arXiv subjects

Junjie Zhang

Publications and source records attributed to Junjie Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2023

Design and synthesis of three-dimensional hybrid Ruddlesden-Popper nickelate single crystals

Advancement of technologies relies on discovery of new materials with emerging physical properties that are determined by their crystal structures. Ruddlesden-Popper (R-P) phases with formula of $A_{n+1}$$B_n$$X_{3n+1}$ (n=1,2,3...) are among one of the most widely studied class of materials due to their electrical, optical, magnetic, thermal properties and their combined multifunctional properties(Ref.1-6). In R-P phases, intergrowth is well-known in the short range(Ref.7-9); however, no existing compounds have been reported to have different n mixed in bulk single crystals. Here we design a hybrid R-P nickelate $La_2NiO_4$$La_3Ni_2O_7$ by alternatively stacking bilayers, which is the active structural motif in the newly discovery high-$T_c$ superconductor $La_3Ni_2O_7$ and single layers of the antiferromagnetic insulator $La_2NiO_4$. We report the successful synthesis of $La_2NiO_4$$La_3Ni_2O_7$ single crystals, and X-ray diffraction and real-space imaging vis STEM show that the crystal structure consists of single layers and bilayers of $NiO_6$ octahedral stacking alternatively perpendicular to the ab plane, characterized by the orthorhombic Immm (No.71) space group. Resistivity measurements indicate a peculiar insulator-to-metal transition around 140 K on cooling. Correlated density functional theory calculations corroborate this finding, and reveal that the single layer becomes paramagnetic metallic due to charge transfer via LaO layers. The discovery of $La_2NiO_4$$La_3Ni_2O_7$ opens a door to access a completely new family of 3D hybrid R-P phases with the formula of $A_{n+1}$$B_n$$X_{3n+1}$$A'_{m+1}$$B'_m$$X'_{3m+1}$ which potentially host a plethora of emerging physical properties for various applications.

cond-mat.mtrl-sci↗

arXiv2023

Polymorphism in Ruddlesden-Popper $La_{3}Ni_{2}O_{7}$: Discovery of a Hidden Phase with Distinctive Layer Stacking

We report the discovery of a novel form of Ruddlesden-Popper (RP) oxide, which stands as the first example of long-range, coherent polymorphism in this class of inorganic solids. Rather than the well-known, uniform stacking of perovskite blocks ubiquitously found in RP phases, this newly discovered polymorph of the bilayer RP phase $La_{3}Ni_{2}O_{7}$adopts a novel stacking sequence in which single and trilayer blocks of $NiO_{6}$ octahedra alternate in a 1313 sequence. Crystals of this new polymorph are described in space group Cmmm, although we note evidence for a competing Imcm variant. Transport measurements at ambient pressure reveal metallic character with evidence of a charge density wave transition with onset at T = 134 K, which lies intermediate between that of the standard 2222 polymorph of $La_{3}Ni_{2}O_{7}$ (space group Amam) and the trilayer RP phase, $La_{4}Ni_{3}O_{10}$. The discovery of such polymorphism could reverberate to the expansive range of science and applications that rely on RP materials, particularly the recently reported signatures of superconductivity with $T_{c}$ as high as 80 K above 14 GPa in bilayer $La_{3}Ni_{2}O_{7}$.

cond-mat.supr-con↗

arXiv2023

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems

Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies mainly focus on simulating human dialogue. Human non-verbal behaviors, such as item clicking in recommender systems, although implicitly exhibiting user preferences and could enhance the modeling of users, have not been deeply explored. The main reasons lie in the gap between language modeling and behavior modeling, as well as the incomprehension of LLMs about user-item relations. To address this issue, we propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimizes both kinds of agents together. Specifically, at each time step, we first prompt the user and item agents to interact autonomously. Then, based on the disparities between the agents' decisions and real-world interaction records, user and item agents are prompted to reflect on and adjust the misleading simulations collaboratively, thereby modeling their two-sided relations. The optimized agents can also propagate their preferences to other agents in subsequent interactions, implicitly capturing the collaborative filtering idea. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions. The results show that these agents can demonstrate personalized behaviors akin to those of real-world individuals, sparking the development of next-generation user behavior simulation.

