arXiv subjects

Xiaoli Ma

Publications and source records attributed to Xiaoli Ma.

At least 19 recordsLinked to original sources

arXiv2026

Magneto-Structural Coupling Enables Cryogenic Cation Redistribution in a Spinel Oxide

Ionic transport in oxides is generally frozen at cryogenic temperatures, where thermal energy lies far below typical cation-migration barriers. Neutron powder diffraction reveals progressive Fe/Mg redistribution between tetrahedral (A) and octahedral (B) sites in the spinel Mg0.5Fe0.5TiFeO4 upon cooling from 200 K to 5 K. A-site Fe occupancy increases toward near completion at 5 K within Rietveld resolution, while Ti remains on the B site. This exchange coincides with complex magnetic correlations rather than a classical thermally activated window. Low-temperature magnetostrictive volume changes indicate strong spin-lattice coupling, but do not identify magnetostriction as the sole thermodynamic driver. Room-temperature high-pressure X-ray diffraction produces the opposite occupancy trend, showing that volume contraction alone cannot explain the cryogenic site exchange. These results point to magneto-structural free-energy minimization as a plausible mechanism for unlocking cryogenic cation mobility in a correlated spinel oxide.

cond-mat.mtrl-sci↗

arXiv2026

Anomalous Structural Response of Quasi-One-Dimensional Antiferromagnetic Metal KMn6Bi5 under high pressure

We report high-pressure single-crystal X-ray diffraction measurements on the quasi-one-dimensional (Q1D) antiferromagnetic metal KMn6Bi5 up to 12.5 GPa, revealing the detailed pressure evolution of its atomic coordination environment. We find that the lattice exhibits pronounced anisotropic compressibility-the relative changes in the a and b lattice parameters reach a/a0=0.91 and b/b0 = 0.94 at 12.5 GPa-and a distinct structural anomaly emerges near 11 GPa without any symmetry-breaking. Detailed structural analysis further uncovers an anomalous hardening of the Mn nanotubes between 5 and 11 GPa, followed by a configuration optimization of the Mn/Bi nanotubes around 11 GPa. These features correlate closely with the reported pressure-temperature phase diagram of KMn6Bi5 and compare favorably with the chemical pressure effects induced by substituting K with Na, Rb, or Cs. Our findings provide key microscopic insights into how coordination environment modulation governs the stability of electronic orders in low-dimensional systems.

cond-mat.mtrl-sci↗

arXiv2026

Structural responses incipient to pressure-driven antiferromagnetic quantum critical point of van der Waals heavy-fermion metal CeSiI

CeSiI is a van der Waals heavy-fermion metal recently found to exhibit unconventional superconductivity near a pressure-induced antiferromagnetic quantum critical point (QCP) at Pc =6 GPa. Here, we report a comprehensive single-crystal X-ray diffraction study of CeSiI under high pressures up to 8.3 GPa at room temperature, revealing subtle structural responses that precede pressure-driven QCP. We find that the unit-cell volume decreases smoothly upon compression without showing any structural phase transition in the investigated pressure range. Intriguingly, we observe abrupt and concurrent anisotropic responses of the lattice parameters around Pc =6 GPa, i.e., the a-axis contracts while the c-axis enlongated suddenly, with the unit-cell volume smoothily varies with pressure. Structural refinements further show that these lattice anomalies primarily originate from changes of Ce-Ce and Ce-Si bond lengths, as well as a flattening of the inner honeycomb Si layer within the CeSiI monolayer around Pc. Our findings establish an interesting case linking pressure-driven electronic transition of QCP at low temperatures to incipient structural responses at room temperature, thereby providing fresh insight into the pressure-temperature phase diagram of CeSiI.

cond-mat.str-el↗

arXiv2025

Compression-induced magnetic obstructed atomic insulator and spin singlet state in antiferromagnetic KV2Se2O

