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Researcher profile

Changjian Li

Changjian Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-scicond-mat.str-elGraphicsComputer Visioncond-mat.mes-hallcond-mat.supr-conHuman-Computer InteractionMachine Learning

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Published work

6 published item(s)

preprint2026arXiv

Sketch2Arti: Sketch-based Articulation Modeling of CAD Objects

Articulation modeling aims to infer movable parts and their motion parameters for a 3D object, enabling interactive animation, simulation, and shape editing. In this paper, we present Sketch2Arti, the first sketch-based articulation modeling system for CAD objects. Our key observation is that designers naturally communicate articulation intent through lightweight sketches (e.g., arrows and strokes) that indicate how parts should move, yet translating such sketches into articulated 3D models remains largely manual. Sketch2Arti bridges this gap by enabling users to specify articulation through simple 2D sketches drawn from a chosen viewpoint. Given a CAD model and user sketches, our approach automatically discovers the corresponding movable parts and predicts their motion parameters, allowing iterative modeling of multiple articulations on complex objects with fine-grained control. Importantly, Sketch2Arti is trained in a category-agnostic manner without requiring object category information, leading to strong generalization to diverse objects beyond existing articulation datasets. Moreover, for shell models lacking interior structures, Sketch2Arti supports controllable internal completion guided by user sketches, generating plausible internal components consistent with the existing geometry and predicted motion constraints. Comprehensive experiments and user evaluations demonstrate the effectiveness, controllability, and generalization of Sketch2Arti. The code, dataset, and the prototype system are at https://arlo-yang.github.io/Sketch2Arti.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Dimensionality control and rotational symmetry breaking superconductivity in square-planar layered nickelates

The interplay between dimensionality and various phases of matter is a central inquiry in condensed matter physics. New phases are often discovered through spontaneously broken symmetry. Understanding the dimensionality of superconductivity in the high-temperature cuprate analogue $-$ layered nickelates and revealing a new symmetry-breaking state are the keys to deciphering the underlying pairing mechanism. Here, we demonstrate the highly-tunable dimensionality and a broken rotational symmetry state in the superconductivity of square-planar layered nickelates. The superconducting state, probed by superconducting critical current and magnetoresistance within superconducting transition under direction-dependent in-plane magnetic fields, exhibits a $C_2$ rotational symmetry which breaks the $C_4$ rotational symmetry of the square-planar lattice. Furthermore, by performing detailed examination of the angular dependent upper critical fields at temperatures down to 0.5 K with high-magnetic pulsed-fields, we observe a crossover from two-dimensional to three-dimensional superconducting states which can be manipulated by the ionic size fluctuations in the rare-earth spacer layer. Such a large degree of controllability is desired for tailoring strongly two/three-dimensional superconductors and navigating various pairing landscapes for a better understanding of the correlation between reduced dimensionality and unconventional pairing. These results illuminate new directions to unravel the high-temperature superconducting pairing mechanism.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Characteristic Lengths of Interlayer Charge-Transfer in Correlated Oxide Heterostructures

Using interlayer interaction to control functional heterostructures with atomic-scale designs has become one of the most effective interface-engineering strategies nowadays. Here, we demonstrate the effect of a crystalline LaFeO3 buffer layer on amorphous and crystalline LaAlO3/SrTiO3 heterostructures. The LaFeO3 buffer layer acts as an energetically favored electron acceptor in both LaAlO3/SrTiO3 systems, resulting in modulation of interfacial carrier density and hence metal-to-insulator transition. For amorphous and crystalline LaAlO3/SrTiO3 heterostructures, the metal-to-insulator transition is found when the LaFeO3 layer thickness crosses 3 and 6 unit cells, respectively. Such different critical LaFeO3 thicknesses are explained in terms of distinct characteristic lengths of the redox-reaction-mediated and polar-catastrophe-dominated charge transfer, controlled by the interfacial atomic contact and Thomas-Fermi screening effect, respectively. Our results not only shed light on the complex interlayer charge transfer across oxide heterostructures but also provides a new route to precisely tailor the charge-transfer process at a functional interface.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Sketch2CAD: Sequential CAD Modeling by Sketching in Context

We present a sketch-based CAD modeling system, where users create objects incrementally by sketching the desired shape edits, which our system automatically translates to CAD operations. Our approach is motivated by the close similarities between the steps industrial designers follow to draw 3D shapes, and the operations CAD modeling systems offer to create similar shapes. To overcome the strong ambiguity with parsing 2D sketches, we observe that in a sketching sequence, each step makes sense and can be interpreted in the \emph{context} of what has been drawn before. In our system, this context corresponds to a partial CAD model, inferred in the previous steps, which we feed along with the input sketch to a deep neural network in charge of interpreting how the model should be modified by that sketch. Our deep network architecture then recognizes the intended CAD operation and segments the sketch accordingly, such that a subsequent optimization estimates the parameters of the operation that best fit the segmented sketch strokes. Since there exists no datasets of paired sketching and CAD modeling sequences, we train our system by generating synthetic sequences of CAD operations that we render as line drawings. We present a proof of concept realization of our algorithm supporting four frequently used CAD operations. Using our system, participants are able to quickly model a large and diverse set of objects, demonstrating Sketch2CAD to be an alternate way of interacting with current CAD modeling systems.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Some Insights into Lifelong Reinforcement Learning Systems

A lifelong reinforcement learning system is a learning system that has the ability to learn through trail-and-error interaction with the environment over its lifetime. In this paper, I give some arguments to show that the traditional reinforcement learning paradigm fails to model this type of learning system. Some insights into lifelong reinforcement learning are provided, along with a simplistic prototype lifelong reinforcement learning system.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Electrical switching of perpendicular magnetization in L10 FePt single layer

Electrical manipulation of magnetization is essential for integration of magnetic functionalities such as magnetic memories and magnetic logic devices into electronic circuits. The current induced spin-orbit torque (SOT) in heavy metal/ferromagnet (HM/FM) bilayers via the spin Hall effect in the HM and/or the Rashba effect at the interfaces provides an efficient way to switch the magnetization. In the meantime, current induced SOT has also been used to switch the in-plane magnetization in single layers such as ferromagnetic semiconductor (Ga,Mn)As and antiferromagnetic metal CuMnAs with globally or locally broken inversion symmetry. Here we demonstrate the current induced perpendicular magnetization switching in L10 FePt single layer. The current induced spin-orbit effective fields in L10 FePt increase with the chemical ordering parameter (S). In 20 nm FePt films with high S, we observe a large charge-to-spin conversion efficiency and a switching current density as low as 7.0E6 A/cm2. We anticipate our findings may stimulate the exploration of the spin-orbit torques in bulk perpendicular magnetic anisotropic materials and the application of high-efficient perpendicular magnetization switching in single FM layer.

0 citations0 reviewsNo code linkedOpen access