arXiv subjects

Xie Zhang

Publications and source records attributed to Xie Zhang.

11 recordsLinked to original sources

arXiv2026

TaFall: Balance-Informed Fall Detection via Passive Thermal Sensing

Falls are a major cause of injury and mortality among older adults, yet most incidents occur in private indoor environments where monitoring must balance effectiveness with privacy. Existing privacy-preserving fall detection approaches, particularly those based on radio frequency sensing, often rely on coarse motion cues, which limits reliability in real-world deployments. We introduce TaFall, a balance-informed fall detection system based on low-cost, privacy-preserving thermal array sensing. The key insight is that TaFall models a fall as a process of balance degradation and detects falls by estimating pose-driven biomechanical balance dynamics. To enable this capability from low-resolution thermal array maps, we propose (i) an appearance-motion fusion model for robust pose reconstruction, (ii) physically grounded balance-aware learning, and (iii) pose-bridged pretraining to improve robustness. TaFall achieves a detection rate of 98.26% with a false alarm rate of 0.65% on our dataset with over 3,000 fall instances from 35 participants across diverse indoor environments. In 27 day deployments across four homes, TaFall attains an ultra-low false alarm rate of 0.00126% and a pilot bathroom study confirms robustness under moisture and thermal interference. Together, these results establish TaFall as a reliable and privacy-preserving approach to fall detection in everyday living environments.

cs.CV↗

arXiv2025

Control of magnetic transition, metal-semiconductor transition, and magnetic anisotropy in noncentrosymmetric monolayer Cr$_2$Ge$_2$Se$_3$Te$_3$

Recent advances in two-dimensional materials have greatly expanded the family of ferromagnetic materials. The well-known 2D ferromagnets, such as CrI$_3$, Cr$_2$Ge$_2$Te$_6$, and Fe$_3$GeTe$_2$ monolayers, are characterized by centrosymmetric crystal structures. In contrast, ferromagnetic ordering in 2D noncentrosymmetric materials remains an underexplored area. Here we report a Janus ferromagnet, Cr$_2$Ge$_2$Se$_3$Te$_3$ with inversion symmetry breaking, through first-principles calculations. This monolayer can undergo a ferromagnetic-antiferromagnetic transformation and a metal-semiconductor transition under different strains. Additionally, the strength of magnetocrystalline anisotropy energy (MAE) can be modulated by electric field or strain. In particular, the magnetization easy axis can be altered from in-plane to out-of-plane under strain. We find that Te$_3$ atoms play a key role in determining the MAE, where contributions are primarily from $p_z / p_y$ and $p_x / p_y$ orbitals. This study of Janus ferromagnetic materials has provided a promising platform for the research on the control of magnetism by strain or electric field.

cond-mat.mtrl-sci↗

arXiv2025

Towards White-Box Deep Wireless Sensing

The empirical success of deep learning has spurred its application to the radio-frequency (RF) domain, leading to significant advances in Deep Wireless Sensing (DWS). However, most existing DWS models remain black boxes, with ad-hoc architectures and learned representations lacking explicit physical and mathematical grounding, which limits their reliability and generalizability in real-world deployments. We present RF-CRATE, an early step towards white-box DWS grounded in the complex sparse rate reduction principle. Using the CR-Calculus framework, we derive a fully complex-valued transformer with mathematically interpretable self-attention and residual modules. To address labeled data scarcity, we introduce subspace regularization to enhance representation diversity, yielding a 19.98% average improvement. We evaluate RF-CRATE across heterogeneous RF modalities and human sensing tasks, including activity, gait, and gesture recognition, pose estimation, and respiration monitoring. Experiments on five datasets show that RF-CRATE remains competitive with strong black-box models while providing mathematically interpretable architectures and representations. Moreover, the complex-valued design achieves a 3.39% gain in classification accuracy and a 10.34% reduction in regression error. Our results demonstrate that mathematically grounded models can achieve strong performance in wireless sensing, offering a promising step towards physically aligned white-box DWS systems.

cs.LG↗

arXiv2025

TAPOR: 3D Hand Pose Reconstruction with Fully Passive Thermal Sensing for Around-Device Interactions