Among the complex many-body systems, the metal-insulator transition stands out as a cornerstone and a particularly fertile ground for scientific inquiry. The established models including Mott insulator, Anderson localization and Peierls transition, are still insufficient to capture the complex and intertwined phenomena observed in certain material systems. KV2Se2O, a newly discovered room-temperature altermagnetic candidate exhibiting a spin-density-wave transition below 100 K, provides a unique platform to investigate the interplay of many-body effects and unconventional magnetism, specifically the anticipated metal-insulator transition under extreme conditions. Here, we report a compression-induced insulator by suppressing the metallic behavior without structural phase transition. The newly opened gap is estimated to be 40 meV at around 43.5 GPa, given direct evidence for the insulating state. A concurrent switching of carrier type demonstrates the large Fermi surface reconstruction crossing the metal-insulator transition. The density functional theory calculations indicate that the discovered V+2.5-based insulator is a magnetic obstructed atomic insulator, being a spin-singlet state with bonding orbital order. This work not only presents an archetype of a pressure-driven metal-insulator transition decoupled from structural change but also delivers fundamental physical insights into the metal-insulator transition.

cond-mat.str-el↗

arXiv2025

SPH-Net: A Co-Attention Hybrid Model for Accurate Stock Price Prediction

Prediction of stock price movements presents a formidable challenge in financial analytics due to the inherent volatility, non-stationarity, and nonlinear characteristics of market data. This paper introduces SPH-Net (Stock Price Prediction Hybrid Neural Network), an innovative deep learning framework designed to enhance the accuracy of time series forecasting in financial markets. The proposed architecture employs a novel co-attention mechanism that initially processes temporal patterns through a Vision Transformer, followed by refined feature extraction via an attention mechanism, thereby capturing both global and local dependencies in market data. To rigorously evaluate the model's performance, we conduct comprehensive experiments on eight diverse stock datasets: AMD, Ebay, Facebook, FirstService Corp, Tesla, Google, Mondi ADR, and Matador Resources. Each dataset is standardized using six fundamental market indicators: Open, High, Low, Close, Adjusted Close, and Volume, representing a complete set of features for comprehensive market analysis. Experimental results demonstrate that SPH-Net consistently outperforms existing stock prediction models across all evaluation metrics. The model's superior performance stems from its ability to effectively capture complex temporal patterns while maintaining robustness against market noise. By significantly improving prediction accuracy in financial time series analysis, SPH-Net provides valuable decision-support capabilities for investors and financial analysts, potentially enabling more informed investment strategies and risk assessment in volatile market conditions.

cs.CE↗

arXiv2025

AttnBoost: Retail Supply Chain Sales Insights via Gradient Boosting Perspective

Forecasting product demand in retail supply chains presents a complex challenge due to noisy, heterogeneous features and rapidly shifting consumer behavior. While traditional gradient boosting decision trees (GBDT) offer strong predictive performance on structured data, they often lack adaptive mechanisms to identify and emphasize the most relevant features under changing conditions. In this work, we propose AttnBoost, an interpretable learning framework that integrates feature-level attention into the boosting process to enhance both predictive accuracy and explainability. Specifically, the model dynamically adjusts feature importance during each boosting round via a lightweight attention mechanism, allowing it to focus on high-impact variables such as promotions, pricing, and seasonal trends. We evaluate AttnBoost on a large-scale retail sales dataset and demonstrate that it outperforms standard machine learning and deep tabular models, while also providing actionable insights for supply chain managers. An ablation study confirms the utility of the attention module in mitigating overfitting and improving interpretability. Our results suggest that attention-guided boosting represents a promising direction for interpretable and scalable AI in real-world forecasting applications.

cs.LG↗

arXiv2025

Vector Orthogonal Chirp Division Multiplexing Over Doubly Selective Channels

In this letter, we extend orthogonal chirp division multiplexing (OCDM) to vector OCDM (VOCDM) to provide more design freedom to deal with doubly selective channels. The VOCDM modulation is implemented by performing M parallel N-size inverse discrete Fresnel transforms (IDFnT). Based on the complex exponential basis expansion model (CE-BEM) for doubly selective channels, we derive the VOCDM input-output relationship, and show performance tradeoffs of VOCDM with respect to (w.r.t.) its modulation parameters M and N. Specifically, we investigate the diversity and peak-to-average power ratio (PAPR) of VOCDM w.r.t. M and N. Under doubly selective channels, VOCDM exhibits superior diversity performance as long as the parameters M and N are configured to satisfy some constraints from the delay and the Doppler spreads of the channel, respectively. Furthermore, the PAPR of VOCDM signals decreases with a decreasing N. These theoretical findings are verified through numerical simulations.

