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Ferroelectricity is characterized by the presence of spontaneous and switchable macroscopic polarization. Scaling limits of ferroelectricity have been of both fundamental and technological importance, but the probes of ferroelectricity have often been indirect due to confounding factors such as leakage in the direct electrical measurements. Recent interest in low-voltage switching electronic devices squarely puts the focus on ultrathin limits of ferroelectricity in an electronic device form, specifically on the robustness of ferroelectric characteristics such as retention and endurance for practical applications. Here, we illustrate how manipulating the kinetic energy of the plasma plume during pulsed laser deposition can yield ultrathin ferroelectric capacitor heterostructures with high bulk and interface quality, significantly low leakage currents and a broad "growth window". These heterostructures venture into previously unexplored aspects of ferroelectric properties, showcasing ultralow switching voltages ($<$0.3 V), long retention times ($>$10$^{4}$s), and high endurance ($>$10$^{11}$cycles) in 20 nm films of the prototypical perovskite ferroelectric, BaTiO$_{3}$. Our work demonstrates that materials engineering can push the envelope of performance for ferroelectric materials and devices at the ultrathin limit and opens a direct, reliable and scalable pathway to practical applications of ferroelectrics in ultralow voltage switches for logic and memory technologies.

Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose differences, or leverage global shapes, which leads to inconsistency when facing distribution variances such as partial overlapping. Combining the advantages of both types of methods, we adopt a coarse-to-fine pipeline that concurrently handles both issues. We first reduce the pose differences between input point clouds by aligning global features; then we match the local features to further refine the inaccurate alignments resulting from distribution variances. As global feature alignment requires the features to preserve the poses of input point clouds and local feature matching expects the features to be invariant to these poses, we propose an SE(3)-equivariant feature extractor to simultaneously generate two types of features. In this feature extractor, representations that preserve the poses are first encoded by our novel SE(3)-equivariant network and then converted into pose-invariant ones by a pose-detaching module. Experiments demonstrate that our proposed method increases the recall rate by 20% compared to state-of-the-art methods when facing both pose differences and distribution variances., Comment: ICRA 2023

Robotic peg-in-hole assembly remains a challenging task due to its high accuracy demand. Previous work tends to simplify the problem by restricting the degree of freedom of the end-effector, or limiting the distance between the target and the initial pose position, which prevents them from being deployed in real-world manufacturing. Thus, we present a Coarse-to-Fine Visual Servoing (CFVS) peg-in-hole method, achieving 6-DoF end-effector motion control based on 3D visual feedback. CFVS can handle arbitrary tilt angles and large initial alignment errors through a fast pose estimation before refinement. Furthermore, by introducing a confidence map to ignore the irrelevant contour of objects, CFVS is robust against noise and can deal with various targets beyond training data. Extensive experiments show CFVS outperforms state-of-the-art methods and obtains 100%, 91%, and 82% average success rates in 3-DoF, 4-DoF, and 6-DoF peg-in-hole, respectively., Comment: Accepted by ICRA 2023

In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with few informative labeled data, comes in handy for such a scenario. In this work, we present D2ADA, a general active domain adaptation framework for semantic segmentation. To adapt the model to the target domain with minimum queried labels, we propose acquiring labels of the samples with high probability density in the target domain yet with low probability density in the source domain, complementary to the existing source domain labeled data. To further facilitate labeling efficiency, we design a dynamic scheduling policy to adjust the labeling budgets between domain exploration and model uncertainty over time. Extensive experiments show that our method outperforms existing active learning and domain adaptation baselines on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than 5% target domain annotations, our method reaches comparable results with that of full supervision. Our code is publicly available at https://github.com/tsunghan-wu/D2ADA., Comment: Accepted by ECCV 2022. The code is available at https://github.com/tsunghan-wu/D2ADA

Anomaly awareness is an essential capability for safety-critical applications such as autonomous driving. While recent progress of robotics and computer vision has enabled anomaly detection for image classification, anomaly detection on semantic segmentation is less explored. Conventional anomaly-aware systems assuming other existing classes as out-of-distribution (pseudo-unknown) classes for training a model will result in two drawbacks. (1) Unknown classes, which applications need to cope with, might not actually exist during training time. (2) Model performance would strongly rely on the class selection. Observing this, we propose a novel Synthetic-Unknown Data Generation, intending to tackle the anomaly-aware semantic segmentation task. We design a new Masked Gradient Update (MGU) module to generate auxiliary data along the boundary of in-distribution data points. In addition, we modify the traditional cross-entropy loss to emphasize the border data points. We reach the state-of-the-art performance on two anomaly segmentation datasets. Ablation studies also demonstrate the effectiveness of proposed modules.

