arXiv subjects

Xiaoxia Wang

Publications and source records attributed to Xiaoxia Wang.

At least 19 recordsLinked to original sources

arXiv2026

Phonon-Localization-Driven Decoupling of Dual-Channel Transport for Record-Low Intrinsic Lattice Thermal Conductivity

A fundamental bottleneck in pushing the intrinsic lattice thermal conductivity of inorganic crystalline solids to its lowest limit arises from the inherent competition between the particle-like propagation ($κ_{\mathrm{L}}^{\mathrm{P}}$) and wave-like tunneling ($κ_{\mathrm{L}}^{\mathrm{C}}$) channels. Herein, we demonstrate that phonon localization provides a robust pathway to decouple the dual-channel transport, achieving record-low $κ_{\mathrm{L}}$ in quasi-1D ternary helical crystals. Despite the structural complexity leading to densely populated phonon branches and thus inducing abundant coherent phonons, the weak interchain interactions and heavy elements compress numerous branches into highly localized, nearly dispersionless flat bands. Such strong localization simultaneously suppresses both the diagonal and off-diagonal components of the group velocity, thereby synergistically suppressing $κ_{\mathrm{L}}^{\mathrm{P}}$ and $κ_{\mathrm{L}}^{\mathrm{C}}$. Taking InSeI as an example, the interchain room-temperature $κ_{\mathrm{L}}^{\mathrm{P}}$ and $κ_{\mathrm{L}}^{\mathrm{C}}$ are 0.145 and 0.053 W/mK, respectively, yielding an ultralow total $κ_{\mathrm{L}}$ of 0.198 W/mK. Weaker interchain interactions further drive the room-temperature $κ_{\mathrm{L}}$ of GaSeI and AlSeI to record lows of 0.086 and 0.089 W/mK, respectively; these values even drop to 0.058 and 0.059 W/mK at 900 K. These findings provide useful insights into exploring the thermal conductivity limit in crystals.

cond-mat.mtrl-sci↗

arXiv2025

CardAIc-Agents: A Multimodal Framework with Hierarchical Adaptation for Cardiac Care Support

Cardiovascular diseases (CVDs) remain the foremost cause of mortality worldwide, a burden worsened by a severe deficit of healthcare workers. Artificial intelligence (AI) agents have shown potential to alleviate this gap through automated detection and proactive screening, yet their clinical application remains limited by: 1) rigid sequential workflows, whereas clinical care often requires adaptive reasoning that select specific tests and, based on their results, guides personalised next steps; 2) reliance solely on intrinsic model capabilities to perform role assignment without domain-specific tool support; 3) general and static knowledge bases without continuous learning capability; and 4) fixed unimodal or bimodal inputs and lack of on-demand visual outputs when clinicians require visual clarification. In response, a multimodal framework, CardAIc-Agents, was proposed to augment models with external tools and adaptively support diverse cardiac tasks. First, a CardiacRAG agent generated task-aware plans from updatable cardiac knowledge, while the Chief agent integrated tools to autonomously execute these plans and deliver decisions. Second, to enable adaptive and case-specific customization, a stepwise update strategy was developed to dynamically refine plans based on preceding execution results, once the task was assessed as complex. Third, a multidisciplinary discussion team was proposed which was automatically invoked to interpret challenging cases, thereby supporting further adaptation. In addition, visual review panels were provided to assist validation when clinicians raised concerns. Experiments across three datasets showed the efficiency of CardAIc-Agents compared to mainstream Vision-Language Models (VLMs) and state-of-the-art agentic systems.

cs.AI↗

arXiv2025

Unique Hierarchical Rotational Dynamics Induces Ultralow Lattice Thermal Conductivity in Cyanide-bridged Framework Materials

