arXiv subjects

Han Ma

Publications and source records attributed to Han Ma.

35 records · Page 2Linked to original sources

arXiv2022

Quenched random mass disorder in the large N theory of vector bosons

We study the critical bosonic O(N) vector model with quenched random mass disorder in the large N limit. Due to the replicated action which is sometimes not bounded from below, we avoid the replica trick and adopt a traditional approach to directly compute the disorder averaged physical observables. At $N=\infty$, we can exactly solve the disordered model. The resulting low energy behavior can be described by two scale invariant theories, one of which has an intrinsic scale. At finite $N$, we find that the previously proposed attractive disordered fixed point at $d=2$ continues to exist at $d=2+ε$ spatial dimensions. We also studied the system in the $3<d<4$ spatial dimensions where the disorder is relevant at the Gaussian fixed point. However, no physical attractive fixed point is found right below four spatial dimensions. Nevertheless, the stable fixed point at $2+ε$ dimensions can still survive at $d=3$ where the system has experimental realizations. Some critical exponents are predicted in order to be checked by future numerics and experiments.

cond-mat.str-el↗

arXiv2022

Helical superfluid in a frustrated honeycomb Bose-Hubbard model

We study a "helical" superfluid, a nonzero-momentum condensate in a frustrated bosonic model. At mean-field Bogoliubov level, such a novel state exhibits "smectic" fluctuation that are qualitatively stronger than that of a conventional superfluid. We develop a phase diagram and compute a variety of its physical properties, including the spectrum, structure factor, condensate depletion, momentum distribution, all of which are qualitatively distinct from that of a conventional superfluid. Interplay of fluctuations, interaction and lattice effects gives rise to the phenomenon of order-by-disorder, leading to a crossover from the smectic superfluid regime to the anisotropic XY superfluid phase. We complement the microscopic lattice analysis with a field theoretic description for such a helical superfluid, which we derive from microscopics and justify on general symmetry grounds, reassuringly finding full consistency. Possible experimental realizations are discussed.

cond-mat.quant-gas↗

arXiv2022

Enhance Connectivity of Promising Regions for Sampling-based Path Planning

Sampling-based path planning algorithms usually implement uniform sampling methods to search the state space. However, uniform sampling may lead to unnecessary exploration in many scenarios, such as the environment with a few dead ends. Our previous work proposes to use the promising region to guide the sampling process to address the issue. However, the predicted promising regions are often disconnected, which means they cannot connect the start and goal state, resulting in a lack of probabilistic completeness. This work focuses on enhancing the connectivity of predicted promising regions. Our proposed method regresses the connectivity probability of the edges in the x and y directions. In addition, it calculates the weight of the promising edges in loss to guide the neural network to pay more attention to the connectivity of the promising regions. We conduct a series of simulation experiments, and the results show that the connectivity of promising regions improves significantly. Furthermore, we analyze the effect of connectivity on sampling-based path planning algorithms and conclude that connectivity plays an essential role in maintaining algorithm performance.

cs.RO↗

arXiv2022

NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments

Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explicitly limit the probability of collision to an acceptable bound in uncertain environments. However, convex obstacles or Gaussian uncertainties are usually assumed to make the problem tractable in the existing method. These assumptions limit the generalization and application of path planners in real-world implementations. In this article, we propose to apply deep learning methods to the sampling-based planner, developing a novel risk bounded near-optimal path planning algorithm named neural risk-aware RRT (NR-RRT). Specifically, a deterministic risk contours map is maintained by perceiving the probabilistic nonconvex obstacles, and a neural network sampler is proposed to predict the next most-promising safe state. Furthermore, the recursive divide-and-conquer planning and bidirectional search strategies are used to accelerate the convergence to a near-optimal solution with guaranteed bounded risk. Worst-case theoretical guarantees can also be proven owing to a standby safety guaranteed planner utilizing a uniform sampling distribution. Simulation experiments demonstrate that the proposed algorithm outperforms the state-of-the-art remarkably for finding risk bounded low-cost paths in seen and unseen environments with uncertainty and nonconvex constraints.

