arXiv subjects

Tao Wu

Publications and source records attributed to Tao Wu.

At least 163 records · Page 9Linked to original sources

arXiv2022

Emergent charge order and unconventional superconductivity in pressurized kagome superconductor CsV3Sb5

The discovery of multiple electronic orders in kagome superconductors AV3Sb5 (A = K, Rb, Cs) provides a promising platform for exploring unprecedented emergent physics. Under moderate pressure (< 2.2 GPa), the triple-Q charge density wave (CDW) order is monotonically suppressed by pressure, while the superconductivity displays a two-dome-like behavior, suggesting an unusual interplay between superconductivity and CDW order. Given that time-reversal symmetry breaking and electronic nematicity have been revealed inside the triple-Q CDW phase, understanding this CDW order and its interplay with superconductivity becomes one of the core questions in AV3Sb5. Here, we report the evolution of CDW and superconductivity with pressure in CsV3Sb5 by 51V nuclear magnetic resonance measurements. An emergent CDW phase, ascribed to a possible stripe-like CDW order with a unidirectional 4a0 modulation, is observed between Pc1 ~ 0.58 GPa and Pc2 ~ 2.0 GPa, which explains the two-dome-like superconducting behavior under pressure. Furthermore, the nuclear spin-lattice relaxation measurement reveals evidence for pressure-independent charge fluctuations above the CDW transition temperature and unconventional superconducting pairing above Pc2. Our results not only shed new light on the interplay of superconductivity and CDW but also reveal novel electronic correlation effects in kagome superconductors AV3Sb5.

cond-mat.supr-con↗

arXiv2022

EaaS: A Service-Oriented Edge Computing Framework Towards Distributed Intelligence

Edge computing has become a popular paradigm where services and applications are deployed at the network edge closer to the data sources. It provides applications with outstanding benefits, including reduced response latency and enhanced privacy protection. For emerging advanced applications, such as autonomous vehicles, industrial IoT, and metaverse, further research is needed. This is because such applications demand ultra-low latency, hyper-connectivity, and dynamic and reliable service provision, while existing approaches are inadequate to address the new challenges. Hence, we envision that the future edge computing is moving towards distributed intelligence, where heterogeneous edge nodes collaborate to provide services in large-scale and geo-distributed edge infrastructure. We thereby propose Edge-as-a-Service (EaaS) to enable distributed intelligence. EaaS jointly manages large-scale cross-node edge resources and facilitates edge autonomy, edge-to-edge collaboration, and resource elasticity. These features enable flexible deployment of services and ubiquitous computation and intelligence. We first give an overview of existing edge computing studies and discuss their limitations to articulate the motivation for proposing EaaS. Then, we describe the details of EaaS, including the physical architecture, proposed software framework, and benefits of EaaS. Various application scenarios, such as real-time video surveillance, smart building, and metaverse, are presented to illustrate the significance and potential of EaaS. Finally, we discuss several challenging issues of EaaS to inspire more research towards this new edge computing framework.

cs.NI↗

arXiv2022

A new class of bilayer kagome lattice compounds with Dirac nodal lines and pressure-induced superconductivity

Kagome lattice composed of transition-metal ions provides a great opportunity to explore the intertwining between geometry, electronic orders and band topology. The discovery of multiple competing orders that connect intimately with the underlying topological band structure in nonmagnetic kagome metals $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs) further pushes this topic to the quantum frontier. Here we report the discovery and characterization of a new class of vanadium-based compounds with kagome bilayers, namely $A$V$_6$Sb$_6$ ($A$ = K, Rb, Cs) and V$_6$Sb$_4$, which, together with $A$V$_3$Sb$_5$, compose a series of kagome compounds with a generic chemical formula ($A_{m-1}$Sb$_{2m}$)(V$_3$Sb)$_n$ (m = 1, 2; n = 1, 2). Theoretical calculations combined with angle-resolved photoemission measurements reveal that these compounds feature Dirac nodal lines in close vicinity to the Fermi level. Pressure-induced superconductivity in $A$V$_6$Sb$_6$ further suggests promising emergent phenomena in these materials. The establishment of a new family of layered kagome materials paves the way for designer of fascinating kagome systems with diverse topological nontrivialities and collective ground states.

