arXiv subjects

Tian Chen

Publications and source records attributed to Tian Chen.

At least 55 records · Page 3Linked to original sources

arXiv2022

Nth power root topological phases in Hermitian and non-Hermitian systems

Constructing new topological phases is very important in both Hermitian and non-Hermitian systems because of their potential applications. Here we propose theoretically and demonstrate a general scheme experimentally to construct Nth power root (NPR) topological phases. Such a scheme is not only suitable for Hermitian systems, but also non-Hermitian systems. It is found that the robust degree of edge state in the Hermitian system becomes stronger and stronger with the increase of N. It tends to be a strongly surface localized form when N is large enough. In the non-Hermitian system, the skin effect becomes more apparent, and it approaches the ideal situation with the increase of N. This means that edge states and skin effects can be observed by taking different N. This scheme has been proved experimentally by designing circuits. Our work opens up a new way to engineer topological states according to the requirements, which is very important for developing topologically protected devices, such as topology sensing, switches, and so on.

cond-mat.mes-hall↗

arXiv2021

Creation of electrical knots and observation of DNA topology

Knots are fascinating topological structures that have been observed in various contexts, ranging from micro-worlds to macro-systems, and are conjectured to play a fundamental role in their respective fields. In order to characterize their physical properties, some topological invariants have been introduced, such as unknotting number, bridge number, Jones Polynomial and so on. While these invariants have been proven to theoretically describe the topological properties of knots, they have remained unexplored experimentally because of the difficulty associated with control. Herein, we report the creation of isolated electrical knots based on discrete distributions of impedances in electric circuits and observation of the unknotting number for the first time. Furthermore, DNA structure transitions under the action of enzymes were studied experimentally using electrical circuits, and the topological equivalence of DNA double strands was demonstrated. As the first experiment on the creation of electrical knots in real space, our work opens up the exciting possibility of exploring topological properties of DNA and some molecular strands using electric circuits.

physics.bio-ph↗

arXiv2021

Smooth triaxial weaving with naturally curved ribbons

Triaxial weaving is a handicraft technique that has long been used to create curved structures using initially straight and flat ribbons. Weavers typically introduce discrete topological defects to produce nonzero Gaussian curvature, albeit with faceted surfaces. We demonstrate that, by tuning the in-plane curvature of the ribbons, the integrated Gaussian curvature of the weave can be varied continuously, which is not feasible using traditional techniques. Further, we reveal that the shape of the physical unit cells is dictated solely by the in-plane geometry of the ribbons, not elasticity. Finally, we leverage the geometry-driven nature of triaxial weaving to design a set of ribbon profiles to weave smooth spherical, ellipsoidal, and toroidal structures.

cond-mat.soft↗

arXiv2021

Observation of hybrid higher-order skin-topological effect in non-Hermitian topolectrical circuits

Robust boundary states epitomize how deep physics can give rise to concrete experimental signatures with technological promise. Of late, much attention has focused on two distinct mechanisms for boundary robustness - topological protection, as well as the non-Hermitian skin effect. In this work, we report the first experimental realizations of hybrid higher-order skin-topological effect, in which the skin effect selectively acts only on the topological boundary modes, not the bulk modes. Our experiments, which are performed on specially designed non-reciprocal 2D and 3D topolectrical circuit lattices, showcases how non-reciprocal pumping and topological localization dynamically interplays to form various novel states like 2D skin-topological, 3D skin-topological-topological hybrid states, as well as 2D and 3D higher-order non-Hermitian skin states. Realized through our highly versatile and scalable circuit platform, theses states have no Hermitian nor lower-dimensional analog, and pave the way for new applications in topological switching and sensing through the simultaneous non-trivial interplay of skin and topological boundary localizations.

cond-mat.mes-hall↗

arXiv2021

Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration

Recent years have witnessed significant progress in 3D hand mesh recovery. Nevertheless, because of the intrinsic 2D-to-3D ambiguity, recovering camera-space 3D information from a single RGB image remains challenging. To tackle this problem, we divide camera-space mesh recovery into two sub-tasks, i.e., root-relative mesh recovery and root recovery. First, joint landmarks and silhouette are extracted from a single input image to provide 2D cues for the 3D tasks. In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues. Such generated 3D mesh coordinates are expressed relative to a root position, i.e., wrist of the hand. In the root recovery task, the root position is registered to the camera space by aligning the generated 3D mesh back to 2D cues, thereby completing cameraspace 3D mesh recovery. Our pipeline is novel in that (1) it explicitly makes use of known semantic relations among joints and (2) it exploits 1D projections of the silhouette and mesh to achieve robust registration. Extensive experiments on popular datasets such as FreiHAND, RHD, and Human3.6M demonstrate that our approach achieves stateof-the-art performance on both root-relative mesh recovery and root recovery. Our code is publicly available at https://github.com/SeanChenxy/HandMesh.

cs.CV↗

arXiv2020

What is the stiffness of a bent book?