cs.RO↗

arXiv2021

Constraints on beta functions in field theories

The $β$-functions describe how couplings run under the renormalization group flow in field theories. In general, all couplings that respect the symmetry and locality are generated under the renormalization group flow, and the exact renormalization group flow is characterized by the $β$-functions defined in the infinite dimensional space of couplings. In this paper, we show that the renormalization group flow is highly constrained so that the $β$-functions defined in a measure zero subspace of couplings completely determine the $β$-functions in the entire space of couplings. We provide a quantum renormalization group-based algorithm for reconstructing the full $β$-functions from the $β$-functions defined in the subspace. As examples, we derive the full $β$-functions for the $O(N)$ vector model and the $O_L(N) \times O_R(N)$ matrix model entirely from the $β$-functions defined in the subspace of single-trace couplings.

hep-th↗

arXiv2019

Shadow of complex fixed point: Approximate conformality of Q>4 Potts model

We study the famous example of weakly first order phase transitions in the 1+1D quantum Q-state Potts model at Q>4. We numerically show that these weakly first order transitions have approximately conformal invariance. Specifically, we find entanglement entropy on considerably large system sizes fits perfectly with the universal scaling law of this quantity in the conformal field theories (CFTs). This supports that the weakly first order transitions is proximate to complex fixed points, which are described by recent conjectured complex CFTs. Moreover, the central charge extracted from this fitting is close to the real part of the complex central charge of these complex CFTs. We also study the conformal towers and the drifting behaviors of these conformal data (e.g., central charge and scaling dimensions).

cond-mat.str-el↗

arXiv2019

Using DP Towards A Shortest Path Problem-Related Application

The detection of curved lanes is still challenging for autonomous driving systems. Although current cutting-edge approaches have performed well in real applications, most of them are based on strict model assumptions. Similar to other visual recognition tasks, lane detection can be formulated as a two-dimensional graph searching problem, which can be solved by finding several optimal paths along with line segments and boundaries. In this paper, we present a directed graph model, in which dynamic programming is used to deal with a specific shortest path problem. This model is particularly suitable to represent objects with long continuous shape structure, e.g., lanes and roads. We apply the designed model and proposed an algorithm for detecting lanes by formulating it as the shortest path problem. To evaluate the performance of our proposed algorithm, we tested five sequences (including 1573 frames) from the KITTI database. The results showed that our method achieves an average successful detection precision of 97.5%.

cs.CV↗

arXiv2018

Higher Rank Deconfined Quantum Criticality at the Lifshitz Transition and the Exciton Bose Condensate

Deconfined quantum critical points are characterized by the presence of an emergent gauge field and exotic fractionalized particles, which exist as well-defined excitations only at the critical point. We here demonstrate the existence of quantum critical points described by an emergent tensor gauge theory featuring subdimensional excitations, in close relation to fracton theories. We begin by reexamining a previously studied deconfined quantum critical point between two valence bond solid (VBS) phases on a bilayer honeycomb lattice. We show that the critical theory maps onto a rank-two tensor gauge theory featuring one-dimensional particles. In a slightly different context, the same tensor gauge theory also describes a deconfined quantum critical point between a two-dimensional superfluid and a finite-momentum Bose condensate, both of which are dual to rank-one gauge theories. This represents an entirely new class of deconfined quantum criticality, in which a critical tensor gauge theory arises on top of a stable conventional gauge theory. Furthermore, we propose that this quantum critical point gives rise to a new finite-temperature phase of bosons, behaving as an exciton Bose condensate, in which excitons (boson-hole pairs) are condensed but individual bosons are not. We discuss how small modifications of this theory give rise to the stable quantum "exciton Bose liquid" phase studied by Paramekanti, Balents, and Fisher.