Object detection plays a deep role in visual systems by identifying instances for downstream algorithms. In industrial scenarios, however, a slight change in manufacturing systems would lead to costly data re-collection and human annotation processes to re-train models. Existing solutions such as semi-supervised and few-shot methods either rely on numerous human annotations or suffer low performance. In this work, we explore a novel object detector based on interactive perception (ODIP), which can be adapted to novel domains in an automated manner. By interacting with a grasping system, ODIP accumulates visual observations of novel objects, learning to identify previously unseen instances without human-annotated data. Extensive experiments show ODIP outperforms both the generic object detector and state-of-the-art few-shot object detector fine-tuned in traditional manners. A demo video is provided to further illustrate the idea.

While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existing FSOD systems follow FSC approaches, ignoring critical issues such as spatial variability and uncertain representations, and consequently result in low performance. Observing this, we propose a novel \textbf{Dual-Awareness Attention (DAnA)} mechanism that enables networks to adaptively interpret the given support images. DAnA transforms support images into \textbf{query-position-aware} (QPA) features, guiding detection networks precisely by assigning customized support information to each local region of the query. In addition, the proposed DAnA component is flexible and adaptable to multiple existing object detection frameworks. By adopting DAnA, conventional object detection networks, Faster R-CNN and RetinaNet, which are not designed explicitly for few-shot learning, reach state-of-the-art performance in FSOD tasks. In comparison with previous methods, our model significantly increases the performance by 47\% (+6.9 AP), showing remarkable ability under various evaluation settings.

When electrons in a solid are excited with light, they can alter the free energy landscape and access phases of matter that are beyond reach in thermal equilibrium. This accessibility becomes of vast importance in the presence of phase competition, when one state of matter is preferred over another by only a small energy scale that, in principle, is surmountable by light. Here, we study a layered compound, LaTe$_3$, where a small in-plane (a-c plane) lattice anisotropy results in a unidirectional charge density wave (CDW) along the c-axis. Using ultrafast electron diffraction, we find that after photoexcitation, the CDW along the c-axis is weakened and subsequently, a different competing CDW along the a-axis emerges. The timescales characterizing the relaxation of this new CDW and the reestablishment of the original CDW are nearly identical, which points towards a strong competition between the two orders. The new density wave represents a transient non-equilibrium phase of matter with no equilibrium counterpart, and this study thus provides a framework for unleashing similar states of matter that are "trapped" under equilibrium conditions.

Complex systems, which consist of a large number of interacting constituents, often exhibit universal behavior near a phase transition. A slowdown of certain dynamical observables is one such recurring feature found in a vast array of contexts. This phenomenon, known as critical slowing down, is well studied mostly in thermodynamic phase transitions. However, it is less understood in highly nonequilibrium settings, where the time it takes to traverse the phase boundary becomes comparable to the timescale of dynamical fluctuations. Using transient optical spectroscopy and femtosecond electron diffraction, we studied a photo-induced transition of a model charge-density-wave (CDW) compound, LaTe$_3$. We observed that it takes the longest time to suppress the order parameter at the threshold photoexcitation density, where the CDW transiently vanishes. This finding can be quantitatively captured by generalizing the time-dependent Landau theory to a system far from equilibrium. The experimental observation and theoretical understanding of dynamical slowing down may offer insight into other general principles behind nonequilibrium phase transitions in many-body systems.

X-ray scattering is one of the primary tools to determine crystallographic configuration with atomic accuracy. However, the measurement of ultrafast structural dynamics in monolayer crystals remains a long-standing challenge due to a significant reduction of diffraction volume and complexity of data analysis, prohibiting the application of ultrafast x-ray scattering to study nonequilibrium structural properties at the two-dimensional limit. Here, we demonstrate femtosecond surface x-ray diffraction in combination with crystallographic model-refinement calculations to quantify the ultrafast structural dynamics of monolayer WSe$_2$ crystals supported on a substrate. We found the absorbed optical photon energy is preferably coupled to the in-plane lattice vibrations within 2 picoseconds while the out-of-plane lattice vibration amplitude remains unchanged during the first 10 picoseconds. The model-assisted fitting suggests an asymmetric intralayer spacing change upon excitation. The observed nonequilibrium anisotropic structural dynamics in two-dimensional materials agrees with first-principles nonadiabatic modeling in both real and momentum space, marking the distinct structural dynamics of monolayer crystals from their bulk counterparts. The demonstrated methods unlock the benefit of surface sensitive x-ray scattering to quantitatively measure ultrafast structural dynamics in atomically thin materials and across interfaces.