The pursuit of materials combining light constituent elements with ultralow lattice thermal conductivity ($κ_{\mathrm{L}}$) is crucial to advancing technologies like thermoelectrics and thermal barrier coatings, yet it remains a formidable challenge to date. Herein, we achieve ultralow $κ_{\mathrm{L}}$ in lightweight cyanide-bridged framework materials (CFMs) through the rational integration of properties such as the hierarchical vibrations exhibited in superatomic structures and rotational dynamics exhibited in perovskites. Unique hierarchical rotation behavior leads to multiple negative peaks in Grüneisen parameters across a wide frequency range, thereby inducing pronounced negative thermal expansion and strong cubic anharmonicity in CFMs. Meanwhile, the synergistic effect between large four-phonon scattering phase space (induced by phonon quasi-flat bands and wide bandgaps) and strong quartic anharmonicity (associated with rotation modes) leads to giant quartic anharmonic scattering rates in these materials. Consequently, the $κ_{\mathrm{L}}$ of these CFMs decreases by one to two orders of magnitude compared to the known perovskites or perovskite-like materials with equivalent average atomic masses. For instance, the Cd(CN)$_{2}$, NaB(CN)$_{4}$, LiIn(CN)$_{4}$, and AgX(CN)$_{4}$ (X = B, Al, Ga, In) exhibit ultralow room-temperature $κ_{\mathrm{L}}$ values ranging from 0.35 to 0.81 W/mK. This work not only establishes CFMs as a novel and rich platform for studying extreme phonon anharmonicity, but also provides a new paradigm for achieving ultralow thermal conductivity in lightweight materials via the conscious integration of hierarchical and rotational dynamics.

cond-mat.mtrl-sci↗

arXiv2025

A Language-Signal-Vision Multimodal Framework for Multitask Cardiac Analysis

Contemporary cardiovascular management involves complex consideration and integration of multimodal cardiac datasets, where each modality provides distinct but complementary physiological characteristics. While the effective integration of multiple modalities could yield a holistic clinical profile that accurately models the true clinical situation with respect to data modalities and their relatives weightings, current methodologies remain limited by: 1) the scarcity of patient- and time-aligned multimodal data; 2) reliance on isolated single-modality or rigid multimodal input combinations; 3) alignment strategies that prioritize cross-modal similarity over complementarity; and 4) a narrow single-task focus. In response to these limitations, a comprehensive multimodal dataset was curated for immediate application, integrating laboratory test results, electrocardiograms, and echocardiograms with clinical outcomes. Subsequently, a unified framework, Textual Guidance Multimodal fusion for Multiple cardiac tasks (TGMM), was proposed. TGMM incorporated three key components: 1) a MedFlexFusion module designed to capture the unique and complementary characteristics of medical modalities and dynamically integrate data from diverse cardiac sources and their combinations; 2) a textual guidance module to derive task-relevant representations tailored to diverse clinical objectives, including heart disease diagnosis, risk stratification and information retrieval; and 3) a response module to produce final decisions for all these tasks. Furthermore, this study systematically explored key features across multiple modalities and elucidated their synergistic contributions in clinical decision-making. Extensive experiments showed that TGMM outperformed state-of-the-art methods across multiple clinical tasks, with additional validation confirming its robustness on another public dataset.

cs.AI↗

arXiv2025

Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification

Metaphors play a crucial role in human communication, yet their comprehension remains a significant challenge for natural language processing (NLP) due to the cognitive complexity involved. According to Conceptual Metaphor Theory (CMT), metaphors map a target domain onto a source domain, and understanding this mapping is essential for grasping the nature of metaphors. While existing NLP research has focused on tasks like metaphor detection and sentiment analysis of metaphorical expressions, there has been limited attention to the intricate process of identifying the mappings between source and target domains. Moreover, non-English multimodal metaphor resources remain largely neglected in the literature, hindering a deeper understanding of the key elements involved in metaphor interpretation. To address this gap, we developed a Chinese multimodal metaphor advertisement dataset (namely CM3D) that includes annotations of specific target and source domains. This dataset aims to foster further research into metaphor comprehension, particularly in non-English languages. Furthermore, we propose a Chain-of-Thought (CoT) Prompting-based Metaphor Mapping Identification Model (CPMMIM), which simulates the human cognitive process for identifying these mappings. Drawing inspiration from CoT reasoning and Bi-Level Optimization (BLO), we treat the task as a hierarchical identification problem, enabling more accurate and interpretable metaphor mapping. Our experimental results demonstrate the effectiveness of CPMMIM, highlighting its potential for advancing metaphor comprehension in NLP. Our dataset and code are both publicly available to encourage further advancements in this field.

