arXiv subjects

Zeyu Deng

Publications and source records attributed to Zeyu Deng.

At least 19 recordsLinked to original sources

arXiv2026

Probing mesoscopic nonlocal screening in van der Waals heterostructures with polaritons

Predictive optical modelling of van der Waals (vdW) heterostructures is critical for meta-optics, near-field photonics and quantum technologies. At their buried interfaces, charge transfer and spatially extended screening challenge local descriptions based on layer-by-layer stacking of fixed permittivity tensors. However, such nonlocal corrections have been established mainly for plasmonic systems at {\aa}ngstr\"om-nanometre scales and are often assumed negligible on optical-wavelength scales. Here we challenge this view by uncovering a mesoscopic nonlocal screening regime, extending up to ~140 nm, at buried charge-transfer interfaces in transition-metal dichalcogenide/{\alpha}-molybdenum trioxide (TMDC/{\alpha}-MoO3) phonon-polaritonic heterostructures. Using phonon polaritons as an ultrasensitive probe, we quantify charge transfer from polariton-wavelength shifts and find a thickness-independent saturated response as {\alpha}-MoO3 is thinned. Rather than merely complicating optical modelling, this nonlocal saturation turns a design-level correction into an opportunity by yielding a transferable cross-material metric. Across more than 120 devices, this metric scales linearly with the work-function difference between the TMDC and {\alpha}-MoO3. We further identify a lattice-mismatch-set energy threshold for charge transfer, revising Anderson-type band alignment for vdW interfaces.

physics.optics↗

arXiv2025

Inferring Operator Emotions from a Motion-Controlled Robotic Arm

A remote robot operator's affective state can significantly impact the resulting robot's motions leading to unexpected consequences, even when the user follows protocol and performs permitted tasks. The recognition of a user operator's affective states in remote robot control scenarios is, however, underexplored. Current emotion recognition methods rely on reading the user's vital signs or body language, but the devices and user participation these measures require would add limitations to remote robot control. We demonstrate that the functional movements of a remote-controlled robotic avatar, which was not designed for emotional expression, can be used to infer the emotional state of the human operator via a machine-learning system. Specifically, our system achieved 83.3$\%$ accuracy in recognizing the user's emotional state expressed by robot movements, as a result of their hand motions. We discuss the implications of this system on prominent current and future remote robot operation and affective robotic contexts.

cs.RO↗

arXiv2025

CD-DPE: Dual-Prompt Expert Network Based on Convolutional Dictionary Feature Decoupling for Multi-Contrast MRI Super-Resolution

Multi-contrast magnetic resonance imaging (MRI) super-resolution intends to reconstruct high-resolution (HR) images from low-resolution (LR) scans by leveraging structural information present in HR reference images acquired with different contrasts. This technique enhances anatomical detail and soft tissue differentiation, which is vital for early diagnosis and clinical decision-making. However, inherent contrasts disparities between modalities pose fundamental challenges in effectively utilizing reference image textures to guide target image reconstruction, often resulting in suboptimal feature integration. To address this issue, we propose a dual-prompt expert network based on a convolutional dictionary feature decoupling (CD-DPE) strategy for multi-contrast MRI super-resolution. Specifically, we introduce an iterative convolutional dictionary feature decoupling module (CD-FDM) to separate features into cross-contrast and intra-contrast components, thereby reducing redundancy and interference. To fully integrate these features, a novel dual-prompt feature fusion expert module (DP-FFEM) is proposed. This module uses a frequency prompt to guide the selection of relevant reference features for incorporation into the target image, while an adaptive routing prompt determines the optimal method for fusing reference and target features to enhance reconstruction quality. Extensive experiments on public multi-contrast MRI datasets demonstrate that CD-DPE outperforms state-of-the-art methods in reconstructing fine details. Additionally, experiments on unseen datasets demonstrated that CD-DPE exhibits strong generalization capabilities.