The superconductor-to-insulator transition (SIT) induced by means such as external magnetic fields, disorder or spatial confinement is a vivid illustration of a quantum phase transition dramatically affecting the superconducting order parameter. In pursuit of a new realization of the SIT by interfacial charge transfer, we developed extremely thin superlattices composed of high $T_c$ superconductor YBa$_2$Cu$_3$O$_7$ (YBCO) and colossal magnetoresistance ferromagnet La$_{0.67}$Ca$_{0.33}$MnO$_3$ (LCMO). By using linearly polarized resonant X-ray absorption spectroscopy and magnetic circular dichroism, combined with hard X-ray photoelectron spectroscopy, we derived a complete picture of the interfacial carrier doping in cuprate and manganite atomic layers, leading to the transition from superconducting to an unusual Mott insulating state emerging with the increase of LCMO layer thickness. In addition, contrary to the common perception that only transition metal ions may response to the charge transfer process, we found that charge is also actively compensated by rare-earth and alkaline-earth metal ions of the interface. Such deterministic control of $T_c$ by pure electronic doping without any hindering effects of chemical substitution is another promising route to disentangle the role of disorder on the pseudo-gap and charge density wave phases of underdoped cuprates., Comment: Accepted in Scientific Reports

Fast, reversible redox reactions in solids at low temperatures without thermomechanical degradation are a promising strategy for enhancing the overall performance and lifetime of many energy materials and devices. However, the robust nature of the cation's oxidation state and the high thermodynamic barrier have hindered the realization of fast catalysis and bulk diffusion at low temperatures. Here, we report a significant lowering of the redox temperature by epitaxial stabilization of strontium cobaltites (SrCoOx) grown directly as one of two distinct crystalline phases, either the perovskite SrCoO3-{\delta} or the brownmillerite SrCoO2.5. Importantly, these two phases can be reversibly switched at a remarkably reduced temperature (200~300 {\deg}C) in a considerably short time (< 1 min) without destroying the parent framework. The fast, low temperature redox activity in SrCoO3-{\delta} is attributed to a small Gibbs free energy difference between two topotatic phases. Our findings thus provide useful information for developing highly sensitive electrochemical sensors and low temperature cathode materials.

(LaNiO3)n/(LaMnO3)2 superlattices were grown using ozone-assisted molecular beam epitaxy, where LaNiO3 is a paramagnetic metal and LaMnO3 is an antiferromagnetic insulator. The superlattices exhibit excellent crystallinity and interfacial roughness of less than 1 unit cell. X-ray spectroscopy and dichroism measurements indicate that electrons are transferred from the LaMnO3 to the LaNiO3, inducing magnetism in LaNiO3. Magnetotransport measurements reveal a transition from metallic to insulating behavior as the LaNiO3 layer thickness is reduced from 5 unit cells to 2 unit cells and suggest a modulated magnetic structure within LaNiO3.

We report on a resonant soft X-ray spectroscopy study of the electronic and magnetic structure of the cuprate-manganite interface. Polarized X-ray spectroscopy measurements taken at the Cu L edge reveal up to a five-fold increase in the dichroic signal as compared to past experimental and theoretical values. Furthermore an increase in the degree of interlayer charge transfer up to 0.25e (where e is charge of an electron) per copper ion is observed leading to a profound reconstruction in the orbital scheme for these interfacial copper ions. It is inferred that these enhancement are related to an increase in TMI observed for manganite layers grown with rapidly modulated flux.

We report on the synthesis of ultrathin films of highly distorted EuNiO3 (ENO) grown by interrupted pulse laser epitaxy on YAlO3 (YAO) substrates. Through mapping the phase space of nickelate thin film epitaxy, the optimal growth temperatures were found to scale linearly with the Goldschmidt tolerance factor. Considering the gibbs energy of the expanding film, this empirical trend is discussed in terms of epitaxial stabilization and the escalation of the lattice energy due to lattice distortions and decreasing symmetry. These findings are fundamental to other complex oxide perovskites, and provide a route to the synthesis of other perovskite structures in ultrathin-film form., Comment: 7 pages, 3 figures

Using resonant X-ray spectroscopies combined with density functional calculations, we find an asymmetric bi-axial strain-induced $d$-orbital response in ultra-thin films of the correlated metal LaNiO$_3$ which are not accessible in the bulk. The sign of the misfit strain governs the stability of an octahedral "breathing" distortion, which, in turn, produces an emergent charge-ordered ground state with an altered ligand-hole density and bond covalency. Control of this new mechanism opens a pathway to rational orbital engineering, providing a platform for artificially designed Mott materials.