We study the bending of a book-like system, comprising a stack of elastic plates coupled through friction. The behavior of this layered system is rich and nontrivial, with a non-additive enhancement of the apparent stiffness and a significant hysteretic response. A dimension reduction procedure is employed to develop a centerline-based theory describing the stack as a non-linear planar rod with internal shear. We consider the coupling between the nonlinear geometry and the elasticity of the stacked plates, treating the interlayer friction perturbatively. This model yields predictions for the stack's mechanical response in three-point bending that are in excellent agreement with our experiments. Remarkably, we find that the energy dissipated during deformation can be rationalized over three orders of magnitude, including the regimes of a thick stack with large deflection. This robust dissipative mechanism could be harnessed to design new classes of low-cost and efficient damping devices.

cond-mat.soft↗

arXiv2020

Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets

Monocular depth estimation plays a crucial role in 3D recognition and understanding. One key limitation of existing approaches lies in their lack of structural information exploitation, which leads to inaccurate spatial layout, discontinuous surface, and ambiguous boundaries. In this paper, we tackle this problem in three aspects. First, to exploit the spatial relationship of visual features, we propose a structure-aware neural network with spatial attention blocks. These blocks guide the network attention to global structures or local details across different feature layers. Second, we introduce a global focal relative loss for uniform point pairs to enhance spatial constraint in the prediction, and explicitly increase the penalty on errors in depth-wise discontinuous regions, which helps preserve the sharpness of estimation results. Finally, based on analysis of failure cases for prior methods, we collect a new Hard Case (HC) Depth dataset of challenging scenes, such as special lighting conditions, dynamic objects, and tilted camera angles. The new dataset is leveraged by an informed learning curriculum that mixes training examples incrementally to handle diverse data distributions. Experimental results show that our method outperforms state-of-the-art approaches by a large margin in terms of both prediction accuracy on NYUDv2 dataset and generalization performance on unseen datasets.

cs.CV↗

arXiv2019

Deep Markov Chain Monte Carlo

We propose a new computationally efficient sampling scheme for Bayesian inference involving high dimensional probability distributions. Our method maps the original parameter space into a low-dimensional latent space, explores the latent space to generate samples, and maps these samples back to the original space for inference. While our method can be used in conjunction with any dimension reduction technique to obtain the latent space, and any standard sampling algorithm to explore the low-dimensional space, here we specifically use a combination of auto-encoders (for dimensionality reduction) and Hamiltonian Monte Carlo (HMC, for sampling). To this end, we first run an HMC to generate some initial samples from the original parameter space, and then use these samples to train an auto-encoder. Next, starting with an initial state, we use the encoding part of the autoencoder to map the initial state to a point in the low-dimensional latent space. Using another HMC, this point is then treated as an initial state in the latent space to generate a new state, which is then mapped to the original space using the decoding part of the auto-encoder. The resulting point can be treated as a Metropolis-Hasting (MH) proposal, which is either accepted or rejected. While the induced dynamics in the parameter space is no longer Hamiltonian, it remains time reversible, and the Markov chain could still converge to the canonical distribution using a volume correction term. Dropping the volume correction step results in convergence to an approximate but reasonably accurate distribution. The empirical results based on several high-dimensional problems show that our method could substantially reduce the computational cost of Bayesian inference.

stat.CO↗

arXiv2019

Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method

It is well established that temporal organization is critical to memory, and that the ability to temporally organize information is fundamental to many perceptual, cognitive, and motor processes. While our understanding of how the brain processes the spatial context of memories has advanced considerably, our understanding of their temporal organization lags far behind. In this paper, we propose a new approach for elucidating the neural basis of complex behaviors and temporal organization of memories. More specifically, we focus on neural decoding - the prediction of behavioral or experimental conditions based on observed neural data. In general, this is a challenging classification problem, which is of immense interest in neuroscience. Our goal is to develop a new framework that not only improves the overall accuracy of decoding, but also provides a clear latent representation of the decoding process. To accomplish this, our approach uses a Variational Auto-encoder (VAE) model with a diversity-encouraging prior based on determinantal point processes (DPP) to improve latent representation learning by avoiding redundancy in the latent space. We apply our method to data collected from a novel rat experiment that involves presenting repeated sequences of odors at a single port and testing the rats' ability to identify each odor. We show that our method leads to substantially higher accuracy rate for neural decoding and allows to discover novel biological phenomena by providing a clear latent representation of the decoding process.

