arXiv subjects

Xiaohui Yu

Publications and source records attributed to Xiaohui Yu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Coexistence of superconductivity and sliding polar metal state in HgPSe3

The simultaneous presence of polarity and metallicity in a material signifies an exotic polar metal state, but such materials are extremely rare, especially in bulk form, due to mutually exclusive nature of the fundamental defining properties. Here, we report experimental findings that HgPSe3 is a robust bulk polar metal at room temperature with a chiral structure stabilized by pressure and, remarkably, this polar metal hosts superconductivity with critical temperature Tc up to 11 K. Theoretical analysis reveals a two-step interlayer sliding-then-compressing mechanism for coexistence of polarity and metallicity in HgPSe3. This work unveils a new paradigm for creating the bulk polar metal state and simultaneous presence of coexisting quantum orders, raising the prospect of discovering novel emergent physics using pressure as a tuning knob.

cond-mat.supr-con↗

arXiv2024

Electronic phase transitions and superconductivity in ferroelectric Sn$_2$P$_2$Se$_6$ under pressure

Since there is both strong electron-phonon coupling during a ferroelectric/FE transition and superconducting/SC transition, it has been an important topic to explore superconductivity from the FE instability. Sn$_2$P$_2$Se$_6$ arouses broad attention due to its unique FE properties. Here, we reported the electronic phase transitions and superconductivity in this compound based on high-pressure electrical transport measurement, optical absorption spectroscopy and Raman based structural analysis. Upon compression, the conductivity of Sn$_2$P$_2$Se$_6$ was elevated monotonously, an electronic phase transition occurred near 5.4 GPa, revealed by optical absorption spectroscopy, and the insulating state is estimated to be fully suppressed near 15 GPa. Then, it started to show the signature of superconductivity near 15.3 GPa. The zero-resistance state was presented from 19.4 GPa, and the superconductivity was enhanced with pressure continuously. The magnetic field effect further confirmed the SC behavior and this compound had a $T_c$ of 5.4 K at 41.8 GPa with a zero temperature upper critical field of 6.55 T. The Raman spectra confirmed the structural origin of the electronic transition near 5.4 GPa, which should due to the transition from the paraelectric phase to the incommensurate phase, and suggested a possible first-order phase transition when the sample underwent the semiconductor-metal transition near 15 GPa. This work demonstrates the versatile physical properties in ferroelectrics and inspires the further investigation on the correlation between FE instability and SC in M$_2$P$_2$X$_6$ family.

cond-mat.supr-con↗

arXiv2023

Emergence of superconductivity near 11 K by suppressing the 3-fold helical-chain structure in noncentrosymmetric HgS

The trigonal $α$-HgS has a 3-fold helical chain structure, and is in form of a noncentrosymmetric $P3_121$ phase, known as the cinnabar phase. However, under pressure, the helical chains gradually approach and connect with each other, finally reconstructing into a centrosymmetric NaCl structure at 21 GPa. Superconductivity emerges just after this helical-nonhelical structural transition. The maximum critical temperature ($T_c$) reaches 11 K at 25.4 GPa, $T_c$ decreases with further compression, and is still 3.5 K at 44.8 GPa. Furthermore, the $T_c$-critical magnetic field ($B_{c2}$) relation exhibits multi-band features, with a $B_{c2}$ of 5.65 T at 0 K by two-band fitting. Raman spectra analysis demonstrates that phonon softening plays a key role in structural transition and the emergence of superconductivity. It is noted that HgS is the first reported IIB group metal sulfide superconductor and the only NaCl-type metal sulfide superconductor with a $T_c$ above 10 K. This work will inspire the exploration of superconductivity in other chiral systems and will extend our understanding of the versatile behavior in such kinds of materials.

cond-mat.supr-con↗

arXiv2023

A Distributed Solution for Efficient K Shortest Paths Computation over Dynamic Road Networks

