arXiv subjects

Jian Yuan

Publications and source records attributed to Jian Yuan.

At least 55 records · Page 3Linked to original sources

arXiv2023

Prediction with Incomplete Data under Agnostic Mask Distribution Shift

Data with missing values is ubiquitous in many applications. Recent years have witnessed increasing attention on prediction with only incomplete data consisting of observed features and a mask that indicates the missing pattern. Existing methods assume that the training and testing distributions are the same, which may be violated in real-world scenarios. In this paper, we consider prediction with incomplete data in the presence of distribution shift. We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i.e., mask distribution may shift agnostically between training and testing. To achieve generalization, we leverage the observation that for each mask, there is an invariant optimal predictor. To avoid the exponential explosion when learning them separately, we approximate the optimal predictors jointly using a double parameterization technique. This has the undesirable side effect of allowing the learned predictors to rely on the intra-mask correlation and that between features and mask. We perform decorrelation to minimize this effect. Combining the techniques above, we propose a novel prediction method called StableMiss. Extensive experiments on both synthetic and real-world datasets show that StableMiss is robust and outperforms state-of-the-art methods under agnostic mask distribution shift.

cs.LG↗

arXiv2023

Tunable positions of Weyl nodes via magnetism and pressure in the ferromagnetic Weyl semimetal CeAlSi

The noncentrosymmetric ferromagnetic Weyl semimetal CeAlSi with simultaneous space-inversion (SI) and time-reversal (TR) symmetry breaking provides a unique platform for the exploration of novel topological states. Here, by employing electrical and thermoelectrical transport, angle-resolved photoemission spectroscopy (ARPES), high-pressure techniques, and band calculations, we demonstrate that magnetism and pressure can serve as efficient parameters to tune the positions of Weyl nodes in CeAlSi. At ambient pressure, an anomalous Hall effect (AHE) and an anomalous Nernst effect (ANE) arise in the paramagnetic state, and then are enhanced when temperature approaches the ferromagnetic ordering temperature, evidencing magnetism facilitates the AHE/ANE. Such an enhancement of AHE/ANE can be ascribed to the tuning of the positions of Weyl nodes via magnetism. The ARPES measurements reveal that the ferromagnetism serves as a pivotal knob to tune the band structure of CeAlSi both in the bulk and on the surface. Such magnetism-tunable electronic structure has hitherto not been reported in other magnetic $R$Al$Pn$ ($R$ = rare earth elements, $Pn$ = Si, Ge) siblings, suggesting the great potential of controlling Weyl node positions in CeAlSi. Under pressure, an enhancement and a sign change of AHE are discovered. Based on band calculations, the evolution of AHE may root in the tuning of Weyl nodes via pressure. Moreover, multiple pressure-induced phase transitions are uncovered. These findings indicate that CeAlSi provides a unique and tunable platform for exploring exotic topological physics and electron correlations, as well as catering to an array of potential applications, such as spintronics and thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2023

Few-shot Image Generation with Diffusion Models

Denoising diffusion probabilistic models (DDPMs) have been proven capable of synthesizing high-quality images with remarkable diversity when trained on large amounts of data. However, to our knowledge, few-shot image generation tasks have yet to be studied with DDPM-based approaches. Modern approaches are mainly built on Generative Adversarial Networks (GANs) and adapt models pre-trained on large source domains to target domains using a few available samples. In this paper, we make the first attempt to study when do DDPMs overfit and suffer severe diversity degradation as training data become scarce. Then we fine-tune DDPMs pre-trained on large source domains to solve the overfitting problem when training data is limited. Although the directly fine-tuned models accelerate convergence and improve generation quality and diversity compared with training from scratch, they still fail to retain some diverse features and can only produce coarse images. Therefore, we design a DDPM pairwise adaptation (DDPM-PA) approach to optimize few-shot DDPM domain adaptation. DDPM-PA efficiently preserves information learned from source domains by keeping the relative pairwise distances between generated samples during adaptation. Besides, DDPM-PA enhances the learning of high-frequency details from source models and limited training data. DDPM-PA further improves generation quality and diversity and achieves results better than current state-of-the-art GAN-based approaches. We demonstrate the effectiveness of our approach on a series of few-shot image generation tasks qualitatively and quantitatively.

