arXiv subjects

Yinghao Zhu

Publications and source records attributed to Yinghao Zhu.

At least 73 records · Page 4Linked to original sources

arXiv2024

Superconductivity in pressurized trilayer La$_4$Ni$_3$O$_{10-δ}$ single crystals

The pursuit of discovering new high-temperature superconductors that diverge from the copper-based paradigm1-3 carries profound implications for elucidating mechanisms behind superconductivity and may also enable new applications4-8. Here, our investigation reveals that application of pressure effectively suppresses the spin and charge order in trilayer nickelate La4Ni3O10-δ single crystals, leading to the emergence of superconductivity with a maximum critical temperature (Tc) of around 30 K at 69.0 GPa. The DC susceptibility measurements confirm a substantial diamagnetic response below Tc, indicating the presence of bulk superconductivity with a volume fraction exceeding 80%. In the normal state, we observe a "strange metal" behavior, characterized by a linear temperature-dependent resistance extending up to 300 K. Furthermore, the layer-dependent superconductivity observed hints at a unique interlayer coupling mechanism specific to nickelates, setting them apart from cuprates in this regard. Our findings provide crucial insights into the fundamental mechanisms underpinning superconductivity, while also introducing a new material platform to explore the intricate interplay between the spin/charge order, flat band structures, interlayer coupling, strange metal behavior and high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2024

Your Network May Need to Be Rewritten: Network Adversarial Based on High-Dimensional Function Graph Decomposition

In the past, research on a single low dimensional activation function in networks has led to internal covariate shift and gradient deviation problems. A relatively small research area is how to use function combinations to provide property completion for a single activation function application. We propose a network adversarial method to address the aforementioned challenges. This is the first method to use different activation functions in a network. Based on the existing activation functions in the current network, an adversarial function with opposite derivative image properties is constructed, and the two are alternately used as activation functions for different network layers. For complex situations, we propose a method of high-dimensional function graph decomposition(HD-FGD), which divides it into different parts and then passes through a linear layer. After integrating the inverse of the partial derivatives of each decomposed term, we obtain its adversarial function by referring to the computational rules of the decomposition process. The use of network adversarial methods or the use of HD-FGD alone can effectively replace the traditional MLP+activation function mode. Through the above methods, we have achieved a substantial improvement over standard activation functions regarding both training efficiency and predictive accuracy. The article addresses the adversarial issues associated with several prevalent activation functions, presenting alternatives that can be seamlessly integrated into existing models without any adverse effects. We will release the code as open source after the conference review process is completed.

cs.LG↗

arXiv2024

How far are AI-powered programming assistants from meeting developers' needs?

Recent In-IDE AI coding assistant tools (ACATs) like GitHub Copilot have significantly impacted developers' coding habits. While some studies have examined their effectiveness, there lacks in-depth investigation into the actual assistance process. To bridge this gap, we simulate real development scenarios encompassing three typical types of software development tasks and recruit 27 computer science students to investigate their behavior with three popular ACATs. Our goal is to comprehensively assess ACATs' effectiveness, explore characteristics of recommended code, identify reasons for modifications, and understand users' challenges and expectations. To facilitate the study, we develop an experimental platform that includes a data collection plugin for VSCode IDE and provides functions for screen recording, code evaluation, and automatic generation of personalized interview and survey questions. Through analysis of the collected data, we find that ACATs generally enhance task completion rates, reduce time, improve code quality, and increase self-perceived productivity. However, the improvement is influenced by both the nature of coding tasks and users' experience level. Notably, for experienced participants, the use of ACATs may even increase completion time. We observe that "edited line completion" is the most frequently recommended way, while "comments completion" and "string completion" have the lowest acceptance rates. The primary reasons for modifying recommended code are disparities between output formats and requirements, flawed logic, and inconsistent code styles. In terms of challenges and expectations, optimization of service access and help documentation is also concerned by participants except for functionality and performance. Our study provides valuable insights into the effectiveness and usability of ACATs, informing further improvements in their design and implementation.