The problem of identifying the k-shortest paths KSPs for short in a dynamic road network is essential to many location-based services. Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Very often such services have to process numerous KSP queries over large road networks at the same time, thus there is a pressing need to identify distributed solutions for this problem. However, most existing approaches are designed to identify KSPs on a static graph in a sequential manner, restricting their scalability and applicability in a distributed setting. We therefore propose KSP-DG, a distributed algorithm for identifying k-shortest paths in a dynamic graph. It is based on partitioning the entire graph into smaller subgraphs, and reduces the problem of determining KSPs into the computation of partial KSPs in relevant subgraphs, which can execute in parallel on a cluster of servers. A distributed two-level index called DTLP is developed to facilitate the efficient identification of relevant subgraphs. A salient feature of DTLP is that it indexes a set of virtual paths that are insensitive to varying traffic conditions in an efficient and compact fashion, leading to very low maintenance cost in dynamic road networks. This is the first treatment of the problem of processing KSP queries over dynamic road networks. Extensive experiments conducted on real road networks confirm the superiority of our proposal over baseline methods.

cs.DB↗

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

Wide-band Unambiguous Quantum Sensing via Geodesic Evolution

We present a quantum sensing technique that utilizes a sequence of $π$ pulses to cyclically drive the qubit dynamics along a geodesic path of adiabatic evolution. This approach effectively suppresses the effects of both decoherence noise and control errors while simultaneously removing unwanted resonance terms, such as higher harmonics and spurious responses commonly encountered in dynamical decoupling control. As a result, our technique offers robust, wide-band, unambiguous, and high-resolution quantum sensing capabilities for signal detection and individual addressing of quantum systems, including spins. To demonstrate its versatility, we showcase successful applications of our method in both low-frequency and high-frequency sensing scenarios. The significance of this quantum sensing technique extends to the detection of complex signals and the control of intricate quantum environments. By enhancing detection accuracy and enabling precise manipulation of quantum systems, our method holds considerable promise for a variety of practical applications.

quant-ph↗

arXiv2023

Tunable magnetism and electron correlation in Titanium-based Kagome metals RETi3Bi4 (RE = Yb, Pr, and Nd) by rare-earth engineering

Rare-earth engineering is an effective way to introduce and tune the magnetism in topological Kagome magnets, which has been acting as a fertile platform to investigate the quantum interactions between geometry, topology, spin, and correlation. Here we report the structure and properties of three newly discovered Titanium-based Kagome metals RETi3Bi4 (RE = Yb, Pr, and Nd) with various magnetic states. They crystalize in the orthogonal space group Fmmm (No.69), where slightly distorted Ti Kagome lattice, RE triangular lattice, Bi honeycomb and triangular lattices stack along the a axis. By changing the rare earth atoms on RE zag-zig chains, the magnetism can be tuned from nonmagnetic YbTi3Bi4 to short-range ordered PrTi3Bi4 (Tanomaly ~ 8.2 K), and finally to ferromagnetic NdTi3Bi4 (Tc ~ 8.5 K). The measurements of resistivity and specific heat capacity demonstrate an evolution of electron correlation and density of states near the Fermi level with different rare earth atoms. In-situ resistance measurements of NdTi3Bi4 under high pressure further reveal a potential relationship between the electron correlation and ferromagnetic ordering temperature. These results highlight RETi3Bi4 as another family of topological Kagome magnets to explore nontrivial band topology and exotic phases in Kagome materials.

