arXiv subjects

Tianye Wang

Publications and source records attributed to Tianye Wang.

13 recordsLinked to original sources

arXiv2026

Absence of magnetic order in epitaxial RuO2 revealed by X-ray linear dichroism

Recently, the topic of altermagnetism has attracted tremendous attention and RuO2 have been demonstrated to be one of the most promising altermagnetic candidates. However, disputes still remain on the existence of magnetic order in RuO2. Here in this work, we employ X-ray linear dichroism (XLD), a widely utilized technique for characterizing antiferromagnets, in conjunction with photoemission electron microscopy and multiple scattering calculation to provide clear evidence of the absence of magnetic order in epitaxial RuO2 films. The observed XLD signal is nearly invariant with temperature and independent on cooling field direction, in stark contrast to the substantial magnetic order-related XLD signal predicted by multiple scattering calculation. This finding strongly suggests a nonmagnetic origin for RuO2. Furthermore, we observed significantly distinct XLD signals at the Ru M3 and O K edges in RuO2 films grown on TiO2 substrate with different surface orientations, which can be attributed to the low-symmetry crystal field. These results unequivocally demonstrate the absence of magnetic order in RuO2 and establishes XLD measurement as a robust technique for probing the low-symmetry magnetic materials.

cond-mat.mtrl-sci↗

arXiv2025

Integrative characterization of the topography of V4 neural codes using deep learning approaches

Area V4 is a mid-level stage of the macaque ventral visual stream, known to encode intermediate visual features such as color, curvature, corners, texture, three-dimensional (3D) solids, and local form. Classical neurophysiological studies have typically examined these dimensions in isolation, contrasting V4 selectivity for shape versus texture, 3D solid surfaces versus two-dimensional (2D) flat patterns, or object form versus texture. Yet how these tunings relate to one another within individual neurons, and how they are jointly organized across the cortical surface, remain unknown. For instance, does a neuron selective for 2D contour-defined shape prefer 3D solid surfaces or 2D flat surfaces? How are preferences for such heterogeneous attributes arranged in a common topographic map? To address these questions, we leverage V4 "digital twins" -- deep neural network models fitted to large-scale, wide-field calcium imaging data comprising tens of thousands of natural images. These digital twins allow us to systematically probe not only the stimulus dimensions explored in earlier studies, but also new, multidimensional stimulus sets that reveal additional aspects of the V4 code. In this study, we find that neural pixels preferring 2D contour-defined shapes also tend to prefer 3D surface shape defined by shading or texture gradients and by object form. In contrast, pixels preferring 2D texture tend to prefer flat surfaces defined by uniform texture or reflectance. We propose that this division of labor suggests that V4 may decompose the encoding of geometrical shape and surface appearance of visual stimuli into distinct populations of neurons, organized as interleaved clusters in the V4 topographic map.

q-bio.NC↗

arXiv2025

Self-Attention-Based Contextual Modulation Improves Neural System Identification

Convolutional neural networks (CNNs) have been shown to be state-of-the-art models for visual cortical neurons. Cortical neurons in the primary visual cortex are sensitive to contextual information mediated by extensive horizontal and feedback connections. Standard CNNs integrate global contextual information to model contextual modulation via two mechanisms: successive convolutions and a fully connected readout layer. In this paper, we find that self-attention (SA), an implementation of non-local network mechanisms, can improve neural response predictions over parameter-matched CNNs in two key metrics: tuning curve correlation and peak tuning. We introduce peak tuning as a metric to evaluate a model's ability to capture a neuron's top feature preference. We factorize networks to assess each context mechanism, revealing that information in the local receptive field is most important for modeling overall tuning, but surround information is critically necessary for characterizing the tuning peak. We find that self-attention can replace posterior spatial-integration convolutions when learned incrementally, and is further enhanced in the presence of a fully connected readout layer, suggesting that the two context mechanisms are complementary. Finally, we find that decomposing receptive field learning and contextual modulation learning in an incremental manner may be an effective and robust mechanism for learning surround-center interactions.

cs.CV↗

arXiv2025

Spin frustration and unconventional spin twisting state in van der Waals ferromagnet/antiferromagnet heterostructures