cs.SE↗

arXiv2024

LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation

UNet and its variants have been widely used in medical image segmentation. However, these models, especially those based on Transformer architectures, pose challenges due to their large number of parameters and computational loads, making them unsuitable for mobile health applications. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as competitive alternatives to CNN and Transformer architectures. Building upon this, we employ Mamba as a lightweight substitute for CNN and Transformer within UNet, aiming at tackling challenges stemming from computational resource limitations in real medical settings. To this end, we introduce the Lightweight Mamba UNet (LightM-UNet) that integrates Mamba and UNet in a lightweight framework. Specifically, LightM-UNet leverages the Residual Vision Mamba Layer in a pure Mamba fashion to extract deep semantic features and model long-range spatial dependencies, with linear computational complexity. Extensive experiments conducted on two real-world 2D/3D datasets demonstrate that LightM-UNet surpasses existing state-of-the-art literature. Notably, when compared to the renowned nnU-Net, LightM-UNet achieves superior segmentation performance while drastically reducing parameter and computation costs by 116x and 21x, respectively. This highlights the potential of Mamba in facilitating model lightweighting. Our code implementation is publicly available at https://github.com/MrBlankness/LightM-UNet.

eess.IV↗

arXiv2024

Structure and magnetic properties of a La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-δ}$ single crystal

We have successfully grown large and good-quality single crystals of the La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-δ}$ compound using the floating-zone method with laser diodes. We investigated the crystal quality, crystallography, chemical composition, magnetic properties and the oxidation state of Cr in the grown single crystals by employing a combination of techniques, including X-ray Laue and powder diffraction, scanning electron microscopy, magnetization measurements, X-ray photoelectron spectroscopy and light absorption. The La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-δ}$ single crystal exhibits a single-phase composition, crystallizing in a trigonal structure with the space group $R\bar{3}c$ at room temperature. The chemical composition was determined as La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-δ}$, indicating a significant chromium deficiency. Upon warming, we observed five distinctive characteristic temperatures, namely $T_1 =$ 21.50(1) K, $T_2 =$ 34.98(1) K, $T_3 =$ 117.94(1) K, $T_4 =$ 155.01(1) K, and $T_{\textrm{N}} =$ 271.80(1) K, revealing five distinct magnetic anomalies. Our magnetization study allows us to explore the nature of these anomalies. Remarkably, the oxidation state of chromium in the single-crystal La$_{0.75}$Sr$_{0.25}$Cr$_{0.90}$O$_{3-δ}$, characterized by a band gap of 1.630(8) eV, is exclusively attributed to Cr$^{3+}$ ions, making a departure from the findings of previous studies on polycrystalline materials.

cond-mat.mtrl-sci↗

arXiv2024

Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data

The inherent complexity of structured longitudinal Electronic Health Records (EHR) data poses a significant challenge when integrated with Large Language Models (LLMs), which are traditionally tailored for natural language processing. Motivated by the urgent need for swift decision-making during new disease outbreaks, where traditional predictive models often fail due to a lack of historical data, this research investigates the adaptability of LLMs, like GPT-4, to EHR data. We particularly focus on their zero-shot capabilities, which enable them to make predictions in scenarios in which they haven't been explicitly trained. In response to the longitudinal, sparse, and knowledge-infused nature of EHR data, our prompting approach involves taking into account specific EHR characteristics such as units and reference ranges, and employing an in-context learning strategy that aligns with clinical contexts. Our comprehensive experiments on the MIMIC-IV and TJH datasets demonstrate that with our elaborately designed prompting framework, LLMs can improve prediction performance in key tasks such as mortality, length-of-stay, and 30-day readmission by about 35\%, surpassing ML models in few-shot settings. Our research underscores the potential of LLMs in enhancing clinical decision-making, especially in urgent healthcare situations like the outbreak of emerging diseases with no labeled data. The code is publicly available at https://github.com/yhzhu99/llm4healthcare for reproducibility.