cond-mat.str-el↗

arXiv2022

Distributed Processing of k Shortest Path Queries over Dynamic Road Networks

The problem of identifying the k-shortest paths (KSPs for short) in a dynamic road network is essential to many location-based services. Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Very often such services have to process numerous KSP queries over large road networks at the same time, thus there is a pressing need to identify distributed solutions for this problem. However, most existing approaches are designed to identify KSPs on a static graph in a sequential manner (i.e., the (i+1)-th shortest path is generated based on the i-th shortest path), restricting their scalability and applicability in a distributed setting. We therefore propose KSP-DG, a distributed algorithm for identifying k-shortest paths in a dynamic graph. It is based on partitioning the entire graph into smaller subgraphs, and reduces the problem of determining KSPs into the computation of partial KSPs in relevant subgraphs, which can execute in parallel on a cluster of servers. A distributed two-level index called DTLP is developed to facilitate the efficient identification of relevant subgraphs. A salient feature of DTLP is that it indexes a set of virtual paths that are insensitive to varying traffic conditions, leading to very low maintenance cost in dynamic road networks. This is the first treatment of the problem of processing KSP queries over dynamic road networks. Extensive experiments conducted on real road networks confirm the superiority of our proposal over baseline methods.

cs.DB↗

arXiv2022

Positive solutions to multi-critical elliptic problems

In this paper, we investigate the existence of multiple solutions to the following multi-critical elliptic problem \begin{equation}\label{eq:0.1} \left\{\begin{aligned} -Δu & =λ|u|^{p-2}u +\sum_{i=1}^k(|x|^{-(N-α_i)}*|u|^{2^*_i})|u|^{2^*_i-2}u\quad {\rm in}\quad Ω,\\ &u\in H^1_0(Ω)\\ \end{aligned}\right. \end{equation} in connection with the topology of the bounded domain $Ω\subset \mathbb{R}^N, \,N\geq 4$, where $λ>0$, $2^*_i=\frac{N+α_i}{N-2}$ with $N-4<α_i 0$ such that if $0<λ<λ^*$ problem \eqref{eq:0.1} possesses at least $cat_Ω(Ω)$ positive solutions. We also study the existence and uniqueness of solutions for the limit problem of \eqref{eq:0.1}.

math.AP↗

arXiv2022

Physical realization of topological Roman surface by spin-induced ferroelectric polarization in cubic lattice

Topology, a mathematical concept in geometry, has become an ideal theoretical tool for describing topological states and phase transitions. Many topological concepts have found their physical entities in real or reciprocal spaces identified by topological/geometrical invariants, which are usually defined on orientable surfaces such as torus and sphere. It is natural to quest whether it is possible to find the physical realization of more intriguing non-orientable surfaces. Herein, we show that the set of spin-induced ferroelectric polarizations in cubic perovskite oxides AMn3Cr4O12 (A = La and Tb) resides on the topological Roman surface, a non-orientable two-dimensional manifold formed by sewing a Mobius strip edge to that of a disc. The induced polarization may travel in a loop along the non-orientable Mobius strip or orientable disc depending on how the spin evolves as controlled by external magnetic field. Experimentally, the periodicity of polarization can be the same or the twice of the rotating magnetic field, being well consistent with the orientability of disc and Mobius strip, respectively. This path dependent topological magnetoelectric effect presents a way to detect the global geometry of the surface and deepens our understanding of topology in both mathematics and physics

cond-mat.str-el↗

arXiv2021

Superconductivity in compressed SnPS3

Metal phosphorous trichalcogenides, MPX3, is a group of van der Waals materials with rich electronic properties and even exotic magnetic behavior. These properties can be well manipulated by pressure/strain via effective control of interlayer interaction, lattice parameters and crystal structure. Superconducting transition has been observed in compressed FePSe3. However, it is the only one superconductor reported in the large MPX3 family. Is it possible to achieve superconducting transition in other MPX3 compounds, especially in a trisulfide compound? In this work, we tentatively compressed the SnPS3 (an insulator with large band gap at ambient condition) up to 48.9 GPa, and managed to achieve the superconducting transition above 31.7 GPa with Tc ranging from ~2.2 K to ~2.8 K. The upper critical field is estimated to be ~3.03 T at 40.5 GPa. Optical absorption measurements together with Raman spectroscopy show a series of transitions under pressure, which is well consistent with the electric transport results. This work provides direct experimental evidence that SnPS3 undergoes an insulator-metal transition near 31.7 GPa. More importantly, it demonstrates that superconductivity can exist in MPS3 compounds, which not only further enriches the electronic properties of this kind of material but also paves a new avenue to explore the abundant emergence phenomena in the whole MPX3 family, and it also benefits the study of superconductor mechanism.