cs.IR↗

arXiv2023

Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO

We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-agent systems: participants train teams of agents to complete a multi-task objective against opponents not seen during training. The competition combines relatively complex environment design with large numbers of agents in the environment. The top submissions demonstrate strong success on this task using mostly standard reinforcement learning (RL) methods combined with domain-specific engineering. We summarize the competition design and results and suggest that, as an academic community, competitions may be a powerful approach to solving hard problems and establishing a solid benchmark for algorithms. We will open-source our benchmark including the environment wrapper, baselines, a visualization tool, and selected policies for further research.

cs.AI↗

arXiv2023

Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning

Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, even some datasets with poor quality. Unfortunately, however, it can not be guaranteed that the generated samples from the trained dynamics model are reliable (e.g., some synthetic samples may lie outside of the support region of the static dataset). To address this issue, we propose Trajectory Truncation with Uncertainty (TATU), which adaptively truncates the synthetic trajectory if the accumulated uncertainty along the trajectory is too large. We theoretically show the performance bound of TATU to justify its benefits. To empirically show the advantages of TATU, we first combine it with two classical model-based offline RL algorithms, MOPO and COMBO. Furthermore, we integrate TATU with several off-the-shelf model-free offline RL algorithms, e.g., BCQ. Experimental results on the D4RL benchmark show that TATU significantly improves their performance, often by a large margin. Code is available here.

cs.LG↗

arXiv2023

Large effective magnetic fields from chiral phonons in rare-earth halides

Time-reversal symmetry (TRS) is pivotal for materials optical, magnetic, topological, and transport properties. Chiral phonons, characterized by atoms rotating unidirectionally around their equilibrium positions, generate dynamic lattice structures that break TRS. Here we report that coherent chiral phonons, driven by circularly polarized terahertz light pulses, can polarize the paramagnetic spins in CeF3 like a quasi-static magnetic field on the order of 1 Tesla. Through time-resolved Faraday rotation and Kerr ellipticity, we found the transient magnetization is only excited by pulses resonant with phonons, proportional to the angular momentum of the phonons, and growing with magnetic susceptibility at cryogenic temperatures, as expected from the spin-phonon coupling model. The time-dependent effective magnetic field quantitatively agrees with that calculated from phonon dynamics. Our results may open a new route to directly investigate mode-specific spin-phonon interaction in ultrafast magnetism, energy-efficient spintronics, and non-equilibrium phases of matter with broken TRS.

cond-mat.mtrl-sci↗

arXiv2023

Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effective recommendation models. Basically speaking, these models mainly learn the underlying user preference from historical behavior data, and then estimate the user-item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs. The key idea is that the preferences or needs of a user can be expressed in natural language descriptions (called instructions), so that LLMs can understand and further execute the instruction for fulfilling the recommendation task. Instead of using public APIs of LLMs, we instruction tune an open-source LLM (3B Flan-T5-XL), in order to better adapt LLMs to recommender systems. For this purpose, we first design a general instruction format for describing the preference, intention, task form and context of a user in natural language. Then we manually design 39 instruction templates and automatically generate a large amount of user-personalized instruction data (252K instructions) with varying types of preferences and intentions. To demonstrate the effectiveness of our approach, we instantiate the instruction templates into several widely-studied recommendation (or search) tasks, and conduct extensive experiments on these tasks with real-world datasets. Experiment results show that the proposed approach can outperform several competitive baselines, including the powerful GPT-3.5, on these evaluation tasks. Our approach sheds light on developing more user-friendly recommender systems, in which users can freely communicate with the system and obtain more accurate recommendations via natural language instructions.