stat.ML↗

arXiv2019

Differentiated context-aware hook placement for different owners' smartphones

A hook is a piece of code. It checks user privacy policy before some sensitive operations happen. We propose an automated solution named Prihook for hook placement in the Android Framework. Addressing specific context-aware user privacy concerns, the hook placement in Prihook is personalized. Specifically, we design User Privacy Preference Table (UPPT) to help a user express his privacy concerns. And we leverage machine learning to discover a Potential Method Set (consisting of Sensor Data Access Methods and Sensor Control Methods) from which we can select a particular subset to put hooks. We propose a mapping from words in the UPPT lexicon to methods in the Potential Method Set. With this mapping, Prihook is able to (a) select a specific set of methods; and (b) generate and place hooks automatically. We test Prihook separately on 6 typical UPPTs representing 6 kinds of resource-sensitive UPPTs, and no user privacy violation is found. The experimental results show that the hooks placed by PriHook have small runtime overhead.

cs.SE↗

arXiv2019

Observation of novel robust edge states without bulk-boundary correspondence in non-Hermitian quantum walks

Recently, the study of non-Hermitian physics has attracted considerable attention. The modified bulk-boundary correspondence has been proposed to understand topological edge states in non-Hermitian static systems. Here we report a new experimental observation of edge states in non-Hermitian periodically driven systems. Some unconventional edge states are found not to be satisfied with the bulk-boundary correspondence when the system belongs to the broken parity-time (PT) symmetric phase. The experiments are performed in our constructed non-Hermitian light quantum walk platform with left and right boundaries, where the beams outside system boundary are blocked subtly at the end of each step. The robust properties of these edge states against to static perturbations and disorder have also been demonstrated experimentally. The finding of robust edge states in broken PT-symmetric phase inspires us to explore a robust transport channel in ubiquitously complex systems with strong dissipation.

physics.optics↗

arXiv2018

Autonomous Deployment of a Solar Panel Using an Elastic Origami and Distributed Shape Memory Polymer Actuators

Deployable mechanical systems such as space solar panels rely on the intricate stowage of passive modules, and sophisticated deployment using a network of motorized actuators. As a result, a significant portion of the stowed mass and volume are occupied by these support systems. An autonomous solar panel array deployed using the inherent material behavior remains elusive. In this work, we develop an autonomous self-deploying solar panel array that is programmed to activate in response to changes in the surrounding temperature. We study an elastic "flasher" origami sheet embedded in a circle of scissor mechanisms, both printed with shape memory polymers. The scissor mechanisms are optimized to provide the maximum expansion ratio while delivering the necessary force for deployment. The origami sheet is also optimized to carry the maximum number of solar panels given space constraints. We show how the folding of the "flasher" origami exhibits a bifurcation behavior resulting in either a cone or disk shape both numerically and in experiments. A folding strategy is devised to avoid the undesired cone shape. The resulting design is entirely 3D printed, achieves an expansion ratio of 1000% in under 40 seconds, and shows excellent agreement with simulation prediction both in the stowed and deployed configurations.

physics.app-ph↗

arXiv2018

Long-lived quantum speedup based on plasmonic hot spot systems

Long-lived quantum speedup serves as a fundamental component for quantum algorithms. The quantum walk is identified as an ideal scheme to realize the long-lived quantum speedup. However, one finds that the duration of quantum speedup is very short in real systems implementing quantum walk. The speedup can last only dozens of femtoseconds in the photosynthetic light-harvesting system, which was regarded as the best candidate for quantum information processing. Here, we construct one plasmonic system with two-level molecules embodied in the hot spots of one-dimensional nanoparticle chains to realize the long-lived quantum speedup. The coherent and incoherent coupling parameters in the system are obtained by means of Green's tensor technique. Our results reveal that the duration of quantum speedup in our scheme can exceed 500 fs under strong coherent coupling conditions, which is several times larger than that in the photosynthetic light-harvesting system. Our proposal presents a competitive scheme to realize the long-lived quantum speedup, which is very beneficial for quantum algorithms.

