arXiv subjects

Yu Liu

Publications and source records attributed to Yu Liu.

At least 829 records · Page 46Linked to original sources

arXiv2022

Sensitivity tests of cosmic velocity fields to massive neutrinos

We investigate impacts of massive neutrinos on the cosmic velocity fields, employing high-resolution cosmological $N$-body simulations provided by the information-optimized CUBE code, where cosmic neutrinos are evolved using collisionless hydrodynamics and their perturbations can be accurately resolved. In this study we focus, for the first time, on the analysis of massive-neutrino induced suppression effects in various cosmic velocity field components of velocity magnitude, divergence, vorticity and dispersion. By varying the neutrino mass sum $M_ν$ from 0 -- 0.4 eV, the simulations show that, the power spectra of vorticity -- exclusively sourced by non-linear structure formation that is affected by massive neutrinos significantly -- is very sensitive to the mass sum, which potentially provide novel signatures in detecting massive neutrinos. Furthermore, using the chi-square statistic, we quantitatively test the sensitivity of the density and velocity power spectra to the neutrino mass sum. Indeed, we find that, the vorticity spectrum has the highest sensitivity, and the null hypothesis of massless neutrinos is incompatible with both vorticity and divergence spectra from $M_ν=0.1$ eV at high significance ($p$-value $= 0.03$ and $0.07$, respectively). These results demonstrate clearly the importance of peculiar velocity field measurements, in particular of vorticity and divergence components, in determination of neutrino mass and mass hierarchy.

astro-ph.CO↗

arXiv2022

Mapping evolving population geography in China

China's demographic changes have important global economic and geopolitical implications. Yet, our understanding of such transitions at the micro-spatial scale remains limited due to spatial inconsistency of the census data caused by administrative boundary adjustments. To fill this gap, we manually collected and built a population census panel from 2010 to 2020 at both the county and prefectural-city levels. We show that the massive internal migration drives China's increasing population concentration and regional disparity, resulting in severe population aging in shrinking cities and increasing gender imbalance in growing cities.

econ.GN↗

arXiv2022

Polaronic Conductivity in Cr$_2$Ge$_2$Te$_6$ Single Crystals

Intrinsic, two-dimensional (2D) ferromagnetic semiconductors are an important class of materials for spin-charge conversion applications. Cr$_2$Ge$_2$Te$_6$ retains long-range magnetic order in bilayer at cryogenic temperatures and shows complex magnetic interactions with considerable magnetic anisotropy. Here, we performed a series of structural, magnetic, X-ray scattering, electronic, thermal transport and first-principles calculation studies which reveal that localized electronic charge carriers in Cr$_2$Ge$_2$Te$_6$ are dressed by surrounding lattice and are involved in polaronic transport via hopping that is sensitive on details of magnetocrystalline anisotropy. This opens possibility for manipulation of charge transport in Cr$_2$Ge$_2$Te$_6$ - based devices by electron-phonon- and spin-orbit coupling-based tailoring of polaron properties.

cond-mat.mtrl-sci↗

arXiv2022

Thermal transport and mixed valence in ZrTe$_3$ doped with Hf and Se

Two-dimensional transition metal trichalcogenides (TMTC's) feature covalently bonded metal-chalcogen layers separated by the van der Waals (vdW) gap. Similar to transition metal dichalcogenides (TMDCs), TMTCs often host charge density waves (CDWs) and superconductivity but unlike TMDCs atomic chains in the crystal structure give rise to quasi one-dimensional (quasi 1D) conduction. ZrTe$_3$ features CDW below $T_{\textrm{CDW}}$ = 63 K and filamentary superconductivity below 2 K that can be enhanced by pressure or chemical substitution. Here we report the presence of mixed valent Zr$^{2+}$ and Zr$^{4+}$ atoms in ZrTe$_3$ crystals that is reduced by doping in ZrTe$_{3-x}$Se$_x$ and Zr$_{1-y}$Hf$_y$Te$_3$. Superconductivity is enhanced via disorder in Te2-Te3 atomic chains that are associated with CDW formation. Hf substitution on Zr atomic site enhances $T_{\textrm{CDW}}$ due to unperturbed Te2-Te3 chain periodicity and enhanced electron-phonon coupling. Weak electronic correlations in ZrTe$_{3-x}$Se$_x$ are likely governed by the lattice contraction effects.