cs.IR↗

arXiv2023

GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection

Along with the rapid evolution of mobile communication technologies, such as 5G, there has been a drastically increase in telecom fraud, which significantly dissipates individual fortune and social wealth. In recent years, graph mining techniques are gradually becoming a mainstream solution for detecting telecom fraud. However, the graph imbalance problem, caused by the Pareto principle, brings severe challenges to graph data mining. This is a new and challenging problem, but little previous work has been noticed. In this paper, we propose a Graph ATtention network with COst-sensitive BOosting (GAT-COBO) for the graph imbalance problem. First, we design a GAT-based base classifier to learn the embeddings of all nodes in the graph. Then, we feed the embeddings into a well-designed cost-sensitive learner for imbalanced learning. Next, we update the weights according to the misclassification cost to make the model focus more on the minority class. Finally, we sum the node embeddings obtained by multiple cost-sensitive learners to obtain a comprehensive node representation, which is used for the downstream anomaly detection task. Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors. In addition, our model is also helpful for solving the widespread over-smoothing problem in GNNs. The GAT-COBO code and datasets are available at https://github.com/xxhu94/GAT-COBO.

cs.LG↗

arXiv2023

Electronic character of charge order in square planar low valence nickelates

Charge order is a central feature of the physics of cuprate superconductors and is known to arise from a modulation of holes with primarily oxygen character. Low-valence nickelate superconductors also host charge order, but the electronic character of this symmetry breaking is unsettled. Here, using resonant inelastic x-ray scattering at the Ni $L_2$-edge, we identify intertwined involvements of Ni $3d_{x^2-y^2}$, $3d_{3z^2-r^2}$, and O $2p_σ$ orbitals in the formation of diagonal charge order in an overdoped low-valence nickelate La$_{4}$Ni$_{3}$O$_{8}$. The Ni $3d_{x^2-y^2}$ orbitals, strongly hybridized with planar O $2p_σ$, largely shape the spatial charge distribution and lead to Ni site-centered charge order. The $3d_{3z^2-r^2}$ orbitals play a small, but non-negligible role in the charge order as they hybridize with the rare-earth $5d$ orbitals. Our results reveal that the low-energy physics and ground-state character of these nickelates are more complex than those in cuprates.

cond-mat.str-el↗

arXiv2022

Recent Advances in RecBole: Extensions with more Practical Considerations

RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update requests. This motivates us to make some significant improvements on our library, so as to meet the user requirements and contribute to the research community. In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole. In general, we focus on the flexibility and efficiency of RecBole in the past few months. More specifically, we have four development targets: (1) more flexible data processing, (2) more efficient model training, (3) more reproducible configurations, and (4) more comprehensive user documentation. Readers can download the above updates at: https://github.com/RUCAIBox/RecBole.

cs.IR↗

arXiv2022

Bootstrapped Dimensional Crossover of a Spin Density Wave

Quantum materials display rich and myriad types of magnetic, electronic, and structural ordering, often with these ordering modes either competing with one another or 'intertwining', that is, reinforcing one another. Low dimensional quantum materials, influenced strongly by competing interactions and/or geometric frustration, are particularly susceptible to such ordering phenomena and thus offer fertile ground for understanding the consequent emergent collective quantum phenomena. Such is the case of the quasi-2D materials R4Ni3O10(R=La, Pr), in which intertwined charge-and spin-density waves (CDW and SDW) on the Ni sublattice have been identified and characterized. Not unexpectedly, these density waves are largely 2D as a result of weak coupling between planes, compounded with magnetic frustration. In the case of R=Pr, however, we show here that exchange coupling between the transition metal and rare earth sublattices upon cooling overcomes both obstacles, leading to a dimensional crossover into a fully 3D ordered and coupled SDW state on both sublattices, as an induced moment on notionally nonmagnetic Pr3+ opens exchange pathways in the third dimension. In the process, the structure of the SDW on the Ni sublattice is irreversibly altered, an effect that survives reheating of the material until the underlying CDW melts. This 'bootstrapping' mechanism linking incommensurate SDWs on the two sublattices illustrates a new member of the multitude of quantum states that low-dimensional magnets can express, driven by coupled orders and modulated by frustrated exchange pathways.