cond-mat.mtrl-sci↗

arXiv2022

Photoacoustic Imaging Based on AlN MF-PMUT with Broadened Bandwidth

This paper reports an aluminum nitride (AlN) multi-frequency piezoelectric micromachined ultrasound transducers (MF-PMUT) array for photoacoustic (PA) imaging, where the broadened bandwidth is beneficial to improve imaging resolution. Specifically, PMUT based on micro-electromechanical systems (MEMS) technology is suitable for PA endoscopic imaging of blood vessels and bronchi due to its miniature size. More importantly, AlN is a non-toxic material, which makes it harmless for biomedical applications. In this work, a MF-PMUT array are designed and fabricated for PAI. The device's vibration mode impedance and bandwidth are analyzed. The MF-PMUT sensor provides a wider bandwidth (65%) signal detection, which increases the resolution of PAI compared with traditional PMUT. We conduct an experiment on agar sample to present sensor's performance in images' axial resolution.

physics.med-ph↗

arXiv2022

Interactive Image Synthesis with Panoptic Layout Generation

Interactive image synthesis from user-guided input is a challenging task when users wish to control the scene structure of a generated image with ease.Although remarkable progress has been made on layout-based image synthesis approaches, in order to get realistic fake image in interactive scene, existing methods require high-precision inputs, which probably need adjustment several times and are unfriendly to novice users. When placement of bounding boxes is subject to perturbation, layout-based models suffer from "missing regions" in the constructed semantic layouts and hence undesirable artifacts in the generated images. In this work, we propose Panoptic Layout Generative Adversarial Networks (PLGAN) to address this challenge. The PLGAN employs panoptic theory which distinguishes object categories between "stuff" with amorphous boundaries and "things" with well-defined shapes, such that stuff and instance layouts are constructed through separate branches and later fused into panoptic layouts. In particular, the stuff layouts can take amorphous shapes and fill up the missing regions left out by the instance layouts. We experimentally compare our PLGAN with state-of-the-art layout-based models on the COCO-Stuff, Visual Genome, and Landscape datasets. The advantages of PLGAN are not only visually demonstrated but quantitatively verified in terms of inception score, Fréchet inception distance, classification accuracy score, and coverage.

cs.CV↗

arXiv2022

Pressure-induced dimensional crossover in a kagome superconductor

The recently discovered kagome superconductors AV3Sb5 exhibit tantalizing high-pressure phase diagrams, in which a new dome-like superconducting phase emerges under moderate pressure. However, its origin is as yet unknown. Here, we carried out the high-pressure electrical measurements up to 150 GPa, together with the high-pressure X-ray diffraction measurements and first-principles calculations on CsV3Sb5. We find the new superconducting phase to be rather robust and inherently linked to the interlayer Sb2-Sb2 interactions. The formation of Sb2-Sb2 bonds at high pressure tunes the system from two-dimensional to three-dimensional and pushes the Pz orbital of Sb2 upward across the Fermi level, resulting in enhanced density of states and increase of TC. Our work demonstrates that the dimensional crossover at high pressure can induce a topological phase transition and is related to the abnormal high-pressure TC evolution. Our findings should apply for other layered materials.

cond-mat.supr-con↗

arXiv2021

Onsite Non-Line-of-Sight Imaging via Online Calibrations

There has been an increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects behind an obstacle. Existing solutions generally pre-calibrate the system before scanning the hidden objects. Onsite adjustments of the occluder, object and scanning pattern require re-calibration. We present an online calibration technique that directly decouples the acquired transients at onsite scanning into the LOS and hidden components. We use the former to directly (re)calibrate the system upon changes of scene/obstacle configurations, scanning regions, and scanning patterns whereas the latter for hidden object recovery via spatial, frequency or learning based techniques. Our technique avoids using auxiliary calibration apparatus such as mirrors or checkerboards and supports both laboratory validations and real-world deployments.