Atomically flat surfaces of van der Waals (vdW) materials pave an avenue for addressing a long-standing fundamental issue of how a perfectly compensated antiferromagnet (AFM) surface frustrates a ferromagnetic (FM) overlayer in FM/AFM heterostructures. By revealing the AFM and FM spin structures separately in vdW Fe5GeTe2/NiPS3 heterostructures, we find that C-type in-plane AFM NiPS3 develops three equivalent AFM domains which are robust against external magnetic field and magnetic coupling with Fe5GeTe2. Consequently, spin frustration at the Fe5GeTe2/NiPS3 interface was shown to develop a perpendicular Fe5GeTe2 magnetization in the interfacial region that switches separately from the bulk of the Fe5GeTe2 magnetizations. In particular, we discover an unconventional spin twisting state that the Fe5GeTe2 spins twist from perpendicular direction near the interface to in-plane direction away from the interface in Fe5GeTe2/NiPS3. Our finding of the twisting spin texture is a unique property of spin frustration in van der Waals magnetic heterostructures.

cond-mat.mtrl-sci↗

arXiv2024

120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training

Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 * 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers.

physics.optics↗

arXiv2024

NeuroBind: Towards Unified Multimodal Representations for Neural Signals

Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, measured through techniques like electrophysiology and neuroimaging, reflects various aspects of information processing. Recent advances in deep neural networks offer new approaches to analyzing these signals using pre-trained models. However, challenges arise due to discrepancies between different neural signal modalities and the limited scale of high-quality neural data. To address these challenges, we present NeuroBind, a general representation that unifies multiple brain signal types, including EEG, fMRI, calcium imaging, and spiking data. To achieve this, we align neural signals in these image-paired neural datasets to pre-trained vision-language embeddings. Neurobind is the first model that studies different neural modalities interconnectedly and is able to leverage high-resource modality models for various neuroscience tasks. We also showed that by combining information from different neural signal modalities, NeuroBind enhances downstream performance, demonstrating the effectiveness of the complementary strengths of different neural modalities. As a result, we can leverage multiple types of neural signals mapped to the same space to improve downstream tasks, and demonstrate the complementary strengths of different neural modalities. This approach holds significant potential for advancing neuroscience research, improving AI systems, and developing neuroprosthetics and brain-computer interfaces.

q-bio.NC↗

arXiv2024

Optimized Path Planning for USVs under Ocean Currents

Unmanned Surface Vehicles (USVs) in the ocean environment, considering various spatiotemporal factors such as ocean currents and other energy consumption factors. The paper uses Gaussian Process Motion Planning (GPMP2), a Bayesian optimization method that has shown promising results in continuous and nonlinear motion planning algorithms. The proposed work improves GPMP2 by incorporating a new spatiotemporal factor for tracking and predicting ocean currents using a spatiotemporal Bayesian inference. The algorithm is applied to the USV path planning and is shown to optimize for smoothness, obstacle avoidance, and ocean currents in a challenging environment. The work is relevant for practical applications in ocean scenarios where optimal path planning for USVs is essential for minimizing costs and optimizing performance.

cs.RO↗

arXiv2023

A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes

The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses. We used this dataset to build a digital twin of V4 via deep learning, generating a detailed topographical map of natural image preferences at each cortical position. The map revealed clustered functional domains for specific classes of natural image features. These ranged from surface-related attributes like color and texture to shape-related features such as edges, curvature, and facial features. We validated the model-predicted domains with additional widefield calcium-imaging and single-cell resolution two-photon imaging. Our study illuminates the detailed topological organization and neural codes in V4 that represent natural scenes.

q-bio.NC↗

arXiv2023

Manipulating chiral-spin transport with ferroelectric polarization

A collective excitation of the spin structure in a magnetic insulator can transmit spin-angular momentum with negligible dissipation. This quantum of a spin wave, introduced more than nine decades ago, has always been manipulated through magnetic dipoles, (i.e., timereversal symmetry). Here, we report the experimental observation of chiral-spin transport in multiferroic BiFeO3, where the spin transport is controlled by reversing the ferroelectric polarization (i.e., spatial inversion symmetry). The ferroelectrically controlled magnons produce an unprecedented ratio of up to 18% rectification at room temperature. The spin torque that the magnons in BiFeO3 carry can be used to efficiently switch the magnetization of adja-cent magnets, with a spin-torque efficiency being comparable to the spin Hall effect in heavy metals. Utilizing such a controllable magnon generation and transmission in BiFeO3, an alloxide, energy-scalable logic is demonstrated composed of spin-orbit injection, detection, and magnetoelectric control. This observation opens a new chapter of multiferroic magnons and paves an alternative pathway towards low-dissipation nanoelectronics.

physics.app-ph↗

arXiv2021

Imaging gate-tunable Tomonaga-Luttinger liquids in 1H-MoSe$_2$ mirror twin boundaries