cs.CV↗

arXiv2023

MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs

Video generation has achieved rapid progress benefiting from high-quality renderings provided by powerful image generators. We regard the video synthesis task as generating a sequence of images sharing the same contents but varying in motions. However, most previous video synthesis frameworks based on pre-trained image generators treat content and motion generation separately, leading to unrealistic generated videos. Therefore, we design a novel framework to build the motion space, aiming to achieve content consistency and fast convergence for video generation. We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators. Firstly, we propose an image pair generator named MotionStyleGAN to generate image pairs sharing the same contents and producing various motions. Then we manage to acquire motion codes to edit one image in the generated image pairs and keep the other unchanged. The motion codes help us edit images within the motion space since the edited image shares the same contents with the other unchanged one in image pairs. Finally, we introduce a latent code generator to produce latent code sequences using motion codes for video generation. Our approach achieves state-of-the-art performance on the most complex video dataset ever used for unconditional video generation evaluation, UCF101.

cs.CV↗

arXiv2023

Record-high Mobility and Extreme Magnetoresistance on Kagome-lattice in Compensated Semimetal Ni3In2S2

The kagome-lattice crystal hosts various intriguing properties including the frustrated magnetism, charge order, topological state, superconductivity and correlated phenomena. To achieve high-performance kagome-lattice compounds for electronic and spintronic applications, careful tuning of the band structure would be desired. Here, the electronic structures of kagome-lattice crystal Ni3In2S2 were investigated by transport measurements, angle-resolved photoemission spectroscopy as well as ab initio calculations. The transport measurements reveal Ni3In2S2 as a compensated semimetal with record-high carrier mobility (~8683 cm2 V-1 S-1 and 7356 cm2 V-1 S-1 for holes and electrons) and extreme magnetoresistance (15518% at 2 K and 13 T) among kagome-lattice materials. These extraordinary properties are well explained by its band structure with indirect gap, small electron/hole pockets and large bandwidth of the 3d electrons of Ni on the kagome lattice. This work demonstrates that the crystal field and doping serve as the key tuning knobs to optimize the transport properties in kagome-lattice crystals. Our work provides material basis and optimization routes for kagome-lattice semimetals as electronics and spintronics applications.

cond-mat.mtrl-sci↗

arXiv2022

Large-Scale Integrated Flexible Tactile Sensor Array for Sensitive Smart Robotic Touch

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, the tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64x64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch), by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ~385 kPa-1 for MWCNTs concentration of 6%, while the 14% one exhibited fast response time of ~3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge or near-sensor computing with significantly reduced power consumption and latency. Our work could pave the road to building large-scale intelligent sensor networks for next-generation smart robotics.

cond-mat.mtrl-sci↗

arXiv2022

Supervised Off-Policy Ranking

Off-policy evaluation (OPE) is to evaluate a target policy with data generated by other policies. Most previous OPE methods focus on precisely estimating the true performance of a policy. We observe that in many applications, (1) the end goal of OPE is to compare two or multiple candidate policies and choose a good one, which is a much simpler task than precisely evaluating their true performance; and (2) there are usually multiple policies that have been deployed to serve users in real-world systems and thus the true performance of these policies can be known. Inspired by the two observations, in this work, we study a new problem, supervised off-policy ranking (SOPR), which aims to rank a set of target policies based on supervised learning by leveraging off-policy data and policies with known performance. We propose a method to solve SOPR, which learns a policy scoring model by minimizing a ranking loss of the training policies rather than estimating the precise policy performance. The scoring model in our method, a hierarchical Transformer based model, maps a set of state-action pairs to a score, where the state of each pair comes from the off-policy data and the action is taken by a target policy on the state in an offline manner. Extensive experiments on public datasets show that our method outperforms baseline methods in terms of rank correlation, regret value, and stability. Our code is publicly available at GitHub.