cs.CL↗

arXiv2024

REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models

The integration of multimodal Electronic Health Records (EHR) data has significantly improved clinical predictive capabilities. Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG). Previous approaches with KG knowledge have primarily focused on structured knowledge extraction, neglecting unstructured data modalities and semantic high dimensional medical knowledge. In response, we propose REALM, a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR representations that address these limitations. Firstly, we apply Large Language Model (LLM) to encode long context clinical notes and GRU model to encode time-series EHR data. Secondly, we prompt LLM to extract task-relevant medical entities and match entities in professionally labeled external knowledge graph (PrimeKG) with corresponding medical knowledge. By matching and aligning with clinical standards, our framework eliminates hallucinations and ensures consistency. Lastly, we propose an adaptive multimodal fusion network to integrate extracted knowledge with multimodal EHR data. Our extensive experiments on MIMIC-III mortality and readmission tasks showcase the superior performance of our REALM framework over baselines, emphasizing the effectiveness of each module. REALM framework contributes to refining the use of multimodal EHR data in healthcare and bridging the gap with nuanced medical context essential for informed clinical predictions.

cs.AI↗

arXiv2024

A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care

The COVID-19 pandemic has posed a heavy burden to the healthcare system worldwide and caused huge social disruption and economic loss. Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data. Despite their initial success in certain clinical applications, there is currently a lack of benchmarking results to achieve a fair comparison so that we can select the optimal model for clinical use. Furthermore, there is a discrepancy between the formulation of traditional prediction tasks and real-world clinical practice in intensive care. To fill these gaps, we propose two clinical prediction tasks, Outcome-specific length-of-stay prediction and Early mortality prediction for COVID-19 patients in intensive care units. The two tasks are adapted from the naive length-of-stay and mortality prediction tasks to accommodate the clinical practice for COVID-19 patients. We propose fair, detailed, open-source data-preprocessing pipelines and evaluate 17 state-of-the-art predictive models on two tasks, including 5 machine learning models, 6 basic deep learning models and 6 deep learning predictive models specifically designed for EHR data. We provide benchmarking results using data from two real-world COVID-19 EHR datasets. One dataset is publicly available without needing any inquiry and another dataset can be accessed on request. We provide fair, reproducible benchmarking results for two tasks. We deploy all experiment results and models on an online platform. We also allow clinicians and researchers to upload their data to the platform and get quick prediction results using our trained models. We hope our efforts can further facilitate deep learning and machine learning research for COVID-19 predictive modeling.

cs.LG↗

arXiv2023

M$^3$Fair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method

In the data-driven artificial intelligence paradigm, models heavily rely on large amounts of training data. However, factors like sampling distribution imbalance can lead to issues of bias and unfairness in healthcare data. Sensitive attributes, such as race, gender, age, and medical condition, are characteristics of individuals that are commonly associated with discrimination or bias. In healthcare AI, these attributes can play a significant role in determining the quality of care that individuals receive. For example, minority groups often receive fewer procedures and poorer-quality medical care than white individuals in US. Therefore, detecting and mitigating bias in data is crucial to enhancing health equity. Bias mitigation methods include pre-processing, in-processing, and post-processing. Among them, Reweighting (RW) is a widely used pre-processing method that performs well in balancing machine learning performance and fairness performance. RW adjusts the weights for samples within each (group, label) combination, where these weights are utilized in loss functions. However, RW is limited to considering only a single sensitive attribute when mitigating bias and assumes that each sensitive attribute is equally important. This may result in potential inaccuracies when addressing intersectional bias. To address these limitations, we propose M3Fair, a multi-level and multi-sensitive-attribute reweighting method by extending the RW method to multiple sensitive attributes at multiple levels. Our experiments on real-world datasets show that the approach is effective, straightforward, and generalizable in addressing the healthcare fairness issues.

cs.LG↗

arXiv2023

Exchange renormalized crystal field excitation in a quantum Ising magnet KTmSe$_2$

Rare-earth delafossite compounds, ARCh$_2$ (A = alkali or monovalent ion, R = rare earth, Ch = chalcogen), have been proposed for a range of novel quantum phenomena. Particularly, the Tm series, ATmCh$_2$, featuring Tm ions on a triangular lattice, serves as a representative group of compounds to illustrate the interplay and competition between spin-orbit coupling, crystal fields, and exchange couplings in the presence of geometric frustration. Here we report the thermodynamic and inelastic neutron scattering studies on the newly discovered triangular-lattice magnet KTmSe$_2$. Both heat capacity and neutron diffraction reveal the absence of long-range magnetic order. Magnetic susceptibility shows strong Ising-like interactions with antiferromagnetic correlations. Furthermore, inelastic neutron scattering measurements reveal a branch of dispersive crystal field excitations. To analyze these observations, we employ both the transverse field Ising model and the full crystal field scheme, along with exchange interactions. Our results suggest a strong competition between spin exchange interactions and crystal field effects. This work is expected to offer a valuable framework for understanding low-temperature magnetism in KTmSe$_2$ and similar materials.