cond-mat.str-el↗

arXiv2018

Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation

Lane detection is very important for self-driving vehicles. In recent years, computer stereo vision has been prevalently used to enhance the accuracy of the lane detection systems. This paper mainly presents a multiple lane detection algorithm developed based on optimised dense disparity map estimation, where the disparity information obtained at time t_{n} is utilised to optimise the process of disparity estimation at time t_{n+1}. This is achieved by estimating the road model at time t_{n} and then controlling the search range for the disparity estimation at time t_{n+1}. The lanes are then detected using our previously published algorithm, where the vanishing point information is used to model the lanes. The experimental results illustrate that the runtime of the disparity estimation is reduced by around 37% and the accuracy of the lane detection is about 99%.

cs.CV↗

arXiv2018

Fracton topological order from Higgs and partial confinement mechanisms of rank-two gauge theory

Fractons are gapped point-like excitations in $d=3$ topological ordered phases whose motion is constrained. They have been discovered in several gapped models but a unifying physical mechanism for generating them is still missing. It has been noticed that in symmetric-tensor ${\rm U}(1)$ gauge theories, charges are fractons and cannot move freely due to, for example, the conservation of not only the charge but also the dipole moment. To connect these theories with fully gapped fracton models, we study Higgs and partial confinement mechanisms in rank-2 symmetric-tensor gauge theories, where charges or magnetic excitations, respectively, are condensed. Specifically, we describe two different routes from the rank-2 ${\rm U}(1)$ scalar charge theory to the X-cube fracton topological order, finding that a combination of Higgs and partial confinement mechanisms is necessary to obtain the fully gapped fracton model. On the other hand, the rank-2 $\mathbb{Z}_2$ scalar charge theory, which is obtained from the former theory upon condensing charge-2 matter, is equivalent to four copies of the $d=3$ toric code and does not support fracton excitations. We also explain how the checkerboard fracton model can be viewed as a rank-2 $\mathbb{Z}_2$ gauge theory with two different Gauss' law constraints on different lattice sites.

cond-mat.str-el↗

arXiv2018

Recoverable Information and Emergent Conservation Laws in Fracton Stabilizer Codes

We introduce a new quantity, that we term recoverable information, defined for stabilizer Hamiltonians. For such models, the recoverable information provides a measure of the topological information, as well as a physical interpretation, which is complementary to topological entanglement entropy. We discuss three different ways to calculate the recoverable information, and prove their equivalence. To demonstrate its utility, we compute recoverable information for fracton models using all three methods where appropriate. From the recoverable information, we deduce the existence of emergent $Z_2$ Gauss-law type constraints, which in turn imply emergent $Z_2$ conservation laws for point-like quasiparticle excitations of an underlying topologically ordered phase.

quant-ph↗

arXiv2018

Topological Entanglement Entropy of Fracton Stabilizer Codes

Entanglement entropy provides a powerful characterization of two-dimensional gapped topological phases of quantum matter, intimately tied to their description by topological quantum field theories (TQFTs). Fracton topological orders are three-dimensional gapped topologically ordered states of matter, but the existence of a TQFT description for these phases remains an open question. We show that three-dimensional fracton phases are nevertheless characterized, at least partially, by universal structure in the entanglement entropy of their ground state wave functions. We explicitly compute the entanglement entropy for two archetypal fracton models --- the `X-cube model' and `Haah's code' --- and demonstrate the existence of a topological contribution that scales linearly in subsystem size. We show via Schrieffer-Wolff transformations that the topological entanglement of fracton models is robust against arbitrary local perturbations of the Hamiltonian. Finally, we argue that these results may be extended to characterize localization-protected fracton topological order in excited states of disordered fracton models.

cond-mat.str-el↗

arXiv2018

Possible phases of the spin-$1/2$ $XXZ$ model on a honeycomb lattice by boson-vortex duality

Motivated by recent numerical work, we use the boson-vortex duality to study the possible phases of the frustrated spin-$\frac{1}{2}$ $J_1-J_2$ XXZ models on the honeycomb lattice. By condensing the vortices, we obtain various gapped phases that either break certain lattice symmetry or preserve all the symmetries. The gapped phases breaking lattice symmetries occur when the vortex band structure has two minima. Condensing one of the two vortex flavors leads to an Ising ordered phase, while condensing both vortex flavors gives rise to a valence-bond-solid state. Both of those phases have been observed in the numerical studies of the $J_1-J_2$ XXZ honeycomb model. Furthermore, by tuning the band structure of vortex and condensing it at the $Γ$ point, we obtain a featureless paramagnet. But the precise nature of this featureless state is still unclear and needs future study.