cs.LG↗

arXiv2022

Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning

Learning to coordinate is a daunting problem in multi-agent reinforcement learning (MARL). Previous works have explored it from many facets, including cognition between agents, credit assignment, communication, expert demonstration, etc. However, less attention were paid to agents' decision structure and the hierarchy of coordination. In this paper, we explore the spatiotemporal structure of agents' decisions and consider the hierarchy of coordination from the perspective of multilevel emergence dynamics, based on which a novel approach, Learning to Advise and Learning from Advice (LALA), is proposed to improve MARL. Specifically, by distinguishing the hierarchy of coordination, we propose to enhance decision coordination at meso level with an advisor and leverage a policy discriminator to advise agents' learning at micro level. The advisor learns to aggregate decision information in both spatial and temporal domains and generates coordinated decisions by employing a spatiotemporal dual graph convolutional neural network with a task-oriented objective function. Each agent learns from the advice via a policy generative adversarial learning method where a discriminator distinguishes between the policies of the agent and the advisor and boosts both of them based on its judgement. Experimental results indicate the advantage of LALA over baseline approaches in terms of both learning efficiency and coordination capability. Coordination mechanism is investigated from the perspective of multilevel emergence dynamics and mutual information point of view, which provides a novel perspective and method to analyze and improve MARL algorithms.

cs.MA↗

arXiv2022

Pressure-induced superconductivity reentrant in transition metal dichalcogenide TiSe2

Through either elements intercalation or application of pressure, transition metal dichalcogenide 1T-TiSe2 exhibits superconductivity in proximity to a charge density wave (CDW) quantum critical point (QCP), thus providing an ideal avenue to study the correlation between the two symmetry-breaking exotic quantum electronic states. We report herein that, in addition to the well-known superconducting dome that emerges within the low pressure range of 2 - 4 GPa and peaks with the maximal Tc of about 1.8 K, the pressure induces another separate superconducting transition starting around 15 GPa with a substantially higher Tc that reaches 5.6 K at about 21.5 GPa. The high-pressure X-ray diffraction and Raman spectroscopy measurements unveil that the superconductivity reentrant is caused by a first-order structural phase transition (from P-3m1 space group to Pnma space group), which is also supported by the density functional theory calculation. A comparative theoretical calculation also reveals that the conventional phonon-mediated mechanism can account for the superconductivity of 1T-TiSe2 under low pressure, while the electron-phonon coupling of 4O-TiSe2 under high pressure is too weak to induce the superconductivity with a Tc as high as 5.6 K. This implies that the emergent superconductivity in the 4O-TiSe2 may have an unconventional origin. Our finding would open a new window toward the discovery of more exotic quantum states in transition metal dichalcogenides via high pressure.

cond-mat.supr-con↗

arXiv2021

Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning

In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorithm. In canonical frameworks, distilling of such information is often done in an implicit and uninterpretable manner, or explicitly with cost functions not able to reflect the relationship between information compression and utility in representation. In this paper, we present Information-Bottleneck-based Other agents' behavior Representation learning for Multi-agent reinforcement learning (IBORM) to explicitly seek low-dimensional mapping encoder through which a compact and informative representation relevant to other agents' behaviors is established. IBORM leverages the information bottleneck principle to compress observation information, while retaining sufficient information relevant to other agents' behaviors used for cooperation decision. Empirical results have demonstrated that IBORM delivers the fastest convergence rate and the best performance of the learned policies, as compared with implicit behavior representation learning and explicit behavior representation learning without explicitly considering information compression and utility.