cs.CV↗

arXiv2025

Phase Behavior and Ion Transport in Lithium-Niobium-Tantalum Oxide Alloys

Lithium niobate-tantalate mixtures have garnered considerable interest for their ability to merge the desirable properties of both end members, enabling diverse high-value applications, such as high-performance faradaic capacitors, non-linear optics, and protective coatings in rechargeable batteries. While numerous studies on the application of $\mathrm{LiNb_xTa_{1-x}O_3}$ exist, the phase behavior and properties of $\mathrm{Li_3Nb_xTa_{1-x}O_4}$ remain largely unexplored. In this work, we employ a multiscale approach that encompasses first-principles phonon calculations, cluster expansion, and Monte Carlo simulations to derive the temperature-composition phase diagram for $\mathrm{Li_3Nb_xTa_{1-x}O_4}$. Our findings reveal the critical role of vibrational entropy in accurately predicting phase stability, which promotes the solubility of Nb in $\mathrm{Li_3TaO_4}$ while suppressing the miscibility of Ta in $\mathrm{Li_3NbO_4}$. Additionally, we demonstrate that Nb/Ta mixing offers a promising avenue for tailoring the Li-ion conductivities of $\mathrm{Li_3Nb_xTa_{1-x}O_4}$. On the technical side, we demonstrated the importance of including vibrational entropy effects explicitly in Monte Carlo simulations dealing with multicomponent systems, beyond simple binary mixtures. On the application side, this study provides fundamental insights into the phase behavior and Li-ion transport properties of $\mathrm{Li_3Nb_xTa_{1-x}O_4}$, paving the way for its potential applications in energy storage and other fields.

cond-mat.mtrl-sci↗

arXiv2025

A Neuromorphic Incipient Slip Detection System using Papillae Morphology

Detecting incipient slip enables early intervention to prevent object slippage and enhance robotic manipulation safety. However, deploying such systems on edge platforms remains challenging, particularly due to energy constraints. This work presents a neuromorphic tactile sensing system based on the NeuroTac sensor with an extruding papillae-based skin and a spiking convolutional neural network (SCNN) for slip-state classification. The SCNN model achieves 94.33% classification accuracy across three classes (no slip, incipient slip, and gross slip) in slip conditions induced by sensor motion. Under the dynamic gravity-induced slip validation conditions, after temporal smoothing of the SCNN's final-layer spike counts, the system detects incipient slip at least 360 ms prior to gross slip across all trials, consistently identifying incipient slip before gross slip occurs. These results demonstrate that this neuromorphic system has stable and responsive incipient slip detection capability.

cs.RO↗

arXiv2025

Sync-TVA: A Graph-Attention Framework for Multimodal Emotion Recognition with Cross-Modal Fusion

Multimodal emotion recognition (MER) is crucial for enabling emotionally intelligent systems that perceive and respond to human emotions. However, existing methods suffer from limited cross-modal interaction and imbalanced contributions across modalities. To address these issues, we propose Sync-TVA, an end-to-end graph-attention framework featuring modality-specific dynamic enhancement and structured cross-modal fusion. Our design incorporates a dynamic enhancement module for each modality and constructs heterogeneous cross-modal graphs to model semantic relations across text, audio, and visual features. A cross-attention fusion mechanism further aligns multimodal cues for robust emotion inference. Experiments on MELD and IEMOCAP demonstrate consistent improvements over state-of-the-art models in both accuracy and weighted F1 score, especially under class-imbalanced conditions.

cs.MM↗

arXiv2025

Haptic-Based User Authentication for Tele-robotic System

Tele-operated robots rely on real-time user behavior mapping for remote tasks, but ensuring secure authentication remains a challenge. Traditional methods, such as passwords and static biometrics, are vulnerable to spoofing and replay attacks, particularly in high-stakes, continuous interactions. This paper presents a novel anti-spoofing and anti-replay authentication approach that leverages distinctive user behavioral features extracted from haptic feedback during human-robot interactions. To evaluate our authentication approach, we collected a time-series force feedback dataset from 15 participants performing seven distinct tasks. We then developed a transformer-based deep learning model to extract temporal features from the haptic signals. By analyzing user-specific force dynamics, our method achieves over 90 percent accuracy in both user identification and task classification, demonstrating its potential for enhancing access control and identity assurance in tele-robotic systems.

cs.RO↗

arXiv2024

Thermodynamics of Sodium-Lead Alloys for Negative Electrodes from First-Principles