cond-mat.str-el↗

arXiv2023

MgF$_2$ as an effective additive for improving ionic conductivity of ceramic solid electrolytes

As typical solid-state electrolytes (SSEs), {Na}$_{1+x}${Zr}$_2${Si}$_{x}${P}$_{3-x}${O}$_{12}$ NASICONs provide an ideal platform for solid-state batteries (SSBs) that display higher safety and accommodate higher energy densities. The critical points for achieving SSBs with higher efficiencies are to improve essentially the ionic conductivity and to reduce largely the interfacial resistance between SSEs and cathode materials, which would necessitate extremely high level of craftsmanship and high-pressure equipment. An alternative to higher-performance and lower-cost SSBs is additive manufacturing. Here, we report on an effective additive, MgF$_2$, which was used in synthesizing NASICONs, resulting in SSEs with fewer defects and higher performance. With an addition of mere 1 wt$\%$ MgF$_2$ additive, the total room-temperature ionic conductivity of the NASICON electrolyte reaches up to 2.03 mS cm$^{-1}$, improved up to $\sim$ 181.3$\%$, with an activation energy of 0.277 eV. Meanwhile, the stability of the Na plating/stripping behavior in symmetric cells increases from 236 to 654 h. We tried to reveal the microscopic origins of the higher ionic conductivity of MgF$_2$-doped NASICONs by comprehensive in-house characterizations. Our study discovers a novel MgF$_2$ additive and provides an efficient way to prepare higher-performance SSEs, making it possible to fabricate lower-cost SSBs in industries.

physics.app-ph↗

arXiv2022

M$^3$Care: Learning with Missing Modalities in Multimodal Healthcare Data

Multimodal electronic health record (EHR) data are widely used in clinical applications. Conventional methods usually assume that each sample (patient) is associated with the unified observed modalities, and all modalities are available for each sample. However, missing modality caused by various clinical and social reasons is a common issue in real-world clinical scenarios. Existing methods mostly rely on solving a generative model that learns a mapping from the latent space to the original input space, which is an unstable ill-posed inverse problem. To relieve the underdetermined system, we propose a model solving a direct problem, dubbed learning with Missing Modalities in Multimodal healthcare data (M3Care). M3Care is an end-to-end model compensating the missing information of the patients with missing modalities to perform clinical analysis. Instead of generating raw missing data, M3Care imputes the task-related information of the missing modalities in the latent space by the auxiliary information from each patient's similar neighbors, measured by a task-guided modality-adaptive similarity metric, and thence conducts the clinical tasks. The task-guided modality-adaptive similarity metric utilizes the uncensored modalities of the patient and the other patients who also have the same uncensored modalities to find similar patients. Experiments on real-world datasets show that M3Care outperforms the state-of-the-art baselines. Moreover, the findings discovered by M3Care are consistent with experts and medical knowledge, demonstrating the capability and the potential of providing useful insights and explanations.

cs.LG↗

arXiv2022

A comprehensive review on the ferroelectric orthochromates: Synthesis, property, and application

Multiferroics represent a class of advanced materials for promising applications and stand at the forefront of modern science for the special feature possessing both charge polar and magnetic order. Previous studies indicate that the family of RECrO3 (RE = rare earth) compounds is likely another rare candidate system holding both ferroelectricity and magnetism. However, many issues remain unsolved, casting hot disputes about whether RECrO3 is multiferroic or not. For example, an incompatibility exists between reported structural models and observed ferroelectric behaviors, and it is not easy to determine the spin canting degree. To address these questions, one key step is to grow single crystals because they can provide more reliable information than other forms of matter do. In this review, the parent and doped ferroelectric YCrO3 compounds are comprehensively reviewed based on scientific and patent literatures from 1954 to 2022. The materials syntheses with different methods, including poly-, nano-, and single-crystalline samples and thin films, are summarized. The structural, magnetic, ferroelectric and dielectric, optical, and chemical-pressure (on Y and Cr sites by doping) dependent chemical and physical properties and the corresponding phase diagrams, are discussed. Diverse (potential) applications, including anti-corrosion, magnetohydrodynamic electrode, catalyst, negative-temperature-coefficient thermistor, magnetic refrigeration, protective coating, and solid oxide fuel cell, are present. To conclude, we summarize general results, reached consensuses, and existing controversies of the past nearly 69 years of intensive studies and highlight future research opportunities and emerging challenges to address existing issues.