cond-mat.supr-con↗

arXiv2021

High-Tc superconductivity in clathrate calcium hydride CaH6

Recent discovery of superconductive rare earth/actinide superhydrides has ushered in a new era of superconductivity research at high pressures. This distinct type of clathrate metal hydrides was first proposed for alkaline-earth-metal hydride CaH6 that, however, has long eluded experimental synthesis, impeding an understanding of pertinent physics. Here, we report successful synthesis of CaH6 and its measured superconducting critical temperature Tc of 215 K at 172 GPa, which is evidenced by a sharp drop of resistivity to zero and a characteristic decrease of Tc under a magnetic field up to 9 T.An estimate based on the Werthamer-Helfand-Hohenberg model gives a giant zero-temperature upper critical magnetic field of 203 T. These remarkable benchmark superconducting properties place CaH6 among the most outstanding high-Tc superhydrides, marking it as the hitherto only clathrate metal hydride outside the family of rare earth/actinide hydrides. This exceptional case raises great prospects of expanding the extraordinary class of high-Tc superhydrides to a broader variety of compounds that possess more diverse material features and physics characteristics.

cond-mat.supr-con↗

arXiv2021

Insulator-to-superconductor transition in quasi-one-dimensional HfS3 under pressure

Various transition metal trichalcogenides (TMTC) show the charge-density-wave and superconductivity, which provide an ideal platform to study the correlation between these two orderings and the mechanism of superconductivity. Currently, almost all metallic TMTC compounds can show superconductivity either at ambient pressure or at high pressure. However, most TMTC compounds are semiconductors and even insulators. Does the superconductivity exist in any non-metal TMTC compound? In this work, we managed to manipulate the electronic behavior of highly insulating HfS3 in term of pressure. HfS3 underwent an insulator-semiconductor transition near 17 GPa with a band gap reduce of ~1 eV. The optical absorption and Raman measurement provide the consistent results, suggesting the structural origin of the electronic transition. Upon further compression, HfS3 becomes a superconductor. The superconducting transition was initialized as early as 50.6 GPa and the zero-resistance is reached above 91.2 GPa. The superconducting behavior is further confirmed by both the magnetic field effect and current effect. This work sheds the light that all TMTC may be superconductors, and opens a new avenue to explore the abundant emergence phenomena in TMTC material family.

cond-mat.supr-con↗

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↗

arXiv2021

LES3: Learning-based Exact Set Similarity Search

Set similarity search is a problem of central interest to a wide variety of applications such as data cleaning and web search. Past approaches on set similarity search utilize either heavy indexing structures, incurring large search costs or indexes that produce large candidate sets. In this paper, we design a learning-based exact set similarity search approach, LES3. Our approach first partitions sets into groups, and then utilizes a light-weight bitmap-like indexing structure, called token-group matrix (TGM), to organize groups and prune out candidates given a query set. In order to optimize pruning using the TGM, we analytically investigate the optimal partitioning strategy under certain distributional assumptions. Using these results, we then design a learning-based partitioning approach called L2P and an associated data representation encoding, PTR, to identify the partitions. We conduct extensive experiments on real and synthetic datasets to fully study LES3, establishing the effectiveness and superiority over other applicable approaches.