cond-mat.str-el↗

arXiv2022

Thermoelectricity and electronic correlations enhancement in FeS by slight Se substitution

We report thermoelectric studies of FeS$_{1-x}$Se$_x$ ($x$ = 0, 0.06) superconducting single crystals that feature high irreversibility fields and critical current density $J_c$ comparable to materials with much higher superconducting critical temperatures ($T_c$'s). The ratio of $T_c$ to the Fermi temperature $T_F$ is very small indicating weak electronic correlations. With a slight selenium substitution on sulfur site in FeS both $T_c$/$T_F$ and the effective mass $m^*$ rise considerably, implying increase in electronic correlation of the bulk conducting states. The first-principle calculations show rise of the density of states at the Fermi level in FeS$_{0.94}$Se$_{0.06}$ when compared to FeS which is related not only to Fe but also to chalcogen-derived electronic states.

cond-mat.supr-con↗

arXiv2022

Electrical and thermal transport in van der Waals magnets 2H-M$_x$TaS$_2$ (M = Mn, Co)

We report a detailed study of electrical and thermal transport properties in 2H-M$_x$TaS$_2$ (M = Mn, Co) magnets where M atoms are intercalated in the van der Waals gap. The intercalation induces ferromagentism with an easy-plane anisotropy in 2H-Mn$_x$TaS$_2$, but ferromagnetism with a strong uniaxial anisotropy in 2H-Co$_{0.22}$TaS$_2$, which finally evolves into a three-dimensional antiferromagnetism in 2H-Co$_{0.34}$TaS$_2$. Temperature-dependent electrical resistivity shows metallic behavior for all samples. Thermopower is negative in the whole temperature range for 2H-Co$_x$TaS$_2$, whereas the sign changes from negative to positive with increasing Mn for 2H-Mn$_x$TaS$_2$. The diffusive thermoelectric response dominates in both high- and low-temperature ranges for all samples. A clear kink in electrical resistivity, a weak anomaly in thermal conductivity, as well as a slope change in thermopower were observed at the magnetic transitions for 2H-Mn$_{0.28}$TaS$_2$ ($T_\textrm{c}$ $\approx$ 82 K) and 2H-Co$_{0.34}$TaS$_2$ ($T_\textrm{N}$ $\approx$ 36 K), respectively, albeit weaker for lower $x$ crystals. Co-intercalation promoted ferromagnetic to antiferromagnetic transition is further confirmed by the Hall resistivity; the sign change of the ordinary Hall coefficient indicates a multi-band behavior in 2H-Co$_x$TaS$_2$.

cond-mat.str-el↗

arXiv2022

Singlet-doublet transitions of a quantum dot Josephson junction detected in a transmon circuit

We realize a hybrid superconductor-semiconductor transmon device in which the Josephson effect is controlled by a gate-defined quantum dot in an InAs/Al nanowire. Microwave spectroscopy of the transmon's transition spectrum allows us to probe the ground state parity of the quantum dot as a function of gate voltages, external magnetic flux, and magnetic field applied parallel to the nanowire. The measured parity phase diagram is in agreement with that predicted by a single-impurity Anderson model with superconducting leads. Through continuous time monitoring of the circuit we furthermore resolve the quasiparticle dynamics of the quantum dot Josephson junction across the phase boundaries. Our results can facilitate the realization of semiconductor-based $0-π$ qubits and Andreev qubits.