cond-mat.mtrl-sci↗

arXiv2022

Temperature-dependent structure of an intermetallic ErPd$_2$Si$_2$ single crystal: A combined synchrotron and in-house X-ray diffraction study

We have grown intermetallic ErPd$_2$Si$_2$ single crystals employing laser-diodes with the floating-zone method. The temperature-dependent crystallography was determined using synchrotron and in-house X-ray powder diffraction measurements from 20 to 500 K. The diffraction patterns fit well with the tetragonal $I$4/$mmm$ space group (No. 139) with two chemical formulas within one unit cell. Our synchrotron X-ray powder diffraction study shows that the refined lattice constants are $a$ = 4.10320(2) Å, $c$ = 9.88393(5) Å at 298 K and $a$ = 4.11737(2) Å, $c$ = 9.88143(5) Å at 500 K, resulting in the unit-cell volume $V$ = 166.408(1) Å$^3$ (298 K) and 167.517(2) Å$^3$ (500 K). In the whole studied temperature range, we did not find any structural phase transition. Upon cooling, the lattice constants a and c are shortened and elongated, respectively.

cond-mat.mtrl-sci↗

arXiv2022

Crystal growth engineering and origin of the weak ferromagnetism in antiferromagnetic matrix of orthochromates from $t$-$e$ orbital hybridization

We report a combined experimental and theoretical study on intriguing magnetic properties of quasiferroelectric orthochromates. Large single crystals of the family of RECrO$_3$ (RE = Y, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu) compounds were successfully grown. Neutron Laue study indicates a good quality of the obtained single crystals. Applied magnetic-field and temperature dependent magnetization measurements reveal their intrinsic magnetic properties, especially the antiferromagnetic (AFM) transition temperatures. Density functional theory studies of the electronic structures were carried out using the Perdew-Burke-Ernzerhof functional plus Hubbard $U$ method. Crystallographic information and magnetism were theoretically optimized systematically. When RE$^{3+}$ cations vary from Y$^{3+}$ and Eu$^{3+}$ to Lu$^{3+}$ ions, the calculated $t$-$e$ orbital hybridization degree and Néel temperature behave similarly to the experimentally-determined AFM transition temperature with variation in cationic radius. We found that the $t$-$e$ hybridization is anisotropic, causing a magnetic anisotropy of Cr$^{3+}$ sublattices. This was evaluated with the nearest-neighbour $J_1$-$J_2$ model. Our research provides a picture of the electronic structures during the $t$-$e$ hybridization process while changing RE ions and sheds light on the nature of the weak ferromagnetism coexisting with predominated antiferromagnetism. The available large RECrO$_3$ single crystals build a platform for further studies of orthochromates.

cond-mat.str-el↗

arXiv2021

Insights into the structural symmetry of single-crystal YCrO$_3$ from synchrotron X-ray diffraction