eess.SP↗

arXiv2025

Modeling Insider Filing Delays in Financial Markets with an Interpretable XGBoost Framework

Timely disclosure of insider transactions is a cornerstone of market transparency, yet delays in filing remain widespread and challenging to monitor at scale. This study introduces a comprehensive insider filing delay dataset spanning more than four million Form 4 transactions from 2002 to 2025, enriched with annotations on insider roles, governance attributes, and firm-level indicators. Building on these data, we present a hybrid framework that integrates a state-space encoder with an XGBoost classifier to capture temporal trading patterns while retaining interpretability essential for regulatory auditing. The framework consistently outperforms statistical models, deep sequence learners, and large language model baselines, achieving balanced gains in precision, recall, and F1-score. Feature ablation analyses highlight the predictive importance of insider history, spatiotemporal factors, and governance signals, shedding light on the behavioral drivers of both minor oversights and systematic violations. Beyond accuracy, the dataset and framework establish a reproducible benchmark for studying disclosure compliance, offering regulators and researchers transparent tools to strengthen market integrity.

cs.CE↗

arXiv2025

Observation of a $Pbca$ phase and robust metallicity in $\rm{RuO_2}$ under pressure

$\rm{RuO_2}$ stands as a quintessential rutile-type compound under ambient conditions, with its structural exploration under pressure bearing significant implications for both phase transition investigations and Earth science. Nonetheless, the precise phase transition sequence remains a debate. In this study, we disclose the emergence of the $Pbca$ phase alongside the enduring metallic character of $\rm{RuO_2}$ under megabar pressure. Employing state-of-the-art synchrotron X-ray diffraction, our observations delineate a phase transition trajectory progressing through rutile, $\rm{CaCl_2}$, and ultimately $Pbca$ phases. Notably, the $Pbca$ phase manifests immediately just after the rutile-$\rm{CaCl_2}$ transition, confining a narrow pressure regime for the pure $\rm{CaCl_2}$-type phase. Within the pressure range of 15.5 to 35.0 GPa, a coexistence of the $\rm{CaCl_2}$-type and $Pbca$ phases is observed, transforming to a sole presence of the $Pbca$ phase beyond 35.0 GPa. Electrical transport measurements conducted on both single crystal and powder samples confirm the enduring metallic conductivity of $\rm{RuO_2}$, persisting up to at least $\sim$120 GPa, albeit exhibiting a diminished conductivity at ultrahigh pressures due to a reduction in electronic density of states at the Fermi level. This study furnishes compelling evidence for the presence of the $Pbca$ phase across a broad pressure range, diverging from the previously widely acknowledged $Pa\bar{3}$ phase, thereby offering crucial insights into phase transition phenomena in other metal dioxides and advancing our comprehension of electronic behaviors within 4d and 5d electron systems.

cond-mat.mtrl-sci↗

arXiv2024

Imaging the Meissner effect in pressurized bilayer nickelate with integrated multi-parameter quantum sensor

Recent reports on the signatures of high-temperature superconductivity with a critical temperature Tc close to 80 K have triggered great research interest and extensive follow-up studies. Although the zero resistance has been successfully achieved under improved hydrostatic pressure conditions, the Meissner effect of $\mathrm{La_{3}Ni_{2}O_{7-\delta}}$ under high pressure remains controversial. Here, using shallow nitrogen-vacancy centers implanted on the culet of diamond anvils as in-situ quantum sensors, we observe compelling evidence for the Meissner effect in polycrystalline bilayer nickelate samples: the magnetic field expulsion during both field cooling and field warming processes. In particular, we explore the multiparameter measurement capacity of the diamond quantum sensors to extract the weak demagnetization signal of $\mathrm{La_{3}Ni_{2}O_{7-\delta}}$. The correlated measurements of Raman spectra and magnetic imaging indicate an incomplete structural transformation related to the displacement of oxygen ions emerging in the non-superconducting region. Our work clarifies the controversy about the Meissner effect of $\mathrm{La_{3}Ni_{2}O_{7-\delta}}$ and contributes to the development of quantum sensing of weak signals under high-pressure conditions.