cs.CL↗

arXiv2024

Development of Automated Neural Network Prediction for Echocardiographic Left ventricular Ejection Fraction

The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). In order to quantify LVEF automatically and accurately, this paper proposes a new pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey's method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline's accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson's correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p<0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. This study demonstrates that an automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluation of cardiac systolic function.

cs.CV↗

arXiv2023

Ramanujan-inspired series for $1/π$ involving harmonic numbers

By applying the derivative operator to the known identities from hypergeometric series or WZ pairs, we obtain seven series associated with harmonic numbers. Specifically, six of them are Ramanujan-like formulas for $1/π$ and the remaining onecontains harmonic numbers of order $2$. As conclusions, Sun's five conjectural series are proved.

math.NT↗

arXiv2023

Some Lucas-type congruences for q-trinomial coefficients

In this paper, we present several new $q$-congruences on the $q$-trinomial coefficients introduced by Andrews and Baxter. As a conclusion, we obtain the following congruence: \begin{align*} \bigg(\!\!\binom{ap+b}{cp+d}\!\!\bigg)\equiv\bigg(\!\!\binom{a}{c}\!\!\bigg)\bigg(\!\!\binom{b}{d}\!\!\bigg)+\bigg(\!\!\binom{a}{c+1}\!\!\bigg)\bigg(\!\!\binom{b}{d-p}\!\!\bigg)\pmod{p}, \end{align*} where $a,b,c,d$ are integers subject to $a \geq 0, 0 \leq b,d \leq p-1$, and $p$ is an odd prime. Besides, we find that the method can also be used to reprove Pan's Lucas-type congruence for the $q$-Delannoy numbers.

math.NT↗

arXiv2023

Two curious q-supercongruences and their extensions

We prove two single-parameter q-supercongruences which were recently conjectured by Guo, and establish their further extensions with one more parameter. Crucial ingredients in the proof are the terminating form of q-binomial theorem and a Karlsson-Minton type summation formula due to Gasper. Incidentally, an assertion of Wang, Li and Tang is also verified by establishing its q-analogue.

math.CO↗

arXiv2023

A Latent Fingerprint in the Wild Database

Latent fingerprints are among the most important and widely used evidence in crime scenes, digital forensics and law enforcement worldwide. Despite the number of advancements reported in recent works, we note that significant open issues such as independent benchmarking and lack of large-scale evaluation databases for improving the algorithms are inadequately addressed. The available databases are mostly of semi-public nature, lack of acquisition in the wild environment, and post-processing pipelines. Moreover, they do not represent a realistic capture scenario similar to real crime scenes, to benchmark the robustness of the algorithms. Further, existing databases for latent fingerprint recognition do not have a large number of unique subjects/fingerprint instances or do not provide ground truth/reference fingerprint images to conduct a cross-comparison against the latent. In this paper, we introduce a new wild large-scale latent fingerprint database that includes five different acquisition scenarios: reference fingerprints from (1) optical and (2) capacitive sensors, (3) smartphone fingerprints, latent fingerprints captured from (4) wall surface, (5) Ipad surface, and (6) aluminium foil surface. The new database consists of 1,318 unique fingerprint instances captured in all above mentioned settings. A total of 2,636 reference fingerprints from optical and capacitive sensors, 1,318 fingerphotos from smartphones, and 9,224 latent fingerprints from each of the 132 subjects were provided in this work. The dataset is constructed considering various age groups, equal representations of genders and backgrounds. In addition, we provide an extensive set of analysis of various subset evaluations to highlight open challenges for future directions in latent fingerprint recognition research.

cs.CV↗

arXiv2023

Some double series for $π$ and their $q$-analogues

By applying the partial derivative operator to several summation formulas for hypergeometric series, we prove several double series for $π$ in this paper. Similarly, we also establish several $q$-analogues of them.

math.CO↗

arXiv2023

Spin- and orbital-angular-momentum nonlinear optical selectivity of single-mode nanolasers

