$_3$ single crystal

Magnetization measurements and time-of-flight neutron powder-diffraction studies on the high-temperature (300--980 K) magnetism and crystal structure (321--1200 K) of a pulverized YCrO$_3$ single crystal have been performed. Temperature-dependent inverse magnetic susceptibility coincides with a piecewise linear function with five regimes, with which we fit a Curie-Weiss law and calculate the frustration factor $f$. The fit results indicate a formation of magnetic polarons between 300 and 540 K and a very strong magnetic frustration. By including one factor $η$ that represents the degree of spin interactions into the Brillouin function, we can fit well the applied-magnetic-field dependence of magnetization. No structural phase transition was observed from 321 to 1200 K. The average thermal expansions of lattice configurations (\emph{a}, \emph{b}, \emph{c}, and \emph{V}) obey well the Gr$\ddot{\textrm{u}}$neisen approximations with an anomaly appearing around 900 K, implying an isosymmetric structural phase transition, and display an anisotropic character along the crystallographic \emph{a}, \emph{b}, and \emph{c} axes with the incompressibility $K^a_0 > K^c_0 > K^b_0$. It is interesting to find that at 321 K, the local distortion size $Δ$(O2) $\approx$ 1.96$Δ$(O1) $\approx$ 4.32$Δ$(Y) $\approx$ 293.89$Δ$(Cr). Based on the refined Y-O and Cr-O bond lengths, we deduce the local distortion environments and modes of Y, Cr, O1, and O2 ions. Especially, the Y and O2 ions display obvious atomic displacement and charge subduction, which may shed light on the dielectric property of the YCrO$_3$ compound. Additionally, by comparing Kramers Mn$^{3+}$ with non-Kramers Cr$^{3+}$ ions, it is noted that being a Kramers or non-Kramers ion can strongly affect the local distortion size, whereas, it may not be able to change the detailed distortion mode.

cond-mat.str-el↗

arXiv2019

Unsupervised Few-shot Learning via Self-supervised Training

Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a more natural procedure for cognitive mammals and has produced promising results in many machine learning tasks. In the current study, we develop a method to learn an unsupervised few-shot learner via self-supervised training (UFLST), which can effectively generalize to novel but related classes. The proposed model consists of two alternate processes, progressive clustering and episodic training. The former generates pseudo-labeled training examples for constructing episodic tasks; and the later trains the few-shot learner using the generated episodic tasks which further optimizes the feature representations of data. The two processes facilitate with each other, and eventually produce a high quality few-shot learner. Using the benchmark dataset Omniglot and Mini-ImageNet, we show that our model outperforms other unsupervised few-shot learning methods. Using the benchmark dataset Market1501, we further demonstrate the feasibility of our model to a real-world application on person re-identification.

cs.CV↗

arXiv2019

Geometry of Graph Partitions via Optimal Transport

We define a distance metric between partitions of a graph using machinery from optimal transport. Our metric is built from a linear assignment problem that matches partition components, with assignment cost proportional to transport distance over graph edges. We show that our distance can be computed using a single linear program without precomputing pairwise assignment costs and derive several theoretical properties of the metric. Finally, we provide experiments demonstrating these properties empirically, specifically focusing on its value for new problems in ensemble-based analysis of political districting plans.

math.OC↗

arXiv2019

Spatiotemporal Information Processing with a Reservoir Decision-making Network

Spatiotemporal information processing is fundamental to brain functions. The present study investigates a canonic neural network model for spatiotemporal pattern recognition. Specifically, the model consists of two modules, a reservoir subnetwork and a decision-making subnetwork. The former projects complex spatiotemporal patterns into spatially separated neural representations, and the latter reads out these neural representations via integrating information over time; the two modules are combined together via supervised-learning using known examples. We elucidate the working mechanism of the model and demonstrate its feasibility for discriminating complex spatiotemporal patterns. Our model reproduces the phenomenon of recognizing looming patterns in the neural system, and can learn to discriminate gait with very few training examples. We hope this study gives us insight into understanding how spatiotemporal information is processed in the brain and helps us to develop brain-inspired application algorithms.

q-bio.NC↗

arXiv2015

Fluctuation-response Relation Unifies Dynamical Behaviors in Neural Fields

Anticipation is a strategy used by neural fields to compensate for transmission and processing delays during the tracking of dynamical information, and can be achieved by slow, localized, inhibitory feedback mechanisms such as short-term synaptic depression, spike-frequency adaptation, or inhibitory feedback from other layers. Based on the translational symmetry of the mobile network states, we derive generic fluctuation-response relations, providing unified predictions that link their tracking behaviors in the presence of external stimuli to the intrinsic dynamics of the neural fields in their absence.

cond-mat.dis-nn↗

arXiv2015

A Rich Spectrum of Neural Field Dynamics in the Presence of Short-Term Synaptic Depression

In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred stimuli of the neurons. We investigate how short-term synaptic depression (STD) can reshape the intrinsic dynamics of the CANN model and its responses to a single static input. In particular, CANNs with STD can support various complex firing patterns and chaotic behaviors. These chaotic behaviors have the potential to encode various stimuli in the neuronal system.

q-bio.NC↗

arXiv2015

Dynamics of Neural Networks with Continuous Attractors

We investigate the dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of stationary states. We systematically explore how their neutral stability facilitates the tracking performance of a CANN, which is believed to have wide applications in brain functions. We develop a perturbative approach that utilizes the dominant movement of the network stationary states in the state space. We quantify the distortions of the bump shape during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable, and the reaction time to catch up an abrupt change in stimulus.