cond-mat.str-el↗

arXiv2017

Fracton topological order via coupled layers

In this work, we develop a coupled layer construction of fracton topological orders in $d=3$ spatial dimensions. These topological phases have sub-extensive topological ground-state degeneracy and possess excitations whose movement is restricted in interesting ways. Our coupled layer approach is used to construct several different fracton topological phases, both from stacked layers of simple $d=2$ topological phases and from stacks of $d=3$ fracton topological phases. This perspective allows us to shed light on the physics of the X-cube model recently introduced by Vijay, Haah, and Fu, which we demonstrate can be obtained as the strong-coupling limit of a coupled three-dimensional stack of toric codes. We also construct two new models of fracton topological order: a semionic generalization of the X-cube model, and a model obtained by coupling together four interpenetrating X-cube models, which we dub the "Four Color Cube model." The couplings considered lead to fracton topological orders via mechanisms we dub "p-string condensation" and "p-membrane condensation," in which strings or membranes built from particle excitations are driven to condense. This allows the fusion properties, braiding statistics, and ground-state degeneracy of the phases we construct to be easily studied in terms of more familiar degrees of freedom. Our work raises the possibility of studying fracton topological phases from within the framework of topological quantum field theory, which may be useful for obtaining a more complete understanding of such phases.

cond-mat.str-el↗

arXiv2017

A Data-driven Approach for Furniture and Indoor Scene Colorization

We present a data-driven approach that colorizes 3D furniture models and indoor scenes by leveraging indoor images on the internet. Our approach is able to colorize the furniture automatically according to an example image. The core is to learn image-guided mesh segmentation to segment the model into different parts according to the image object. Given an indoor scene, the system supports colorization-by-example, and has the ability to recommend the colorization scheme that is consistent with a user-desired color theme. The latter is realized by formulating the problem as a Markov random field model that imposes user input as an additional constraint. We contribute to the community a hierarchically organized image-model database with correspondences between each image and the corresponding model at the part-level. Our experiments and a user study show that our system produces perceptually convincing results comparable to those generated by interior designers.

cs.GR↗

arXiv2012

Overlaying optical lattices for simulation of complex frustrated antiferromagnets

We present design techniques of special optical lattices that allow quantum simulation of spin frustration in two-dimensional systems. By carefully overlaying optical lattices with different periods and orientations, we are able to adjust the ratio between the nearest-neighbor and next-nearest-neighbor interaction strengths in a square spin lattice and realize frustration effects. We show that only laser beams of a single frequency is required, and the parameter space reachable in our design is broad enough to study the important phases in the $J_1$-$J_2$ frustrated Heisenberg model and checkerboard antiferromagnet model. By using the polarization spectroscopy for detection, distinct quantum phases and quantum phase transition points can be characterized straightforwardly. Our design thus offers a suitable setup for simulation of frustrated spin systems.

cond-mat.str-el↗

arXiv2012

Formation and local symmetry of Holstein polaron in t-J model

The formation and local symmetry of spin-lattice polaron has been investigated semiclassically in the planar Holstein t-J-like models within the exact diagonalization method. Due to the interplay of strong correlations and electron-lattice interaction, the doped hole may either move freely or lead to the localized spin-lattice distortion and form a Holstein polaron. The formation of polaron breaks the translational symmetry by suppression of antiferromagnetic correlations and inducement of ferromagnetic correlations locally. Moreover, the breaking of local rotational symmetry around the polaron has been shown. The ground state is generically a parity singlet and the first excited state maybe a parity doublet. Further consequences of the density of states spectra for comparison with future STM experiments are discussed.

cond-mat.str-el↗