eess.IV↗

arXiv2021

Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

Temporal grounding aims to localize a video moment which is semantically aligned with a given natural language query. Existing methods typically apply a detection or regression pipeline on the fused representation with the research focus on designing complicated prediction heads or fusion strategies. Instead, from a perspective on temporal grounding as a metric-learning problem, we present a Mutual Matching Network (MMN), to directly model the similarity between language queries and video moments in a joint embedding space. This new metric-learning framework enables fully exploiting negative samples from two new aspects: constructing negative cross-modal pairs in a mutual matching scheme and mining negative pairs across different videos. These new negative samples could enhance the joint representation learning of two modalities via cross-modal mutual matching to maximize their mutual information. Experiments show that our MMN achieves highly competitive performance compared with the state-of-the-art methods on four video grounding benchmarks. Based on MMN, we present a winner solution for the HC-STVG challenge of the 3rd PIC workshop. This suggests that metric learning is still a promising method for temporal grounding via capturing the essential cross-modal correlation in a joint embedding space. Code is available at https://github.com/MCG-NJU/MMN.

cs.CV↗

arXiv2021

A 20-Second Cadence View of Solar-Type Stars and Their Planets with TESS: Asteroseismology of Solar Analogs and a Re-characterization of pi Men c

We present an analysis of the first 20-second cadence light curves obtained by the TESS space telescope during its extended mission. We find a precision improvement of 20-second data compared to 2-minute data for bright stars when binned to the same cadence (~10-25% better for T<~8 mag, reaching equal precision at T~13 mag), consistent with pre-flight expectations based on differences in cosmic ray mitigation algorithms. We present two results enabled by this improvement. First, we use 20-second data to detect oscillations in three solar analogs (gamma Pav, zeta Tuc and pi Men) and use asteroseismology to measure their radii, masses, densities and ages to ~1%, ~3%, ~1% and ~20% respectively, including systematic errors. Combining our asteroseismic ages with chromospheric activity measurements we find evidence that the spread in the activity-age relation is linked to stellar mass and thus convection-zone depth. Second, we combine 20-second data and published radial velocities to re-characterize pi Men c, which is now the closest transiting exoplanet for which detailed asteroseismology of the host star is possible. We show that pi Men c is located at the upper edge of the planet radius valley for its orbital period, confirming that it has likely retained a volatile atmosphere and that the "asteroseismic radius valley" remains devoid of planets. Our analysis favors a low eccentricity for pi Men c (<0.1 at 68% confidence), suggesting efficient tidal dissipation (Q/k <~ 2400) if it formed via high-eccentricity migration. Combined, these early results demonstrate the strong potential of TESS 20-second cadence data for stellar astrophysics and exoplanet science.

astro-ph.SR↗

arXiv2021

Self-adaptive Multi-task Particle Swarm Optimization

Multi-task optimization (MTO) studies how to simultaneously solve multiple optimization problems for the purpose of obtaining better performance on each problem. Over the past few years, evolutionary MTO (EMTO) was proposed to handle MTO problems via evolutionary algorithms. So far, many EMTO algorithms have been developed and demonstrated well performance on solving real-world problems. However, there remain many works to do in adapting knowledge transfer to task relatedness in EMTO. Different from the existing works, we develop a self-adaptive multi-task particle swarm optimization (SaMTPSO) through the developed knowledge transfer adaptation strategy, the focus search strategy and the knowledge incorporation strategy. In the knowledge transfer adaptation strategy, each task has a knowledge source pool that consists of all knowledge sources. Each source (task) outputs knowledge to the task. And knowledge transfer adapts to task relatedness via individuals' choice on different sources of a pool, where the chosen probabilities for different sources are computed respectively according to task's success rate in generating improved solutions via these sources. In the focus search strategy, if there is no knowledge source benefit the optimization of a task, then all knowledge sources in the task's pool are forbidden to be utilized except the task, which helps to improve the performance of the proposed algorithm. Note that the task itself is as a knowledge source of its own. In the knowledge incorporation strategy, two different forms are developed to help the SaMTPSO explore and exploit the transferred knowledge from a chosen source, each leading to a version of the SaMTPSO. Several experiments are conducted on two test suites. The results of the SaMTPSO are comparing to that of 3 popular EMTO algorithms and a particle swarm algorithm, which demonstrates the superiority of the SaMTPSO.