cond-mat.supr-con↗

arXiv2024

Designing Affine OCDM Systems with Maximum Diversity

This work considers the problem of enabling maximum multipath diversity of orthogonal chirp division multiplexing (OCDM)-based systems. We define and study an Affine OCDM (A-OCDM) system in which a chirp parameter is adapted to enable maximum diversity offered by frequency selective channels. Our proposed system also reduces implementation complexity by eliminating the need for precoding, compared to linear constellation precoded OCDM. Corroborating simulations are provided to show that A-OCDM also preserves OCDM systems' resilience against interference offered by spreading.

eess.SP↗

arXiv2024

Density-wave-like gap evolution in La$_3$Ni$_2$O$_7$ under high pressure revealed by ultrafast optical spectroscopy

Density wave (DW) order is believed to be correlated with superconductivity in the recently discovered high-temperature superconductor La$_3$Ni$_2$O$_7$. However, experimental investigations of its evolution under high pressure are still lacking. Here, we explore the quasiparticle dynamics in bilayer nickelate La$_3$Ni$_2$O$_7$ single crystals using ultrafast optical pump-probe spectroscopy under high pressures up to 34.2 GPa. At ambient pressure, the temperature-dependent relaxation dynamics demonstrate a phonon bottleneck effect due to the opening of an energy gap around 151 K. The energy scale of the DW-like gap is determined to be 66 meV by the Rothwarf-Taylor model. Combined with recent experiential results, we propose that this DW-like transition at ambient pressure and low temperature is spin density wave (SDW). With increasing pressure, this SDW order is significantly suppressed up to 13.3 GPa before it completely disappears around 26 GPa. Remarkably, at pressures above 29.4 GPa, we observe the emergence of another DW-like order with a transition temperature of approximately 135 K, which is probably related to the predicted charge density wave (CDW) order. Our study provides the experimental evidence of the evolution of the DW-like gap under high pressure, offering critical insights into the correlation between DW order and superconductivity in La$_3$Ni$_2$O$_7$.

cond-mat.supr-con↗

arXiv2023

Carrier Frequency Offset Estimation for OCDM with Null Subchirps

In this paper, we investigate the carrier frequency offset (CFO) identifiability problem in orthogonal chirp division multiplexing (OCDM) systems. We propose a transmission scheme by inserting consecutive null subchirps. A CFO estimator is accordingly developed to achieve a full acquisition range. We further demonstrate that the proposed transmission scheme not only help to resolve CFO identifiability issues but also enable multipath diversity for OCDM systems. Simulation results corroborate our theoretical findings.

eess.SP↗

arXiv2023

Observation of Emergent Superconductivity in the Quantum Spin Hall Insulator Ta2Pd3Te5 via Pressure Manipulation

Quantum Spin Hall (QSH) insulators possess distinct helical in-gap states, enabling their edge states to act as one-dimensional conducting channels when backscattering is prohibited by time-reversal symmetry. However, it remains challenging to achieve high-performance combinations of nontrivial topological QSH states with superconductivity for applications and requires understanding of the complicated underlying mechanisms. Here, our experimental observations for a novel superconducting phase in the pressurized QSH insulator Ta2Pd3Te5 is reported, and the high-pressure phase maintains its original ambient pressure lattice symmetry up to 45 GPa. Our in-situ high-pressure synchrotron X-ray diffraction, electrical transport, infrared reflectance, and Raman spectroscopy measurements, in combination with rigorous theoretical calculations, provide compelling evidence for the association between the superconducting behavior and the abnormal densified phase. The isostructural transition was found to modify the topology of the Fermi surface directly, accompanied by a fivefold amplification of the density of states at 20 GPa compared to ambient pressure, which synergistically fosters the emergence of robust superconductivity. A profound comprehension of the fascinating properties exhibited by the compressed Ta2Pd3Te5 phase is achieved, highlighting the extraordinary potential of van der Waals (vdW) QSH insulators for exploring and investigating high-performance electronic advanced devices under extreme conditions.