physics.optics↗

arXiv2017

Ultrafast coherent energy transfer with high efficiency based on plasmonic nanostructures

The theory of energy transfer dynamics of a pair of donor and acceptor molecules located in the plasmonic hot spots is developed by means of the master equation approach and the electromagnetic Green's tensor technique. A nonlocal effect has been considered by using a hydrodynamic model. The coherent interaction between the two molecules in plasmonic nanostructures is investigated under some strong coupling conditions. It is shown that the energy transfer efficiency of a pair of molecules can be improved largely and the transfer time decreases to dozens of femtoseconds when the contribution of quantum coherence is considered. The physical origin for such a phenomenon has also been analyzed. This ultrafast and high-efficiency energy transfer mechanism could be beneficial for artificial light-harvesting devices.

physics.optics↗

arXiv2017

An Autonomous Programmable Actuator and Shape Reconfigurable Structures using Bistability and Shape Memory Polymers

Autonomous deployment and shape reconfiguration of structures is a crucial field of research in space exploration with emerging applications in the automotive, building and biomedical industries. Challenges in achieving autonomy include: bulky energy sources, imprecise deployment, jamming of components and lack of structural integrity. Leveraging advances in the fields of shape memory polymers, bistability and 3D multi-material printing, we present a 3D printed programmable actuator that enables the autonomous deployment and shape reconfiguration of structures activated though surrounding temperature change. Using a shape memory polymer as the temperature controllable energy source and a bistable mechanism as the linear actuator and force amplifier, the structures achieve precise geometric activation and quantifiable load bearing capacity. The proposed unit actuator integrates these two components and is designed to be assembled into larger deployable and shape reconfigurable structures. First, we demonstrate that the activation of the unit actuator can be sequenced by tailoring each shape memory polymer to a different activation time. Next, by changing the configuration of the actuator, we demonstrate an initially flat surface that transforms into a pyramid or a hyperbolic paraboloid, thus demonstrating a multi-state structure. Load bearing capability is demonstrated for both during activation and in the operating state.

physics.app-ph↗

arXiv2017

Harnessing bistability for directional propulsion of untethered, soft robots

In most macro-scale robotics systems , propulsion and controls are enabled through a physical tether or complex on-board electronics and batteries. A tether simplifies the design process but limits the range of motion of the robot, while on-board controls and power supplies are heavy and complicate the design process. Here we present a simple design principle for an untethered, entirely soft, swimming robot with the ability to achieve preprogrammed, directional propulsion without a battery or on-board electronics. Locomotion is achieved by employing actuators that harness the large displacements of bistable elements, triggered by surrounding temperature changes. Powered by shape memory polymer (SMP) muscles, the bistable elements in turn actuates the robot's fins. Our robots are fabricated entirely using a commercially available 3D printer with no post-processing. As a proof-of-concept, we demonstrate the ability to program a vessel, which can autonomously deliver a cargo and navigate back to the deployment point.

cs.RO↗

arXiv2016

Conditional Pulse Nulling Receiver for Multi-pulse PPM and Binary Quantum Coding Signals

Conditional pulse nulling (CPN) receiver is proposed by Dolinar to discriminate pulse position modulation (PPM) signals. The receiver using a beam splitter and an on-off photon detector can outperform the standard quantum limit (SQL) for PPM signals. In this paper, we apply this receiver to multi-pulse PPM (MPPM) and binary quantum coding signals, and use dynamic programming algorithm to optimize the control strategy. The MPPM signals is used to improve the band-utilization efficiency, and the binary quantum coding make the communication able to correct the error. Numerical simulation results show that the CPN receiver with optimized strategy can also outperform SQL for both MPPM and binary quantum coding signals.

quant-ph↗

arXiv2016

Extraordinary behaviors in two-dimensional decoherent alternative quantum walk

The quantum and classical behaviors of two-dimensional (2D) alternative quantum walk (AQW) in the presence of decoherence have been discussed in detail. For any kinds of decoherence, the analytic expressions for the moments of position distribution of AQW have been obtained. Taking the broken line noise and coin-decoherence as examples of decoherence, we find that when the decoherence only emerges in one direction, the anisotropic position distribution pattern appears, and not all the motions of quantum walkers exhibit the transition from quantum to classical behaviors. The correlations between the walkers and the coin in 2D AQW have been discussed. The anisotropic correlations between walkers and coin have been revealed in the presence of decoherence.

quant-ph↗