cond-mat.dis-nn↗

arXiv2014

Nematic and spin-charge orders driven by hole-doping a charge-transfer insulator

Recent experimental discoveries have brought a diverse set of broken symmetry states to the center stage of research on cuprate superconductors. Here, we focus on a thematic understanding of the diverse phenomenology by exploring a strong-coupling mechanism of symmetry breaking driven by frustration of antiferromagnetic order. We achieve this through a variational study of a three-band model of the CuO$_2$ plane with Kondo-type exchange couplings between doped oxygen holes and classical copper spins. Two main findings from this strong-coupling multi-band perspective are 1) that the symmetry hierarchy of spin stripe, charge stripe, intra-unit-cell nematic order and isotropic phases are all accessible microscopically within the model, 2) many symmetry-breaking patterns compete with energy differences within a few meV per Cu atom to produce a rich phase diagram. These results indicate that the diverse phenomenology of broken-symmetry states in hole-doped antiferromagnetic charge-transfer insulators may indeed arise from hole-doped frustration of antiferromagnetism.

cond-mat.str-el↗

arXiv2014

Solving the inverse problem of noise-driven dynamic networks

Nowadays massive amount of data are available for analysis in natural and social systems. Inferring system structures from the data, i.e., the inverse problem, has become one of the central issues in many disciplines and interdisciplinary studies. In this Letter, we study the inverse problem of stochastic dynamic complex networks. We derive analytically a simple and universal inference formula called double correlation matrix (DCM) method. Numerical simulations confirm that the DCM method can accurately depict both network structures and noise correlations by using available kinetic data only. This inference performance was never regarded possible by theoretical derivation, numerical computation and experimental design.

physics.bio-ph↗

arXiv2013

Axion response in Weyl semimetals

Weyl semimetal is a new phase of matter that provides the first solid state realization of chiral Weyl fermions. Most of its unique physics is a consequence of chiral anomaly, namely nonconservation of the number of particles of a given chirality. Mathematically, this is expressed in the appearance of the so called $θ$-term in the action of the electromagnetic field, when the Weyl fermions are integrated out. Recently, however, it has been suggested that the analogy between the chiral fermions of quantum field theory with unbounded linear dispersion, and their solid state realization with a dispersion naturally bounded by the bandwidth and crystal momentum defined only within the first Brillouin zone, holds only in a restricted sense, with parts of the $θ$-term absent. Here we demonstrate that this is not the case. We explicitly derive the $θ$-term for a microscopic model of a Weyl semimetal by integrating out fermions coupled to electromagnetic field, and show that the result has exactly the same form as in the case of relativistic chiral fermions.

cond-mat.mes-hall↗

arXiv2013

Resolution enhancement in neural networks with dynamical synapses

Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.

q-bio.NC↗

arXiv2013

Non-Abelian toplogical superconductors from topological semimetals and related systems under superconducting proximity effect

Non-Abelian toplogical superconductors are characterized by the existence of {zero-energy} Majorana fermions bound in the quantized vortices. This is a consequence of the nontrivial bulk topology characterized by an {\em odd} Chern number. It is found that in topological semimetals with a single two-bands crossing point all the gapped superconductors are non-Abelian ones. Such a property is generalized to related but more generic systems which will be useful in the search of non-Abelian superconductors and Majorana fermions.

cond-mat.str-el↗

arXiv2012

Graphene: Kinks, Superlattices, Landau levels, and Magnetotransport

We review recent work on superlattices in monolayer and bilayer graphene. We highlight the role of the quasiparticle chirality in generating new Dirac fermion modes with tunable anisotropic velocities in one dimensional (1D) superlattices in both monolayer and bilayer graphene. We discuss the structure of the Landau levels and magnetotransport in such superlattices over a wide range of perpendicular (orbital) magnetic fields. In monolayer graphene, we show that an orbital magnetic field can reverse the anisotropy of the transport imposed by the superlattice potential, suggesting possible switching-type device applications. We also consider topological modes localized at a kink in an electric field applied perpendicular to bilayer graphene, and show how interactions convert these modes into a two-band Luttinger liquid with tunable Luttinger parameters. The band structures of electric field superlattices in bilayer graphene (with or without a magnetic field) are shown to arise naturally from a coupled array of such topological modes. We briefly review some bandstructure results for 2D superlattices. We conclude with a discussion of recent tunneling and transport experiments and point out open issues.

cond-mat.mes-hall↗

arXiv2012

Graphene under spatially varying external potentials: Landau levels, magnetotransport, and topological modes

Superlattices (SLs) in monolayer and bilayer graphene, formed by spatially periodic potential variations, lead to a modified bandstructure with extra finite-energy and zero-energy Dirac fermions with tunable anisotropic velocities. We theoretically show that transport in a weak perpendicular (orbital) magnetic field allows one to not only probe the number of emergent Dirac points but also yields further information about their dispersion. or monolayer graphene, we find that a moderate magnetic field can lead to a strong reversal of the transport anisotropy imposed by the SL potential, an effect which arises due to the SL induced dispersion of the zero energy Landau levels. This effect may find useful applications in switching or other devices. For bilayer graphene, we discuss the structure of Landau level wave functions and local density of states in the presence of a uniform bias, as well as in the presence of a kink in the bias which leads to topologically bound `edge states'. We consider implications of these results for scanning tunneling spectroscopy measurements, valley filtering, and impurity induced breakdown of the quantum Hall effect in bilayer graphene.

cond-mat.mes-hall↗