One-dimensional electron systems (1DESs) exhibit properties that are fundamentally different from higher-dimensional systems. For example, electron-electron interactions in 1DESs have been predicted to induce Tomonaga-Luttinger liquid behavior. Naturally-occurring grain boundaries in single-layer semiconducting transition metal dichalcogenides provide 1D conducting channels that have been proposed to host Tomonaga-Luttinger liquids, but charge density wave physics has also been suggested to explain their behavior. Clear identification of the electronic ground state of this system has been hampered by an inability to electrostatically gate such boundaries and thereby tune their charge carrier concentration. Here we present a scanning tunneling microscopy/spectroscopy study of gate-tunable mirror twin boundaries (MTBs) in single-layer 1H-MoSe$_2$ devices. Gating here enables STM spectroscopy to be performed for different MTB electron densities, thus allowing precise characterization of electron-electron interaction effects. Visualization of MTB electronic structure under these conditions allows unambiguous identification of collective density wave excitations having two distinct velocities, in quantitative agreement with the spin-charge separation predicted by finite-length Tomonaga-Luttinger-liquid theory.

cond-mat.str-el↗

arXiv2020

Waves in screeching jets

The interaction between various wavelike structures in screeching jets is considered via both experimental measurements and linear stability theory. Velocity snapshots of screeching jets are used to produce a reduced order model of the screech cycle via proper orthogonal decomposition. Streamwise Fourier filtering is then applied to isolate the negative and positive wavenumber components, which for the waves of interest in this jet correspond to upstream and downstream-travelling waves. A global stability analysis on an experimentally derived base flow is conducted, demonstrating a close match to the results obtained via experiment, indicating that the mechanisms considered here are well represented in a linear framework. In both analyses, three distinct wavelike structures are evident. These three waves are those first shown by Tam & Hu (1989) to be supported by a cylindrical vortex sheet. One is the Kelvin-Helmholtz mode. Another is the upstream-travelling guided jet mode that has been a topic of recent discussion. The third component, with positive phase velocity, has not previously been identified in screeching jets. We provide evidence that this downstream-travelling wave is a duct-like mode similar to that recently identified in high-subsonic jets by Towne et al. (2017). We further demonstrate that both of the latter two waves are generated by the interaction between the Kelvin-Helmholtz wavepacket and the shock cells in the flow, according to a theory first proposed in Tam & Tanna (1982). Finally, we consider the periodic spatial modulation of the coherent velocity fluctuation evident in screeching jets, and show that this modulation is the result of the superposition of the three wavelike structures, with no evidence that the shocks in the flow modulate the growth of the Kelvin-Helmholtz wavepacket.

physics.flu-dyn↗

arXiv2020

Distributed Localization without Direct Communication Inspired by Statistical Mechanics

Distributed localization is essential in many robotic collective tasks such as shape formation and self-assembly.Inspired by the statistical mechanics of energy transition, this paper presents a fully distributed localization algorithm named as virtual particle exchange (VPE) localization algorithm, where each robot repetitively exchanges virtual particles (VPs) with neighbors and eventually obtains its relative position from the virtual particle (VP) amount it owns. Using custom-designed hardware and protocol, VPE localization algorithm allows robots to achieve localization using sensor readings only, avoiding direct communication with neighbors and keeping anonymity. Moreover, VPE localization algorithm determines the swarm center automatically, thereby eliminating the requirement of fixed beacons to embody the origin of coordinates. Theoretical analysis proves that the VPE localization algorithm can always converge to the same result regardless of initial state and has low asymptotic time and memory complexity. Extensive localization simulations with up to 10000 robots and experiments with 52 lowcost robots are carried out, which verify that VPE localization algorithm is scalable, accurate and robust to sensor noises. Based on the VPE localization algorithm, shape formations are further achieved in both simulations and experiments with 52 robots, illustrating that the algorithm can be directly applied to support swarm collaborative tasks.

cs.RO↗

arXiv2016

A simple setup to measure muon lifetime and electron energy spectrum of muon decay and its Monte Carlo simulation

We designed a simple setup to measure the muon lifetime and the electron energy spectra of muon decay. A low cost coincidental circuit was designed to select the signals of muon decay events detected by a plastic scintillator detector. It triggered a digital oscilloscope to record the signals of muon decay events for measuring the muon lifetime and electron energy spectrum. A Landau-distribution energy loss method was introduced to conduct the energy calibration of the system. The experimental results were well reproduced by the Monte Carlo simulation. The software and hardware of the system are completely open to students, thus more helpful for instruction and motivation.

physics.ins-det↗