This paper presents the design and implementation of TAPOR, a privacy-preserving, non-contact, and fully passive sensing system for accurate and robust 3D hand pose reconstruction for around-device interaction using a single low-cost thermal array sensor. Thermal sensing using inexpensive and miniature thermal arrays emerges with an excellent utility-privacy balance, offering an imaging resolution significantly lower than cameras but far superior to RF signals like radar or WiFi. The design of TAPOR, however, is challenging, mainly because the captured temperature maps are low-resolution and textureless. To overcome the challenges, we investigate thermo-depth and thermo-pose properties, proposing a novel physics-inspired neural network that learns effective 3D spatial representations of potential hand poses. We then formulate the 3D pose reconstruction problem as a distinct retrieval task, enabling accurate hand pose determination from the input temperature map. To deploy TAPOR on IoT devices, we introduce an effective heterogeneous knowledge distillation method, reducing computation by 377x. TAPOR is fully implemented and tested in real-world scenarios, showing remarkable performance, supported by four gesture control and finger tracking case studies. We envision TAPOR to be a ubiquitous interface for around-device control and have open-sourced it at https://github.com/aiot-lab/TAPOR.

cs.HC↗

arXiv2025

Nature of nonanalytic chemical short-range order in metallic alloys

Nonanalytic chemical short-range order (SRO) has long been observed in diffuse scattering experiments for metallic alloys. However, considerable debate surrounds the validity of these observations due to the unresolved nature of the nonanalyticity. Using prototypical face-centered cubic alloys as an example, here we demonstrate that SRO in metallic alloys is mostly nonanalytic at Γ. The nonanalyticity stems from the elastic anisotropy and long-range atomic interactions of the \emph{host} lattice. The physical insights substantially improve our understanding of chemical order in alloys and resolves the long-standing debate in the field. Nonanalytic SRO is expected to be general in alloys and the nonanalyticity may serve as a unique feature to verify the intensely debated existence of SRO in compositionally complex alloys.

cond-mat.mtrl-sci↗

arXiv2025

Distinguishing thermal fluctuations from polaron formation in halide perovskites

Recent angle-resolved photoelectron spectroscopy (ARPES) measurements of the hole effective mass in CsPbBr$_3$ revealed an enhancement of $\sim$50 % compared to the bare mass computed from first principles for CsPbBr$_3$ at $T = 0 K$. This large enhancement was interpreted as evidence of polaron formation. Employing accurate finite-temperature first-principles calculations, we show that the calculated hole effective mass of CsPbBr$_3$ at $T = 300 K$ can explain experimental results without invoking polarons. Thermal fluctuations are particularly strong in halide perovskites compared to conventional semiconductors such as Si and GaAs, and cannot be ignored when comparing with experiment. We not only resolve the debate on polaron formation in halide perovskites, but also demonstrate the general importance of including thermal fluctuations in first-principles calculations for strongly anharmonic materials.

cond-mat.mtrl-sci↗

arXiv2024

First-principles theory of direct-gap optical emission in hexagonal Ge and its enhancement via strain engineering

The emergence of hexagonal Ge (2H-Ge) as a candidate direct-gap group-IV semiconductor for Si photonics mandates rigorous understanding of its optoelectronic properties. Theoretical predictions of a "pseudo-direct" band gap, characterized by weak oscillator strength, contrast with a claimed high radiative recombination coefficient $B$ comparable to conventional (cubic) InAs. We compute $B$ in 2H-Ge from first principles and quantify its dependence on temperature, carrier density and strain. For unstrained 2H-Ge, our calculated spontaneous emission spectra corroborate that measured photoluminescence corresponds to direct-gap emission, but with $B$ being approximately three orders of magnitude lower than in InAs. We confirm a pseudo-direct- to direct-gap transition under $\sim 2$\% [0001] uniaxial tension, which can enhance $B$ by up to three orders of magnitude, making it comparable to that of InAs. Beyond quantifying strong enhancement of $B$ via strain engineering, our analysis suggests the dominance of additional, as-yet unquantified recombination mechanisms in this nascent material.

cond-mat.mtrl-sci↗

arXiv2024

TADAR: Thermal Array-based Detection and Ranging for Privacy-Preserving Human Sensing