Metals, such as tin, antimony, and lead (Pb) have garnered renewed attention for their potential use as alloyant-negative electrode materials in sodium (Na)-ion batteries (NIBs). Despite Pb's toxicity and its high molecular weight, lead is one of the most commonly recycled metals, positioning Pb as a promising candidate for a cost-effective, high-capacity anode material. Understanding the miscibility of Na into Pb is crucial for the development of high-energy density negative electrode materials for NIBs. Using a first-principles multiscale approach, we analyze the thermodynamic properties and estimate the Na-alloying voltage of the Na-Pb system by constructing the compositional phase diagram. In the Pb-Na system, we elucidate the phase boundaries of important phases, such as Pb-rich face-centered cubic and $β$-NaPb$_3$, thereby improving our understanding of the phase diagram of the Na-Pb alloy. Due to the strong ordering tendencies of the Na-Pb intermetallics (such as NaPb, Na$_5$Pb$_2$, and Na$_{15}$Pb$_4$), we do not observe any solid-solution behavior at intermediate and high Na concentrations.

cond-mat.mtrl-sci↗

arXiv2024

Effects of Grain Boundaries and Surfaces on Electronic and Mechanical Properties of Solid Electrolytes

Extended defects, including exposed surfaces and grain boundaries, are critical to the properties of polycrystalline solid electrolytes in all-solid-state batteries (ASSBs). These defects can significantly alter the mechanical and electronic properties of solid electrolytes, with direct manifestations on the performance of ASSBs. Here, by building a library of 590 surfaces and grain boundaries of 11 relevant solid electrolytes $-$including halides, oxides, and sulfides$-$ their electronic, mechanical, and thermodynamic characteristics are linked to the functional properties of polycrystalline solid electrolytes. It is found that the energy required to mechanically ``separate'' grain boundaries can be significantly lower than in the bulk region of materials, which can trigger preferential cracking of solid electrolyte particles in the grain boundary regions. The brittleness of ceramic solid electrolytes, inferred from the predicted low fracture toughnesses at the grain boundaries, contributes to their cracking under local pressure imparted by Lithium or Sodium penetration in the grain boundaries. Extended defects of solid electrolytes introduce new electronic ``interfacial'' states within bandgaps of solid electrolytes. These interfacial states alter and possibly increase locally the availability of free electrons and holes in solid electrolytes. Factoring effects arising from extended defects appear crucial to explain electrochemical and $-$mechanical observations in ASSBs.

cond-mat.mtrl-sci↗

arXiv2023

AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-based fusion methods can fully exploit the semantic features of each modality, they cannot distinguish the effective low and high frequency information of each modality and fuse them adaptively. To address this issue, we propose AdaFuse, in which multimodal image information is fused adaptively through frequency-guided attention mechanism based on Fourier transform. Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion. The CAF block enhances the high-frequency features of the different modalities so that the details in the fused images can be retained. Moreover, we design a novel loss function composed of structure loss and content loss to preserve both low and high frequency information. Extensive comparison experiments on several datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. The ablation experiments also validate the effectiveness of the proposed loss and fusion strategy.

cs.CV↗

arXiv2023

Kinetic Monte Carlo Simulations of Sodium Ion Transport in NaSICON Electrodes

The development of high-performance sodium (Na) ion batteries requires improved electrode materials. The energy and power densities of Na superionic conductor (NaSICON) electrode materials are promising for large-scale energy storage applications. However, several practical issues limit the full utilization of the theoretical energy densities of NaSICON electrodes. A pressing challenge lies in the limited sodium extraction in low Na content NaSICONs, e.g., $\rm Na_1V^{IV}V^{IV}(PO_4)_3 \leftrightarrow V^{V}V^{IV}(PO_4)_3 + 1e^- + 1Na^+$. Hence, it is important to quantify the Na-ion mobility in a broad range of NaSICON electrodes. Using a kinetic Monte Carlo approach bearing the accuracy of first-principles calculations, we elucidate the variability of Na-ion transport vs. Na content in three important NaSICON electrodes, Na$_{\rm x}$Ti$_{2}$(PO$_{4}$)$_{3}$, Na$_{\rm x}$V$_{2}$(PO$_{4}$)$_{3}$, and Na$_{\rm x}$Cr$_{2}$(PO$_{4}$)$_{3}$. Our study suggests that Na$^+$ transport in NaSICON electrodes is almost entirely determined by the local electrostatic and chemical environment set by the transition metal and the polyanionic scaffold. The competition with the ordering-disordering phenomena of Na-vacancies also plays a role in influencing Na-transport. We link the variations in the Na$^+$ kinetic properties by analyzing the competition of ligand field stabilization transition metal ions and their ionic radii. We interpret the limited Na-extraction at $x = 1$ observed experimentally by gaining insights into the local Na-vacancy interplay. We propose that targeted chemical substitutions of transition metals disrupting local charge arrangements will be critical to reducing the occurrence of strong Na$^+$-vacancy orderings at low Na concentrations, thus, expanding the accessible capacities of these electrode materials.