cs.LG↗

arXiv2021

Magnetization tunable Weyl states in EuB6

The interplay between magnetism and topological band structure offers extraordinary opportunities to realize rich exotic magnetic topological phases such as axion insulators, Weyl semimetals, and quantum anomalous Hall insulators, which therefore has attracted fast growing research interest. The rare-earth hexaborides EuB6 represents an interesting magnetic topological phase with tunable magnetizations along different crystallographic directions, while the correlation with the topological properties remains scarcely explored. In this work, combining magnetotransport measurements and first principles calculations, we demonstrate that EuB6 exhibits versatile magnetic topological phases along different crystallographic directions, which tightly correlate with the varied magnetizations. Moreover, by virtue of the weak magneto-crystalline anisotropy and the relatively strong coupling between the local magnetization and the conduction electrons, we show that the magnetic ground state of the system can be directly probed by the anisotropy in the magnetotransport properties. Our work thus introduces an excellent platform to study the rich topological phases that are tunable by magnetic orders.

cond-mat.str-el↗

arXiv2021

Weak Antilocalization Effect up to ~ 120 K in the van der Waals Crystal Fe5-xGeTe2 with Near Room Temperature Ferromagnetism

The weak antilocalization (WAL) effect is known as a quantum correction to the classical conductivity, which never appeared in two-dimensional magnets. In this work, we reported the observation of a WAL effect in the van der Waals ferromagnet Fe5-xGeTe2 with a Curie temperature Tc ~ 270 K, which can even reach as high as ~ 120 K. The WAL effect could be well described by the Hikami-Larkin-Nagaoka and Maekawa-Fukuyama theories in the presence of strong spin-orbit coupling (SOC). Moreover, A crossover from a peak to dip behavior around 60 K in both the magnetoresistance and magnetoconductance was observed, which could be ascribed to a rare example of temperature driven Lifshitz transition as indicated by the angle-resolved photoemission spectroscopy measurements and first principles calculations. The reflective magnetic circular dichroism measurements indicate a possible spin reorientation that kills the WAL effect above 120 K. Our findings present a rare example of WAL effect in two-dimensional ferromagnet and also a magnetotransport fingerprint of the strong SOC in Fe5-xGeTe2. The results would be instructive for understanding the interaction Hamiltonian for such high Tc itinerant ferromagnetism as well as be helpful for the design of next-generation room temperature spintronic or twistronic devices.

cond-mat.mtrl-sci↗

arXiv2021

Multiple Weyl fermions in the noncentrosymmetric semimetal LaAlSi

The noncentrosymmetric RAlPn (R = rare earth, Pn = Si, Ge) family, predicted to host nonmagnetic and magnetic Weyl states, provide an excellent platform for investigating the relation between magnetism and Weyl physics. By using high field magnetotransport measurements and first principles calculations, we have unveiled herein both type-I and type-II Weyl states in the nonmagnetic LaAlSi. By a careful comparison between experimental results and theoretical calculations, nontrivial Berry phases associated with the Shubnikov-de Haas oscillations are ascribed to the electron Fermi pockets related to both types of Weyl points located ~ 0.1 eV above and exactly on the Fermi level, respectively. Under high magnetic field, signatures of Zeeman splitting are also observed. These results indicate that, in addition to the importance for exploring intriguing physics of multiple Weyl fermions, LaAlSi as a comparison with magnetic Weyl semimetals in the RAlPn family would also yield valuable insights into the relation between magnetism and Weyl physics.

cond-mat.str-el↗

arXiv2020

Topological-darkness-assisted phase regulation for atomically thin meta-optics

Two-dimensional (2D) noble-metal dichalcogenides have emerged as a new platform for the realization of versatile flat optics with a considerable degree of miniaturization. However, light field manipulation at the atomic scale is widely considered unattainable since the vanishing thickness and intrinsic losses of 2D materials completely suppress both resonances and phase accumulation effects. Empowered by conventionally perceived adverse effects of intrinsic losses, we show that the structured PtSe2 films integrated with a uniform substrate can regulate nontrivial singular phase and realize atomic-thick meta-optics in the presence of topological darkness. We experimentally demonstrate a series of atomic-thick binary meta-optics that allows angle-robust and high unit-thickness diffraction efficiency of 0.96%/nm in visible frequencies, given its thickness of merely 4.3 nm. Our results unlock the potential of a new class of 2D flat optics for light field manipulation at an atomic thickness.