cs.NE↗

arXiv2021

Kagome superconductors AV$_3$Sb$_5$ (A=K, Rb, Cs)

The quasi two-dimensional (quasi-2D) kagome materials AV$_3$Sb$_5$ (A=K, Rb, Cs) were found to be a prime example of kagome superconductors, a new quantum platform to investigate the interplay between electron correlation effects, topology and geometric frustration. In this review, we report recent progress on the experimental and theoretical studies of AV$_3$Sb$_5$ and provide a broad picture of this fast-developing field in order to stimulate an expanded search for unconventional kagome superconductors. We review the electronic properties of AV$_3$Sb$_5$, the experimental measurements of the charge density wave state, evidence of time-reversal symmetry breaking, and other potential hidden symmetry breaking in these materials. A variety of theoretical proposals and models that address the nature of the time-reversal symmetry breaking are discussed. Finally, we review the superconducting properties of AV$_3$Sb$_5$, especially the potential pairing symmetries and the interplay between superconductivity and the charge density wave state.

cond-mat.supr-con↗

arXiv2021

Interfacial charge transfer and interaction in the MXene/TiO2 heterostructures

Hybrid materials of MXenes (2D carbides and nitrides) and transition-metal oxides (TMOs) have shown great promise in electrical energy storage and 2D heterostructures have been proposed as the next-generation electrode materials to expand the limits of current technology. Here we use first principles density functional theory to investigate the interfacial structure, energetics, and electronic properties of the heterostructures of MXenes (Tin+1CnT2; T=terminal groups) and anatase TiO2. We find that the greatest work-function differences are between OH-terminated-MXene (1.6 eV) and anatase TiO2(101) (6.4 eV), resulting in the largest interfacial electron transfer (~0.9 e/nm2 across the interface) from MXene to the TiO2 layer. This interface also has the strongest adhesion and further strengthened by hydrogen bond formation. For O-, F-, or mixed O-/F- terminated Tin+1Cn MXenes, electron transfer is minimal and interfacial adhesion is weak for their heterostructures with TiO2. The strong dependence of the interfacial properties of the MXene/TiO2 heterostructures on the surface chemistry of the MXenes will be useful to tune the heterostructures for electric-energy-storage applications.

cond-mat.mtrl-sci↗

arXiv2021

Analytical study on dynamic responses of sheet-pile groin subjected to transient lateral impulse

A novel structure of sheet-pile groin consists of row piles and tied sheet has been introduced to resist the impact of tidal bore in recent years. However, the dynamic responses of the structure under tidal bore have not been carefully studied before. This paper proposed an analytical solution for the dynamic responses of the sheet-pile groin subjected to a transient lateral excitation. In order to take the flexural and longitudinal vibrations into consideration, the piles and the sheet are modeled by the Timoshenko beam (TB) theory and a one-dimensional rod, respectively. The soil around the piles is simulated by the dynamic Winkler foundation model. The excitation is considered by a lateral concentrated point load acting on the front pile. On this basis, the governing equations are constructed in time domain and solved in frequency domain, while the time-domain responses are finally obtained using the discrete Inverse Fourier Transform. The accuracy of the presented analytical solution is validated by comparing with the results obtained from the FEM simulation and a model test. The effects of properties of the sheet, the piles and the soil on the dynamic responses are investigated through a comprehensive parametric study.

physics.geo-ph↗

arXiv2021

Can small-scale magnetic fields be the major cause for the near-surface effect of the solar p-mode frequencies?