cond-mat.mes-hall↗

arXiv2022

INTERN: A New Learning Paradigm Towards General Vision

Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society. However, down the road, a key challenge awaits us, that is, our capability of meeting rapidly-growing scenario-specific demands is severely limited by the cost of acquiring a commensurate amount of training data. This difficult situation is in essence due to limitations of the mainstream learning paradigm: we need to train a new model for each new scenario, based on a large quantity of well-annotated data and commonly from scratch. In tackling this fundamental problem, we move beyond and develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability. We evaluate our model on 26 well-known datasets that cover four categories of tasks in computer vision. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data, often by a significant margin. This is an important step towards a promising prospect where such a model with general vision capability can dramatically reduce our reliance on data, thus expediting the adoption of AI technologies. Furthermore, revolving around our new paradigm, we also introduce a new data system, a new architecture, and a new benchmark, which, together, form a general vision ecosystem to support its future development in an open and inclusive manner. See project website at https://opengvlab.shlab.org.cn .

cs.CV↗

arXiv2022

Time Division Multiplexing Ising Computer Using Single Tunable True Random Number Generator Based on Spin Torque Nano-Oscillator

Ising computer is a powerful computation scheme to deal with NP-hard optimization problems that cannot be efficiently addressed by conventional computers. A robust probabilistic bit (P-Bit) which is realized by a hardware entity fluctuating in time between -1 and 1 plays a key role in the success of Ising computer. Spintronics technology, such as stochastic nanomagnet, is recently proposed as a good platform for the hardware emulation of P-Bit. Here, we report, for the first time, a Time Division Multiplexing (TDM) Ising computer using single tunable true random number generator which is comprised of a Spin Torque Nano-Oscillator (STNO). First, the intrinsic frequency fluctuation of the STNO is utilized to design a simple digital true random number generator (TRNG). The true random number generator is further evolved into a tunable random number generator to act as a P-Bit. Second, in order to accomplish combinational optimization with our proposed P-Bit under Ising model, a novel incremental coupling rule is proposed. With such coupling rule between P-Bit array, high fidelity NOT and XOR logic gate is demonstrated. Third, it is proposed that our digital TRNG can be simply reused acting as a P-Bit array by time division multiplexing. The whole Ising computer can be implemented by one single STNO, and integer factorization of as high as 87% accuracy rate is achieved.

cond-mat.mes-hall↗

arXiv2022

Meta Knowledge Distillation

Recent studies pointed out that knowledge distillation (KD) suffers from two degradation problems, the teacher-student gap and the incompatibility with strong data augmentations, making it not applicable to training state-of-the-art models, which are trained with advanced augmentations. However, we observe that a key factor, i.e., the temperatures in the softmax functions for generating probabilities of both the teacher and student models, was mostly overlooked in previous methods. With properly tuned temperatures, such degradation problems of KD can be much mitigated. However, instead of relying on a naive grid search, which shows poor transferability, we propose Meta Knowledge Distillation (MKD) to meta-learn the distillation with learnable meta temperature parameters. The meta parameters are adaptively adjusted during training according to the gradients of the learning objective. We validate that MKD is robust to different dataset scales, different teacher/student architectures, and different types of data augmentation. With MKD, we achieve the best performance with popular ViT architectures among compared methods that use only ImageNet-1K as training data, ranging from tiny to large models. With ViT-L, we achieve 86.5% with 600 epochs of training, 0.6% better than MAE that trains for 1,650 epochs.

cs.LG↗

arXiv2022

UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning

It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. The recent advances in this research have been mainly driven by 3D convolutional neural networks and vision transformers. Although 3D convolution can efficiently aggregate local context to suppress local redundancy from a small 3D neighborhood, it lacks the capability to capture global dependency because of the limited receptive field. Alternatively, vision transformers can effectively capture long-range dependency by self-attention mechanism, while having the limitation on reducing local redundancy with blind similarity comparison among all the tokens in each layer. Based on these observations, we propose a novel Unified transFormer (UniFormer) which seamlessly integrates merits of 3D convolution and spatiotemporal self-attention in a concise transformer format, and achieves a preferable balance between computation and accuracy. Different from traditional transformers, our relation aggregator can tackle both spatiotemporal redundancy and dependency, by learning local and global token affinity respectively in shallow and deep layers. We conduct extensive experiments on the popular video benchmarks, e.g., Kinetics-400, Kinetics-600, and Something-Something V1&V2. With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1 accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than other state-of-the-art methods. For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60.9% and 71.2% top-1 accuracy respectively. Code is available at https://github.com/Sense-X/UniFormer.