cs.DB↗

arXiv2021

Data Acquisition for Improving Machine Learning Models

The vast advances in Machine Learning over the last ten years have been powered by the availability of suitably prepared data for training purposes. The future of ML-enabled enterprise hinges on data. As such, there is already a vibrant market offering data annotation services to tailor sophisticated ML models. In this paper, we present research on the practical problem of obtaining data in order to improve the accuracy of ML models. We consider an environment in which consumers query for data to enhance the accuracy of their models and data providers who possess data make them available for training purposes. We first formalize this interaction process laying out the suitable framework and associated parameters for data exchange. We then propose two data acquisition strategies that consider a trade-off between exploration during which we obtain data to learn about the distribution of a provider's data and exploitation during which we optimize our data inquiries utilizing the gained knowledge. In the first strategy, Estimation and Allocation, we utilize queries to estimate the utilities of various predicates while learning about the distribution of the provider's data; then we proceed to the allocation stage in which we utilize those learned utility estimates to inform our data acquisition decisions. The second algorithmic proposal, named Sequential Predicate Selection, utilizes a sampling strategy to explore the distribution of the provider's data, adaptively investing more resources to parts of the data space that are statistically more promising to improve overall model accuracy. We present a detailed experimental evaluation of our proposals utilizing a variety of ML models and associated real data sets exploring all applicable parameters of interest. We identify trade-offs and highlight the relative benefits of each algorithm to further optimize model accuracy.

cs.DB↗

arXiv2021

Efficient Construction of Nonlinear Models over Normalized Data

Machine Learning (ML) applications are proliferating in the enterprise. Relational data which are prevalent in enterprise applications are typically normalized; as a result, data has to be denormalized via primary/foreign-key joins to be provided as input to ML algorithms. In this paper, we study the implementation of popular nonlinear ML models, Gaussian Mixture Models (GMM) and Neural Networks (NN), over normalized data addressing both cases of binary and multi-way joins over normalized relations. For the case of GMM, we show how it is possible to decompose computation in a systematic way both for binary joins and for multi-way joins to construct mixture models. We demonstrate that by factoring the computation, one can conduct the training of the models much faster compared to other applicable approaches, without any loss in accuracy. For the case of NN, we propose algorithms to train the network taking normalized data as the input. Similarly, we present algorithms that can conduct the training of the network in a factorized way and offer performance advantages. The redundancy introduced by denormalization can be exploited for certain types of activation functions. However, we demonstrate that attempting to explore this redundancy is helpful up to a certain point; exploring redundancy at higher layers of the network will always result in increased costs and is not recommended. We present the results of a thorough experimental evaluation, varying several parameters of the input relations involved and demonstrate that our proposals for the training of GMM and NN yield drastic performance improvements typically starting at 100%, which become increasingly higher as parameters of the underlying data vary, without any loss in accuracy.

cs.LG↗

arXiv2021

Pressure-enhanced interlayer exciton in WS2/MoSe2 van der Waals heterostructure

The atomic-level vdW heterostructures have been one of the most interesting quantum material systems, due to their exotic physical properties. The interlayer coupling in these systems plays a critical role to realize novel physical observation and enrich interface functionality. However, there is still lack of investigation on the tuning of interlayer coupling in a quantitative way. A prospective strategy to tune the interlayer coupling is to change the electronic structure and interlayer distance by high pressure, which is a well-established method to tune the physical properties. Here, we construct a high-quality WS2/MoSe2 heterostructure in a DAC and successfully tuned the interlayer coupling through hydrostatic pressure. Typical photoluminescence spectra of the monolayer MoSe2 (ML-MoSe2), monolayer WS2 (ML-WS2) and WS2/MoSe2 heterostructure have been observed and it's intriguing that their photoluminescence peaks shift with respect to applied pressure in a quite different way. The intralayer exciton of ML-MoSe2 and ML-WS2 show blue shift under high pressure with a coefficient of 19.8 meV/GPa and 9.3 meV/GPa, respectively, while their interlayer exciton shows relative weak pressure dependence with a coefficient of 3.4 meV/GPa. Meanwhile, external pressure helps to drive stronger interlayer interaction and results in a higher ratio of interlayer/intralayer exciton intensity, indicating the enhanced interlayer exciton behavior. The first-principles calculation reveals the stronger interlayer interaction which leads to enhanced interlayer exciton behavior in WS2/MoSe2 heterostructure under external pressure and reveals the robust peak of interlayer exciton. This work provides an effective strategy to study the interlayer interaction in vdW heterostructures, which could be of great importance for the material and device design in various similar quantum systems.

cond-mat.str-el↗