cond-mat.str-el↗

arXiv2022

Electronic structure and correlations in planar trilayer nickelate Pr4Ni3O8

The recent discovery of superconductivity in hole-doped planar nickelates R1-xSrNiO2 (R=Pr,Nd) raises the foundational question of how the electronic structure and electronic correlations of these Ni1+ compounds compare to those of the Cu2+ cuprate superconductors. Here, we present an Angle-Resolved Photoemission Spectroscopy (ARPES) study of the trilayer nickelate Pr4Ni3O8, revealing an electronic structure and Fermi surface very similar to that of the hole-doped cuprates but with a few critical differences. Specifically, the main portions of the Fermi surface are extremely similar to that of the bilayer cuprates, with an additional piece that can accommodate additional hole doping. We find that the electronic correlations are about twice as strong in the nickelates and are almost k-independent, indicating that they originate from a local effect-likely the Mott interaction, whereas the cuprate interactions are somewhat less local. Nevertheless, the nickelates still demonstrate an approximately linear in energy and linear in temperature scattering rate. Understanding the similarities and differences between these two related families of strongly-correlated novel superconductors is an important challenge.

cond-mat.supr-con↗

arXiv2022

Centimeter-level Positioning by Instantaneous Lidar-aided GNSS Ambiguity Resolution

High-precision vehicle positioning is key to the implementation of modern driving systems in urban environments. Global Navigation Satellite System (GNSS) carrier phase measurements can provide millimeter- to centimeter-level positioning, provided that the integer ambiguities are correctly resolved. Abundant code measurements are often used to facilitate integer ambiguity resolution (IAR), however, they suffer from signal blockage and multipath in urban canyons. In this contribution, a lidar-aided instantaneous ambiguity resolution method is proposed. Lidar measurements, in the form of 3D keypoints, are generated by a learning-based point cloud registration method using a pre-built HD map and integrated with GNSS observations in a mixed measurement model to produce precise float solutions, which in turn increase the ambiguity success rate. Closed-form expressions of the ambiguity variance matrix and the associated Ambiguity Dilution of Precision (ADOP) are developed to provide a priori evaluation of such lidar-aided ambiguity resolution performance. Both analytical and experimental results show that the proposed method enables successful instantaneous IAR with limited GNSS satellites and frequencies, leading to centimeter-level vehicle positioning.

cs.RO↗

arXiv2022

Role of Oxygen States in the Low Valence Nickelate La$_4$Ni$_3$O$_8$

The discovery of superconductivity in square-planar low valence nickelates has ignited a vigorous debate regarding their essential electronic properties: Do these materials have appreciable oxygen charge-transfer character akin to the cuprates, or are they in a distinct Mott-Hubbard regime where oxygen plays a minimal role? Here, we resolve this question using O $K$-edge resonant inelastic x-ray scattering (RIXS) measurements of the low valence nickelate La$_{4}$Ni$_{3}$O$_{8}$ and a prototypical cuprate La$_{2-x}$Sr$_{x}$CuO$_{4}$ ($x=0.35$). As expected, the cuprate lies deep in the charge-transfer regime of the Zaanen-Sawatzky-Allen scheme. The nickelate, however, is not well described by either limit of the ZSA scheme and is found to be of mixed charge-transfer/Mott-Hubbard character with the Coulomb repulsion $U$ of similar size to the charge-transfer energy $Δ$. Nevertheless, the transition-metal-oxygen hopping is larger in La$_{4}$Ni$_{3}$O$_{8}$ than in La$_{2-x}$Sr$_{x}$CuO$_{4}$, leading to a significant superexchange interaction and an appreciable hole occupation of the ligand O orbitals in La$_{4}$Ni$_{3}$O$_{8}$ despite its larger $Δ$. Our results clarify the essential characteristics of low valence nickelates and put strong constraints on theoretical interpretations of superconductivity in these materials.