cs.CV↗

arXiv2022

Topological Defects Induced High-Spin Quartet State in Truxene-Based Molecular Graphenoids

Topological defects in graphene materials introduce exotic properties which are absent in their defect-free counterparts with both fundamental importance and technological implications. Although individual topological defects have been widely studied, collective magnetic behaviors originating from well-organized multiple topological defects remain a great challenge. Here, we studied the collective magnetic properties originating from three pentagon topological defects in truxene-based molecular graphenoids by using scanning tunneling microscopy and non-contact atomic force microscopy. Unpaired $π$ electrons are introduced into the aromatic topology of truxene molecular graphenoids one by one by dissociating hydrogen atoms at the pentagon defects via atom manipulation. Scanning tunneling spectroscopy measurements together with density functional theory calculations suggest that the unpaired electrons are ferromagnetically coupled, forming a collective high-spin quartet state of S=3/2. Our work demonstrates that the collective spin ordering can be realized through engineering regular patterned topological defects in molecular graphenoids, providing a new platform for designer one-dimensional ferromagnetic spin chains and two-dimensional ferromagnetic networks.

cond-mat.mes-hall↗

arXiv2022

Ancient and recent collisions revealed by phosphate minerals in the Chelyabinsk meteorite

The collision history of asteroids is an important archive of inner Solar System evolution. Evidence for these collisions is brought to Earth by meteorites, which can preserve impact-reset radioisotope mineral ages. However, as meteorites often preserve numerous mineral ages, their interpretation is controversial. Here, we combine analysis of phosphate U-Pb ages and mineral microtextures to construct a collision history for the highly shocked Chelyabinsk meteorite. We show that phosphate U-Pb ages in the meteorite are independent of thermal history at macro-to-microscales, correlating instead with phosphate microtexture. Isotopic data from pristine phosphate domains is largely concordant, whereas fracture-damaged domains universally display discordance. Combining both populations best constrains upper (4,473 +/- 11 Ma) and lower intercept (-9 +/- 55 Ma, i.e., within error of the present day) U-Pb ages for Chelyabinsk phosphates. We conclude that all phosphate U-Pb ages were completely reset during an ancient high energy collision. Fracture-damaged phosphate domains experienced further Pb-loss during mild collisional heating in the geologically recent past, and must be targeted to properly constrain a lower intercept age. Targeting textural sub-populations of phosphate grains can significantly improve the calculation and interpretation of U-Pb ages, permitting more robust reconstruction of both ancient and recent asteroidal collision histories.

astro-ph.EP↗

arXiv2022

Two-dimensional charge order stabilized in clean polytype heterostructures

Compelling evidence suggests distinct correlated electron behavior may exist only in clean 2D materials such as 1T-TaS2. Unfortunately, experiment and theory suggest that extrinsic disorder in free standing 2D layers disrupts correlation-driven quantum behavior. Here we demonstrate a route to realizing fragile 2D quantum states through endotaxial polytype engineering of van der Waals materials. The true isolation of 2D charge density waves (CDWs) between metallic layers stabilizes commensurate long-range order and lifts the coupling between neighboring CDW layers to restore mirror symmetries via interlayer CDW twinning. The twinned-commensurate charge density wave (tC-CDW) reported herein has a single metal--insulator phase transition at ~350 K as measured structurally and electronically. Fast in-situ transmission electron microscopy and scanned nanobeam diffraction map the formation of tC-CDWs. This work introduces endotaxial polytype engineering of van der Waals materials to access latent 2D ground states distinct from conventional 2D fabrication.

cond-mat.mtrl-sci↗

arXiv2022

Ferroelectric superdomain controlled graphene plasmon for tunable mid-infrared photodetector with dual-band spectral selectivity