physics.optics↗

arXiv2019

Beam Test of the PIN-diode Readout Units with Electron Energies from 5 to 40 GeV at CERN SPS

The Chinese large-area violet-light-sensitive silicon photodiode PIN is one of the candidates of the lead tungstate crystal detector readout component of the photon spectrometer in the large heavy ion collision experiment. The PIN diode was assembled with the lead tungstate crystal and the low-noise preamplifier into a complete detector unit. The beam test was carried out on the SPS accelerator at CERN. The energy resolution was measured with the electron beam energy ranging from 5 to 40 GeV. The summation correction method was discussed, and an excellent linearity of the nominal beam energy versus the peak position of the detector was obtained, which showed the punch-through effect can be ignored.

physics.ins-det↗

arXiv2018

Equivalence of the Initialized Riemann-Liouville Derivatives and the Initialized Caputo Derivatives

Initialization of fractional differential equations remains an ongoing problem. In recent years, the initialization function approach and the infinite state approach provide two effective ways to deal with this problem. The purpose of this paper is to prove the equivalence of the initialized Riemann-Liouville derivatives and the initialized Caputo derivatives with arbitrary orders. By synthesizing the above two initialization theories, the diffusive representations of the two initialized derivatives with arbitrary orders are derived. Laplace transforms of the two initialized derivatives are shown to be equal. As a result, the two most commonly used derivatives are proved to be equivalent when initial conditions are properly imposed.

math.GM↗

arXiv2018

Hereditary effects of exponentially damped oscillators with past histories

Hereditary effects of exponentially damped oscillators with past histories are considered in this paper. Nonviscously damped oscillators involve hereditary damping forces which depend on time-histories of vibrating motions via convolution integrals over exponentially decaying functions. As a result, this kind of oscillators are said to have memory. In this work, initialization for nonviscously damped oscillators is firstly proposed. Unlike the classical viscously damped ones, information of the past history of response velocity is necessary to fully determine the dynamic behaviors of nonviscously damped oscillators. Then, initialization response of exponentially damped oscillators is obtained to characterize the hereditary effects on the dynamic response. At last, stability of initialization response is proved and the hereditary effects are shown to gradually recede with increasing of time.

math.DS↗

arXiv2018

Wafer-scale fabrication of 2D van der Waals heterojunctions for efficient and broadband photodetection

A variety of fabrication methods for van der Waals heterostructures have been demonstrated; however, their wafer-scale deposition remains a challenge. Here we report few-layer van der Waals PtS2/PtSe2 heterojunction photodiodes fabricated on a 2" SiO2/Si substrate that is only limited by the size of work chamber of the growth equipment, offering throughputs necessary for practical applications. Theoretical simulation results show that the bandgap of PtS2 is shrunk to half of its original size in the PtS2/PtSe2 heterostructures, while PtSe2 exhibits a limited response to the coupling. Both PtSe2 and PtS2 layers in the coupled system are still semiconductors. Dynamic photovoltaic switching in the heterojunctions is observed at zero-volt state under laser illuminations of 532 to 2200 nm wavelengths. The PtS2/PtSe2 photodiodes show excellent characteristics in terms of a high photoresponsivity of 361 mAW-1, an external quantum efficiency (EQE) of 84%, and a fast response speed (66 ms). The wafer-scale production of 2D photodiodes in this work accelerates the possibility of 2D materials for practical applications in the next-generation energy-efficient electronics.

cond-mat.mes-hall↗