cond-mat.supr-con↗

arXiv2021

Understanding and Designing the Spin-Driven Thermoelectrics

While the thermoelectric materials progress based on the engineering of electronic and phononic characteristics is reaching a plateau, adding the spin degree of freedom has the potential to open a new landscape for alternative thermoelectric materials. Here we present the concepts, current understanding, and guidelines for designing spin-driven thermoelectrics. We show that the interplay between the spin and heat currents in entropy transport via charge carriers can offer a strategic path to enhance the electronic thermopower. The classical antiferromagnetic semiconductor manganese telluride (MnTe) is chosen as the case study due to its significant spin-mediated thermoelectric properties. We show that although the spin-disorder scattering reduces the carrier mobility in magnetic materials, spin entropy, magnon, and paramagnon carrier drags can dominate over and significantly enhance the thermoelectric power factor and hence zT. Finally, several guidelines are drawn based on the current understandings for designing high-performance spin-driven thermoelectric materials.

cond-mat.mtrl-sci↗

arXiv2021

PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning

The predicament in semi-supervised few-shot learning (SSFSL) is to maximize the value of the extra unlabeled data to boost the few-shot learner. In this paper, we propose a Poisson Transfer Network (PTN) to mine the unlabeled information for SSFSL from two aspects. First, the Poisson Merriman Bence Osher (MBO) model builds a bridge for the communications between labeled and unlabeled examples. This model serves as a more stable and informative classifier than traditional graph-based SSFSL methods in the message-passing process of the labels. Second, the extra unlabeled samples are employed to transfer the knowledge from base classes to novel classes through contrastive learning. Specifically, we force the augmented positive pairs close while push the negative ones distant. Our contrastive transfer scheme implicitly learns the novel-class embeddings to alleviate the over-fitting problem on the few labeled data. Thus, we can mitigate the degeneration of embedding generality in novel classes. Extensive experiments indicate that PTN outperforms the state-of-the-art few-shot and SSFSL models on miniImageNet and tieredImageNet benchmark datasets.

cs.CV↗

arXiv2021

Review on quasi-2D square planar nickelates

In strongly correlated materials, lattice, charge, spin and orbital degrees of freedom interact with each other, leading to emergent physical properties such as superconductivity, colossal magnetic resistance and metal-insulator transition. Quasi-2D square planar nickelates, Rn+1NinO2n+2 (R=rare earth, n=2, 3...), are of significant interest and long sought for cuprate analogue due to the 3d9 electronic configuration of Ni+, the same as the active ion Cu2+ in the high-Tc superconducting cuprates. The field has attracted intense attention since 2019 due to the discovery of superconductivity in thin films of Nd0.8Sr0.2NiO2, although no superconductivity has been reported in bulk polycrystalline powders. Herein, we review the synthesis of polycrystalline powders of quasi-2D square planar nickelates through topotactic reduction of parent compounds that are synthesized via solid state reaction, precursor method, high pressure floating zone method and high-pressure flux method. We emphasize single crystal preparation using the high-pressure floating zone techniques. We discuss their crystal structure and physical properties including resistivity, magnetic susceptibility and heat capacity. We highlight the cuprate-like physics, including charge/spin stripes and large orbital polarization, identified in single crystals of R4Ni3O8 (R=La and Pr) combining synchrotron X-ray/neutron single crystal diffraction and density functional theory calculations. Furthermore, the challenges and possible research directions of this fast-moving field in the future are briefly discussed.

cond-mat.supr-con↗

arXiv2021

TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples

The challenges of high intra-class variance yet low inter-class fluctuations in fine-grained visual categorization are more severe with few labeled samples, \textit{i.e.,} Fine-Grained categorization problems under the Few-Shot setting (FGFS). High-order features are usually developed to uncover subtle differences between sub-categories in FGFS, but they are less effective in handling the high intra-class variance. In this paper, we propose a Target-Oriented Alignment Network (TOAN) to investigate the fine-grained relation between the target query image and support classes. The feature of each support image is transformed to match the query ones in the embedding feature space, which reduces the disparity explicitly within each category. Moreover, different from existing FGFS approaches devise the high-order features over the global image with less explicit consideration of discriminative parts, we generate discriminative fine-grained features by integrating compositional concept representations to global second-order pooling. Extensive experiments are conducted on four fine-grained benchmarks to demonstrate the effectiveness of TOAN compared with the state-of-the-art models.

cs.CV↗