Selective control of light is essential for optical science and technology with numerous applications. Nanophotonic waveguides and integrated couplers have been developed to achieve selective coupling and spatial control of an optical beam according to its multiple degrees of freedom. However, previous coupling devices remain passive with an inherently linear response to the power of incident light limiting their maximal optical selectivity. Here, we demonstrate nonlinear optical selectivity through selective excitation of individual single-mode nanolasers based on the spin and orbital angular momentum of light. Our designed nanolaser circuits consist of plasmonic metasurfaces and individual perovskite nanowires, enabling subwavelength focusing of angular-momentum-distinctive plasmonic fields and further selective excitation of single transverse laser modes in nanowires. The optically selected nanolaser with nonlinear increase of light emission greatly enhances the baseline optical selectivity offered by the metasurface from about 0.4 up to near unity. Our demonstrated nonlinear optical selectivity may find important applications in all-optical logic gates and nanowire networks, ultrafast optical switches, nanophotonic detectors, and on-chip optical and quantum information processing.

physics.optics↗

arXiv2022

Softened sp2-sp3 bonding network leads to strong anharmonicity and weak hydrodynamics in graphene+

Graphene+, a novel carbon monolayer with sp2-sp3 hybridization, is recently reported to exhibit graphene-like Dirac properties and unprecedented out-of-plane half-auxetic behavior [Yu et al, Cell Reports Physical Science, 3 100790 (2022)]. Herein, from comprehensively state-of-the-art first-principles studies, we report the exceptional lattice thermal transport properties of graphene+ driven by the unique sp2-sp3 crystal configuration. At room temperature, the thermal conductivity of graphene+ is calculated to be ~170 W/mK, which is much lower than that of graphene (~3170 W/mK) Despite the buckling structure, weak phonon scattering phase space is trapped in graphene+. Thus, the reduction in thermal conductivity magnitude stems from soft bonding due to the unique sp2-sp3 crystal configuration. Soft bonding suppresses the vibrations of acoustic phonons, which leads to strong anharmonicity and weak phonon hydrodynamics. Further, lower group velocity, relaxation time and smaller phonon mean free path emerge in graphene+, and the significantly decreased thermal conductivity is achieved. Our study provides fundamental physical insights into the thermal transport properties of graphene+, and it serves as an ideal model to study atomic bonding versus thermal transport properties due to weak scattering phase space.

cond-mat.mtrl-sci↗

arXiv2022

A $q$-supercongruence modulo the fourth power of a cyclotomic polynomial

In this paper, a new $q$-supercongruence with two free parameters modulo the fourth power of a cyclotomic polynomial is obtained. Our main auxiliary tools are Watson's $_8ϕ_7$ transformation formula for basic hypergeometric series, the `creative microscoping' method recently introduced by Guo and Zudilin and the Chinese remainder theorem for coprime polynomials. By taking suitable parameter substitutions in the established $q$-supercongruence, some nice congruences involving the Bernoulli numbers are derived.

math.NT↗

arXiv2022

$q$-Supercongruences on triple and quadruple sums

Inspired by the recent work of El Bachraoui, we present some new $q$-supercongruences on triple and quadruple sums of basic hypergeometric series. In particular, we give a $q$-supercongruence modulo the fifth power of a cyclotomic polynomial, which is a $q$-analogue of the quadruple sum of Van Hamme's supercongruence (G.2).

math.NT↗

arXiv2022

Some new results about $q$-trinomial coefficients

In this paper, we present several new congruences on the $q$-trinomial coefficients introduced by Andrews and Baxter. A new congruence on sums of central $q$-binomial coefficients is also established.

math.NT↗

arXiv2022

New q-supercongruences from the Bailey transformation

Inspired by the recent work of Guo, we establish some new q-supercongruences including q-analogues of some Ramannujan-type supercongruences, by using the Bailey transformation formula and the `creative microscoping' method recently introduced by Guo and Zudilin.

math.NT↗

arXiv2022

Further q-analogues of the (G.2) Supercongruence of Van Hamme

In 2015, Swisher generalized the (G.2) supercongruence of Van Hamme to the modulus p^4. In this paper, we first propose two q-analogues of Swisher's supercongruence and then a new q-congruence with parameters %which including several different q-analogues of Swisher's supercongruence is present.

math.CO↗