cond-mat.mtrl-sci↗

arXiv2023

Precheck Sequence Based False Base Station Detection During Handover: A Physical Layer Security Scheme

False Base Station (FBS) attack has been a severe security problem for the cellular network since 2G era. During handover, the user equipment (UE) periodically receives state information from surrounding base stations (BSs) and uploads it to the source BS. The source BS compares the uploaded signal power and shifts UE to another BS that can provide the strongest signal. An FBS can transmit signal with the proper power and attract UE to connect to it. In this paper, based on the 3GPP standard, a Precheck Sequence-based Detection (PSD) Scheme is proposed to secure the transition of legal base station (LBS) for UE. This scheme first analyzes the structure of received signals in blocks and symbols. Several additional symbols are added to the current signal sequence for verification. By designing a long table of symbol sequence, every UE which needs handover will be allocated a specific sequence from this table. The simulation results show that the performance of this PSD Scheme is better than that of any existing ones, even when a specific transmit power is designed for FBS.

eess.SP↗

arXiv2022

Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement

Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based attention model, U-Net$_{At}$, to enhance adversarial speech signals. Specifically, we evaluate the model performance by interpretable speech recognition metrics and discuss the model performance by the augmented adversarial training. Our experiments show that our proposed U-Net$_{At}$ improves the perceptual evaluation of speech quality (PESQ) from 1.13 to 2.78, speech transmission index (STI) from 0.65 to 0.75, short-term objective intelligibility (STOI) from 0.83 to 0.96 on the task of speech enhancement with adversarial speech examples. We conduct experiments on the automatic speech recognition (ASR) task with adversarial audio attacks. We find that (i) temporal features learned by the attention network are capable of enhancing the robustness of DNN based ASR models; (ii) the generalization power of DNN based ASR model could be enhanced by applying adversarial training with an additive adversarial data augmentation. The ASR metric on word-error-rates (WERs) shows that there is an absolute 2.22 $\%$ decrease under gradient-based perturbation, and an absolute 2.03 $\%$ decrease, under evolutionary-optimized perturbation, which suggests that our enhancement models with adversarial training can further secure a resilient ASR system.

eess.AS↗

arXiv2021

Rethinking the Image Feature Biases Exhibited by Deep CNN Models

In recent years, convolutional neural networks (CNNs) have been applied successfully in many fields. However, such deep neural models are still regarded as black box in most tasks. One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how they are processed by CNNs. It is widely accepted that CNN models combine low-level features to form complex shapes until the object can be readily classified, however, several recent studies have argued that texture features are more important than other features. In this paper, we assume that the importance of certain features varies depending on specific tasks, i.e., specific tasks exhibit a feature bias. We designed two classification tasks based on human intuition to train deep neural models to identify anticipated biases. We devised experiments comprising many tasks to test these biases for the ResNet and DenseNet models. From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased toward a specific anticipated feature.

cs.CV↗

arXiv2021

Pressure-driven electronic and structural phase transition in intrinsic magnetic topological insulator MnSb2Te4

Intrinsic magnetic topological insulators provide an ideal platform to achieve various exciting physical phenomena. However, this kind of materials and related research are still very rare. In this work, we reported the electronic and structural phase transitions in intrinsic magnetic topological insulator MnSb2Te4 driven by hydrostatic pressure. Electric transport results revealed that temperature dependent resistance showed a minimum value near short-range antiferromagnetic (AFM) ordering temperature TN', the TN' values decline with pressure, and the AFM ordering was strongly suppressed near 10 GPa and was not visible above 11.5 GPa. The intensity of three Raman vibration modes in MnSb2Te4 declined quickly starting from 7.5 GPa and these modes become undetectable above 9 GPa, suggesting possible insulator-metal transition, which is further confirmed by theoretical calculation. In situ x-ray diffraction (XRD) demonstrated that an extra diffraction peak appears near 9.1 GPa and MnSb2Te4 started to enter an amorphous-like state above 16.6 GPa, suggesting the structural origin of suppressed AFM ordering and metallization. This work has demonstrated the correlation among interlayer interaction, magnetic ordering, and electric behavior, which could be benefit for the understanding of the fundamental properties of this kind of materials and devices.

cond-mat.mtrl-sci↗