cond-mat.mtrl-sci↗

arXiv2023

On the Active Components in Crystalline Li-Nb-O and Li-Ta-O Coatings from First Principles

Layered-oxide $\mathrm{LiNi_xMn_yCo_{1-x-y}O_2}$ (NMC) positive electrodes with high Nickel content, deliver high voltages and energy densities. However, a high nickel content, e.g., $x$ = 0.8 (NMC 811), can lead to high surface reactivity, which can trigger thermal runaway and gas generation. While claimed safer, all-solid-state batteries still suffer from high interfacial resistance. Here, we investigate niobate and tantalate coating materials, which can mitigate the interfacial reactivities in Li-ion and all-solid-state batteries. First-principles calculations reveal the multiphasic nature of Li-Nb-O and Li-Ta-O coatings, containing mixtures of $\mathrm{LiNbO_3}$ and $\mathrm{Li_3NbO_4}$, or of $\mathrm{LiTaO_3}$ and $\mathrm{Li_3TaO_4}$. The concurrence of several phases in Li-Nb-O or Li-Ta-O modulates the type of stable native defects in these coatings. Li-Nb-O and Li-Ta-O coating materials can form favorably lithium vacancies $\mathrm{Vac^{'}_{Li}}$ and antisite defects $\mathrm{Nb^{\bullet \bullet \bullet \bullet}_{Li}}$ ($\mathrm{Ta^{\bullet \bullet \bullet \bullet}_{Li}}$) combined into charge-neutral defect complexes. Even in defective crystalline $\mathrm{LiNbO_3}$ (or $\mathrm{LiTaO_3}$), we reveal poor Li-ion conduction properties. In contrast, $\mathrm{Li_3NbO_4}$ and $\mathrm{Li_3TaO_4}$ that are introduced by high-temperature calcinations can provide adequate Li-ion transport in these coatings. Our in-depth investigation of the structure-property relationships in the important Li-Nb-O and Li-Ta-O coating materials helps to develop more suitable calcination protocols to maximize the functional properties of these niobates and tantalates.

cond-mat.mtrl-sci↗

arXiv2021

Phase Stability and Sodium-Vacancy Orderings in a NaSICON Electrode

We elucidate the thermodynamics of sodium (Na) intercalation into the sodium super-ionic conductor (NaSICON)-type electrode, Na$_x$V$_2$(PO$_4$)$_3$, for promising Na-ion batteries with high-power density. This is the first report of a computational temperature-composition phase diagram of the NaSICON-type electrode Na$_x$V$_2$(PO$_4$)$_3$. We identify two thermodynamically stable phases at the compositions Na$_2$V$_2$(PO$_4$)$_3$ and Na$_{3.5}$V$_2$(PO$_4$)$_3$, and their structural features are described for the first time based on our computational analysis. We unveil the crystal-structure and the electronic-structure origins of the ground-state compositions associated with specific Na/vacancy arrangements, which are driven by charge orderings on the vanadium sites. These results are significant for the optimization of high-energy and power densities electrodes for sustainable Na-ion batteries

cond-mat.mtrl-sci↗

arXiv2021

Unlocking the Origin of Compositional Fluctuations in InGaN Light Emitting Diodes