Human sensing has gained increasing attention in various applications. Among the available technologies, visual images offer high accuracy, while sensing on the RF spectrum preserves privacy, creating a conflict between imaging resolution and privacy preservation. In this paper, we explore thermal array sensors as an emerging modality that strikes an excellent resolution-privacy balance for ubiquitous sensing. To this end, we present TADAR, the first multi-user Thermal Array-based Detection and Ranging system that estimates the inherently missing range information, extending thermal array outputs from 2D thermal pixels to 3D depths and empowering them as a promising modality for ubiquitous privacy-preserving human sensing. We prototype TADAR using a single commodity thermal array sensor and conduct extensive experiments in different indoor environments. Our results show that TADAR achieves a mean F1 score of 88.8% for multi-user detection and a mean accuracy of 32.0 cm for multi-user ranging, which further improves to 20.1 cm for targets located within 3 m. We conduct two case studies on fall detection and occupancy estimation to showcase the potential applications of TADAR. We hope TADAR will inspire the vast community to explore new directions of thermal array sensing, beyond wireless and acoustic sensing. TADAR is open-sourced on GitHub: https://github.com/aiot-lab/TADAR.

cs.HC↗

arXiv2023

Graph deep learning accelerated efficient crystal structure search and feature extraction

Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, which includes a symmetry-based combinatorial crystal optimization program (SCCOP) and a feature additive attribution model, to significantly reduce computational costs and to extract property-related structural features. Our method is highly accurate and predictive, and extracts structural features from desired structures to guide materials design. As a case study, we apply our new approach to a two-dimensional B-C-N system, which identifies 28 previously undiscovered stable structures out of 82 compositions; our analysis further establishes the structural features that contribute most to energy and bandgap. Compared to conventional approaches, SCCOP is about 10 times faster while maintaining a comparable accuracy. Our new framework is generally applicable to all types of systems for precise and efficient structural search, providing new insights into the relationship between ML-extracted structural features and physical properties.

cond-mat.mtrl-sci↗

arXiv2021

WiFi-based Multi-task Sensing

WiFi-based sensing has aroused immense attention over recent years. The rationale is that the signal fluctuations caused by humans carry the information of human behavior which can be extracted from the channel state information of WiFi. Still, the prior studies mainly focus on single-task sensing (STS), e.g., gesture recognition, indoor localization, user identification. Since the fluctuations caused by gestures are highly coupling with body features and the user's location, we propose a WiFi-based multi-task sensing model (Wimuse) to perform gesture recognition, indoor localization, and user identification tasks simultaneously. However, these tasks have different difficulty levels (i.e., imbalance issue) and need task-specific information (i.e., discrepancy issue). To address these issues, the knowledge distillation technique and task-specific residual adaptor are adopted in Wimuse. We first train the STS model for each task. Then, for solving the imbalance issue, the extracted common feature in Wimuse is encouraged to get close to the counterpart features of the STS models. Further, for each task, a task-specific residual adaptor is applied to extract the task-specific compensation feature which is fused with the common feature to address the discrepancy issue. We conduct comprehensive experiments on three public datasets and evaluation suggests that Wimuse achieves state-of-the-art performance with the average accuracy of 85.20%, 98.39%, and 98.725% on the joint task of gesture recognition, indoor localization, and user identification, respectively.

cs.LG↗

arXiv2019

Bright magnetic dipole radiation from two-dimensional lead-halide perovskites

Light-matter interactions in semiconductor systems are uniformly treated within the electric dipole (ED) approximation, as multipolar interactions are considered "forbidden". Here, we demonstrate that this approximation inadequately describes light emission in novel two-dimensional hybrid organic-inorganic perovskite materials (2D HOIPs) --- a class of solution processable layered semiconductor with promising optoelectronic properties. Consequently, photoluminescence (PL) spectra become strongly dependent on the experimental geometry, a fact that is often overlooked, though critical for correct optical characterization of materials. Using energy-momentum and time-resolved spectroscopies, we experimentally demonstrate that low-energy sideband emission in 2D HOIPs exhibits a highly unusual, multipolar polarization and angle dependence. Using combined electromagnetic and quantum-mechanical analyses, we attribute this radiation pattern to an out-of-plane oriented magnetic dipole transition arising from the 2D character of the excited and ground state orbitals. Symmetry arguments point toward the presence of significant inversion symmetry-breaking mechanisms that are currently under great debate. These results provide a new perspective on the origins of unexpected sideband emission in HOIPs, clarify discrepancies in previous literature, and generally challenge the paradigm of ED-dominated light-matter interactions in novel optoelectronic materials.

cond-mat.mtrl-sci↗