Dual-band infrared photodetectors (DBIPs) can discriminate desired signals from complex scenes and thus are highly expected for threat-warning, remote sensing, and astronomy applications. Conventional DBIPs with high-performances are, however, typically based on semiconductor thin films, but remain the challenges of complex spatial align, expensive growth and cooling requirement. Here, we report a tunable graphene plasmonic photodetector with dual-band infrared spectral selectivity driven by ferroelectric superdomain. The periodic ferroelectric polarization array with nanoscale ring shapes provides ultrahigh electrostatic field for spatially doping of monolayer graphene to desired patterns, and is further used to excite and confine intrinsic graphene plasmons. Our devices exhibit tunable resonance photoresponse in both two bands of 3.7-16.3 um and 15.1-52.1 um. The numerical calculations show that our devices own ultrahigh responsivities of 667-1080 A W-1 at room temperature in range of 5-50 um. Our devices make possible the applications of infrared imaging system and both stationary and motion state of objects detection. These investigations provide a novel approach for advanced infrared system construction by employing simple, low-cost, uncooling multispectral detectors array.

physics.optics↗

arXiv2021

The $f$-Divergence Reinforcement Learning Framework

The framework of deep reinforcement learning (DRL) provides a powerful and widely applicable mathematical formalization for sequential decision-making. This paper present a novel DRL framework, termed \emph{$f$-Divergence Reinforcement Learning (FRL)}. In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the $f$-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to maximize the expected cumulative rewards. We theoretically prove that minimizing such $f$-divergence can make the learning policy converge to the optimal policy. Besides, we convert the process of training agents in FRL framework to a saddle-point optimization problem with a specific $f$ function through Fenchel conjugate, which forms new methods for policy evaluation and policy improvement. Through mathematical proofs and empirical evaluation, we demonstrate that the FRL framework has two advantages: (1) policy evaluation and policy improvement processes are performed simultaneously and (2) the issues of overestimating value function are naturally alleviated. To evaluate the effectiveness of the FRL framework, we conduct experiments on Atari 2600 video games and show that agents trained in the FRL framework match or surpass the baseline DRL algorithms.

cs.LG↗

arXiv2021

Overcome Anterograde Forgetting with Cycled Memory Networks

Learning from a sequence of tasks for a lifetime is essential for an agent towards artificial general intelligence. This requires the agent to continuously learn and memorize new knowledge without interference. This paper first demonstrates a fundamental issue of lifelong learning using neural networks, named anterograde forgetting, i.e., preserving and transferring memory may inhibit the learning of new knowledge. This is attributed to the fact that the learning capacity of a neural network will be reduced as it keeps memorizing historical knowledge, and the fact that conceptual confusion may occur as it transfers irrelevant old knowledge to the current task. This work proposes a general framework named Cycled Memory Networks (CMN) to address the anterograde forgetting in neural networks for lifelong learning. The CMN consists of two individual memory networks to store short-term and long-term memories to avoid capacity shrinkage. A transfer cell is designed to connect these two memory networks, enabling knowledge transfer from the long-term memory network to the short-term memory network to mitigate the conceptual confusion, and a memory consolidation mechanism is developed to integrate short-term knowledge into the long-term memory network for knowledge accumulation. Experimental results demonstrate that the CMN can effectively address the anterograde forgetting on several task-related, task-conflict, class-incremental and cross-domain benchmarks.

cs.LG↗

arXiv2021

Optimization of RIS Configurations for Multiple-RIS-Aided mmWave Positioning Systems based on CRLB Analysis

Reconfigurable intelligent surface (RIS) is a promising technology for future millimeter-wave (mmWave) communication systems. However, its potential benefits of adopting RIS for high-precision positioning in mmWave systems are still less understood. In this paper, we study a multiple-RIS-aided mmWave positioning system and derive the Cram$\rm{\acute{e}}$r-Rao error bound. Based on the derived bound, we optimize the phase shift of the RISs by the particle swarm optimization (PSO) algorithm. Numerical results have demonstrated the advantages of using multiple RISs in enhancing the positioning accuracy in mmWave systems.

eess.SP↗