The accurate determination of the compositional fluctuations is pivotal in understanding their role in the reduction of efficiency in high indium content $In_{x}Ga_{1-x}N$ light-emitting diodes, the origin of which is still poorly understood. Here we have combined electron energy loss spectroscopy (EELS) imaging at sub-nanometer resolution with multiscale computational models to obtain a statistical distribution of the compositional fluctuations in $In_{x}Ga_{1-x}N$ quantum wells (QWs). Employing a multiscale computational model, we show the tendency of intrinsic compositional fluctuation in $In_{x}Ga_{1-x}N$ QWs at different Indium concentration and in the presence of strain. We have developed a systematic formalism based on the autonomous detection of compositional fluctuation in observed and simulated EELS maps. We have shown a direct comparison between the computationally predicted and experimentally observed compositional fluctuations. We have found that although a random alloy model captures the distribution of compositional fluctuations in relatively low In ($\sim$ 18%) content $In_{x}Ga_{1-x}N$ QWs, there exists a striking deviation from the model in higher In content ($\geq$ 24%) QWs. Our results highlight a distinct behavior in carrier localization driven by compositional fluctuations in the low and high In-content InGaN QWs, which would ultimately affect the performance of LEDs. Furthermore, our robust computational and atomic characterization method can be widely applied to study materials in which nanoscale compositional fluctuations play a significant role on the material performance.

cond-mat.mtrl-sci↗

arXiv2020

Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics

Glioma grading before surgery is very critical for the prognosis prediction and treatment plan making. We present a novel wavelet scattering-based radiomic method to predict noninvasively and accurately the glioma grades. The method consists of wavelet scattering feature extraction, dimensionality reduction, and glioma grade prediction. The dimensionality reduction was achieved using partial least squares (PLS) regression and the glioma grade prediction using support vector machine (SVM), logistic regression (LR) and random forest (RF). The prediction obtained on multimodal magnetic resonance images of 285 patients with well-labeled intratumoral and peritumoral regions showed that the area under the receiver operating characteristic curve (AUC) of glioma grade prediction was increased up to 0.99 when considering both intratumoral and peritumoral features in multimodal images, which represents an increase of about 13% compared to traditional radiomics. In addition, the features extracted from peritumoral regions further increase the accuracy of glioma grading.

cs.LG↗

arXiv2020

A Model of Double Descent for High-dimensional Binary Linear Classification

We consider a model for logistic regression where only a subset of features of size $p$ is used for training a linear classifier over $n$ training samples. The classifier is obtained by running gradient descent (GD) on logistic loss. For this model, we investigate the dependence of the classification error on the overparameterization ratio $κ=p/n$. First, building on known deterministic results on the implicit bias of GD, we uncover a phase-transition phenomenon for the case of Gaussian features: the classification error of GD is the same as that of the maximum-likelihood (ML) solution when $κ<κ_\star$, and that of the max-margin (SVM) solution when $κ>κ_\star$. Next, using the convex Gaussian min-max theorem (CGMT), we sharply characterize the performance of both the ML and the SVM solutions. Combining these results, we obtain curves that explicitly characterize the classification error for varying values of $κ$. The numerical results validate the theoretical predictions and unveil double-descent phenomena that complement similar recent findings in linear regression settings as well as empirical observations in more complex learning scenarios.

stat.ML↗

arXiv2019

CNN-Based Invertible Wavelet Scattering for the Investigation of Diffusion Properties of the In Vivo Human Heart in Diffusion Tensor Imaging

In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium structures in DTI with free-breathing acquisitions. The method is based on an invertible Wavelet Scattering achieved by means of Convolutional Neural Network (WSCNN). It consists of first extracting translation-invariant wavelet scattering features from DW images acquired at different trigger delays and then mapping the fused scattering features into motion-compensated spatial DW images by performing an inverse wavelet scattering transform achieved using CNN. The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing.

eess.IV↗

arXiv2019

Metal-free perovskites for non-linear optical materials

We identify the existence of nonlinear optical (NLO) activity in a number of novel $ABX_3$-type metal-free perovskites, where $A$ is a highly tuneable organic cation, $B$ is a NH$_4$ cation and $X$ a halide anion. Through systematic first-principles calculations, we identify important trends to chart the second-harmonic generation of this class of materials. We study three perovskites MDABCO-NH$_4$I$_3$, CNDABCO-NH$_4$I$_3$ and ODABCO-NH$_4$I$_3$ for use as deep-UV second-harmonic generation materials. We identify the role of the dipole moment imparted by the organic group on the $A$ cation as an important parameter to tune the NLO properties of these materials. We apply this knowledge functionalising the organic group DABCO with the highly polar cyanide CN$^-$ group, and we demonstrate a significant improvement of the NLO response in this family of materials. These findings can accelerate the application of metal free perovskites as inexpensive, non-toxic, earth-abundant materials for the next generation of optical communication applications.

cond-mat.mtrl-sci↗