We report on the crystallographic information such as lattice parameters, atomic positions, bond lengths and angles, and local crystalline distortion size and mode of single-crystal YCrO$_3$ compound by a high-resolution synchrotron X-ray diffraction study. The data was collected at 120 K (below $T_\textrm{N} \sim$ 141.5 K), 300 K (within [$T_\textrm{N}$, $T_\textrm{C}$]), and 500 K (above $T_\textrm{C} \sim$ 473 K). Taking advantages of high intensity and brilliance of synchrotron X-rays, we are able to refine collected patterns with the noncentrosymmetric monoclinic structural model ($P12_11$, No. 4) that was proposed previously but detailed structural parameters have not determined yet. Meanwhile, we calculated patterns with the centrosymmetric orthorhombic space group (\emph{Pmnb}, No. 62) for a controlled study. Lattice constants \emph{a}, \emph{b}, and \emph{c} as well as unit-cell volume almost increase linearly upon warming. We observed more dispersive distributions of bond length and angle and local distortion strength with the $P12_11$ space group. This indicates that (i) The local distortion mode of Cr2O$_6$ at 120 K correlates the formation of the canted antiferromagnetic order by Cr1-Cr2 spin interactions mainly through intermediate of O3 and O4 ions. (ii) The strain-balanced Cr1-O3(O4) and Cr2-O5(O5) bonds as well as the local distortion modes of Cr1O$_6$ and Cr2O$_6$ octohedra at 300 K may be a microscopic origin of the previously-reported dielectric anomaly. Our study demonstrates that local crystalline distortion is a key factor for the formation of ferroelectric order and provides a complete set of crystallography for a full understanding of the interesting magnetic and quasi-ferroelectric properties of YCrO$_3$ compound.

cond-mat.mtrl-sci↗

arXiv2021

Temperature-dependent structure and magnetization of YCrO$_3$ compound

We have grown a YCrO$_3$ single crystal by the floating-zone method and studied its temperature-dependent crystalline structure and magnetization by X-ray powder diffraction and PPMS DynaCool measurements. All diffraction patterns were well indexed by an orthorhombic structure with space group of $Pbnm$ (No. 62). From 36 to 300 K, no structural phase transition occurs in the pulverized YCrO$_3$ single crystal. The antiferromagnetic phase transition temperature was determined as $T_\textrm{N} =$ 141.58(5) K by the magnetization versus temperature measurements. We found weak ferromagnetic behavior in the magnetic hysteresis loops below $T_\textrm{N}$. Especially, we demonstrated that the antiferromagnetism and weak ferromagnetism appear simutaniously upon cooling. The lattice parameters ($a$, $b$, $c$, and $V$) deviate downward from the Gr$\ddot{\textrm{u}}$neisen law, displaying an anisotropic magnetostriction effect. We extracted temperature variation of the local distortion parameter $Δ$. Compared to the $Δ$ value of Cr ions, Y, O1, and O2 ions show one order of magnitude larger $Δ$ values indicative of much stronger local lattice distortions. Moreover, the calculated bond valence states of Y and O2 ions have obvious subduction charges.

cond-mat.str-el↗

arXiv2020

Disorder-induced spin-liquid-like behavior in kagome-lattice compounds

Quantum spin liquids (QSLs) are an exotic state of matter that is subject to extensive research. However, the relationship between the ubiquitous disorder and the QSL behaviors is still unclear. Here, by performing comparative experimental studies on two kagomé-lattice QSL candidates, Tm$_3$Sb$_3$Zn$_2$O$_{14}$ and Tm$_3$Sb$_3$Mg$_2$O$_{14}$, which are isostructural to each other but with strong and weak structural disorder, respectively, we show unambiguously that the disorder can induce spin-liquid-like features. In particular, both compounds show dominant antiferromagnetic interactions with a Curie-Weiss temperature of -17.4 and -28.7 K for Tm$_3$Sb$_3$Zn$_2$O$_{14}$ and Tm$_3$Sb$_3$Mg$_2$O$_{14}$, respectively, but remain disordered down to about 0.05 K. Specific heat results suggest the presence of gapless magnetic excitations characterized by a residual linear term. Magnetic excitation spectra obtained by inelastic neutron scattering (INS) at low temperatures display broad continua. All these observations are consistent with those of a QSL. However, we find in Tm$_3$Sb$_3$Zn$_2$O$_{14}$ which has strong disorder resulting from the random mixing of the magnetic Tm$^{3+}$ and nonmagnetic Zn$^{2+}$, that the low-energy magnetic excitations observed in the specific heat and INS measurements are substantially enhanced, compared to those of Tm$_3$Sb$_3$Mg$_2$O$_{14}$ which has much less disorder. We believe that the effective spins of the Tm$^{3+}$ ions in the Zn$^{2+}$/Mg$^{2+}$ sites give rise to the low-energy magnetic excitations, and the amount of the random occupancy determines the excitation strength. These results provide direct evidence of the mimicry of a QSL caused by disorder.

cond-mat.str-el↗