Small-scale magnetic fields are not only the fundamental element of the solar magnetism, but also closely related to the structure of the solar atmosphere. The observations have shown that there is a ubiquitous tangled small-scale magnetic field with a strength of 60 $\sim$ 130\,G in the canopy forming layer of the quiet solar photosphere. On the other hand, the multi-dimensional MHD simulations show that the convective overshooting expels the magnetic field to form the magnetic canopies at a height of about 500\,km in the upper photosphere. However, the distribution of such small-scale ``canopies" in the solar photosphere cannot be rigorously constrained by either observations and numerical simulations. Based on stellar standard models, we identify that these magnetic canopies can act as a global magnetic-arch splicing layer, and find that the reflections of the solar p-mode oscillations at this magnetic-arch splicing layer results in significant improvement on the discrepancy between the observed and calculated p-mode frequencies. The location of the magnetic-arch splicing layer is determined at a height of about 630\,km, and the inferred strength of the magnetic field is about 90\,G. These features of the magnetic-arch splicing layer derived independently in the present study are quantitatively in agreement with the presence of small-scale magnetic canopies as those obtained by the observations and 3-D MHD simulations.

astro-ph.SR↗

arXiv2021

Superconducting fluctuations in overdoped Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$

Fluctuating superconductivity - vestigial Cooper pairing in the resistive state of a material - is usually associated with low dimensionality, strong disorder or low carrier density. Here, we report single particle spectroscopic, thermodynamic and magnetic evidence for persistent superconducting fluctuations in heavily hole-doped cuprate superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ ($T_c$ = 66~K) despite the high carrier density. With a sign-problem free quantum Monte Carlo calculation, we show how a partially flat band at ($π$,0) can help enhance superconducting phase fluctuations. Finally, we discuss the implications of an anisotropic band structure on the phase-coherence-limited superconductivity in overdoped cuprates and other superconductors.

cond-mat.supr-con↗

arXiv2020

Microsoft Recommenders: Tools to Accelerate Developing Recommender Systems

The purpose of this work is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. The open source repository provides python utilities to simplify common recommender-related data science work as well as example Jupyter notebooks that demonstrate use of the algorithms and tools under various environments.

cs.IR↗

arXiv2020

Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval

Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they are often insufficient when there are many previously unseen (query, item) combinations, often referred to as the cold start problem. Furthermore, the search system can be biased towards items that are frequently shown to a query previously, also known as the 'rich get richer' (a.k.a. feedback loop) problem. In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation. In this paper, we propose a new Zero-Shot Heterogeneous Transfer Learning framework that transfers learned knowledge from the recommender system component to improve the search component of a content platform. First, it learns representations of items and their natural-language features by predicting (item, item) correlation graphs derived from the recommender system as an auxiliary task. Then, the learned representations are transferred to solve the target search retrieval task, performing query-to-item prediction without having seen any (query, item) pairs in training. We conduct online and offline experiments on one of the world's largest search and recommender systems from Google, and present the results and lessons learned. We demonstrate that the proposed approach can achieve high performance on offline search retrieval tasks, and more importantly, achieved significant improvements on relevance and user interactions over the highly-optimized production system in online experiments.

cs.LG↗

arXiv2020

Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds

3D moving object detection is one of the most critical tasks in dynamic scene analysis. In this paper, we propose a novel Drosophila-inspired 3D moving object detection method using Lidar sensors. According to the theory of elementary motion detector, we have developed a motion detector based on the shallow visual neural pathway of Drosophila. This detector is sensitive to the movement of objects and can well suppress background noise. Designing neural circuits with different connection modes, the approach searches for motion areas in a coarse-to-fine fashion and extracts point clouds of each motion area to form moving object proposals. An improved 3D object detection network is then used to estimate the point clouds of each proposal and efficiently generates the 3D bounding boxes and the object categories. We evaluate the proposed approach on the widely-used KITTI benchmark, and state-of-the-art performance was obtained by using the proposed approach on the task of motion detection.

cs.CV↗