arXiv subjects

Zhentian Qian

Publications and source records attributed to Zhentian Qian.

9 recordsLinked to original sources

arXiv2026

Topology-Guided Modular Actor-Critic Learning for Continuous Systems under Temporal Objectives

We study formal policy synthesis for continuous-state stochastic systems under linear temporal logic specifications. The product of the system with the automaton of the specification has a hybrid state space with sparse rewards. We introduce a generalized optimal backup order, defined in reverse to a topological order over automaton states, that guides value backups and provably preserves optimality. We further present a model-free actor-critic algorithm whose policy evaluation solves a constrained optimization problem by the augmented Lagrangian method, yielding hyperparameter self-tuning, and prove its optimality and convergence in the tabular case. Since integer encodings of automaton states impose a spurious ordinal relationship on functions learned by one network, we dedicate a value and a policy network to each automaton state (modular learning). The algorithm matches or outperforms PPO, DQN, and A2C on CartPole, and on a Dubins car under a temporal specification the topological order and modular learning raise the success rate from 26.0% to 71.5%.

cs.AI↗

arXiv2026

Observation of a tripartite quantum phase for coexisting extended, localized, and critical states

The disordered quantum world hosts three fundamental types of states: extended, localized, and critical, of which the critical states are confined to fine-tuned critical points or mobility edges in randomly disordered systems. The tripartite phase, with all three types of states coexisting over finite spectral windows, represents a hallmark distinction between quasiperiodic and truly random systems in the localization physics. Here, we report the realization of this exotic phase in a quasi-periodically driven orbital optical lattice with ultracold atoms. The optical lattice with a quasiperiodic Floquet modulation coupling s and p orbitals is realized in experiment and shown to host the tripartite phase from exact theory. We develop a two-stage protocol to precisely prepare and detect the three types of quantum states. The characteristic exponents of these states are determined from expansion dynamics, showing their distinct universal transport properties. Our study marks a significant advancement in exploring unconventional critical phenomena and localization physics with ultracold atoms.

cond-mat.quant-gas↗

arXiv2025

A Radio-Frequency Emitter Design for the Low-Frequency Regime in Atomic Experiments

Radio-frequency (RF) control is a key technique in cold atom experiments. We present a compact and efficient RF circuit based on a capacitive transformer network, where a low-frequency coil operating up to 30MHz serves as both an intrinsic inductor and a power-sharing element. The design enables high current delivery and flexible impedance matching across a wide frequency range. We integrate both broadband and narrowband RF networks into a unified configuration that overcomes the geometric constraints imposed by the metallic chamber. In evaporative cooling, the broadband network allows a reduction of the applied RF input power from 14.7dBW to -3.5dBW, owing to its non-zero coil current even at ultra-low frequencies. This feature enables the Bose-Fermi mixture to be cooled below 10μK. In a Landau-Zener protocol, the coil driven by the narrowband network transfers 80% of rubidium atoms from |F = 2,mF = 2> to |2,-2> in 1 millisecond, achieving a Rabi frequency of approximately 9 kHz at an input power of 0.1dBW.

physics.atom-ph↗

arXiv2023

Towards Accurate Loop Closure Detection in Semantic SLAM with 3D Semantic Covisibility Graphs

Loop closure is necessary for correcting errors accumulated in simultaneous localization and mapping (SLAM) in unknown environments. However, conventional loop closure methods based on low-level geometric or image features may cause high ambiguity by not distinguishing similar scenarios. Thus, incorrect loop closures can occur. Though semantic 2D image information is considered in some literature to detect loop closures, there is little work that compares 3D scenes as an integral part of a semantic SLAM system. This paper introduces an approach, called SmSLAM+LCD, integrated into a semantic SLAM system to combine high-level 3D semantic information and low-level feature information to conduct accurate loop closure detection and effective drift reduction. The effectiveness of our approach is demonstrated in testing results.

cs.RO↗

arXiv2023

Autonomous Search of Semantic Objects in Unknown Environments

This paper addresses the problem of enabling a robot to search for a semantic object, i.e., an object with a semantic label, in an unknown and GPS-denied environment. For the robot in the unknown environment to detect and find the target semantic object, it must perform simultaneous localization and mapping (SLAM) at both geometric and semantic levels using its onboard sensors while planning and executing its motion based on the ever-updated SLAM results. In other words, the robot must be able to conduct simultaneous localization, semantic mapping, motion planning, and execution in real-time in the presence of sensing and motion uncertainty. This is an open problem as it combines semantic SLAM based on perception and real-time motion planning and execution under uncertainty. Moreover, the goals of the robot motion change on the fly depending on whether and how the robot can detect the target object. We propose a novel approach to tackle the problem, leveraging semantic SLAM, Bayesian Networks, Markov Decision Process, and Real-Time Dynamic Programming. The results in simulation and real experiments demonstrate the effectiveness and efficiency of our approach.

cs.RO↗

arXiv2023

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies. The aim is to extract the brain from raw imaging scans (i.e., extraction step), align it with a target brain image (i.e., registration step) and label the anatomical brain regions (i.e., segmentation step). Conventional studies typically focus on developing separate methods for the extraction, registration and segmentation tasks in a supervised setting. The performance of these methods is largely contingent on the quantity of training samples and the extent of visual inspections carried out by experts for error correction. Nevertheless, collecting voxel-level labels and performing manual quality control on high-dimensional neuroimages (e.g., 3D MRI) are expensive and time-consuming in many medical studies. In this paper, we study the problem of one-shot joint extraction, registration and segmentation in neuroimaging data, which exploits only one labeled template image (a.k.a. atlas) and a few unlabeled raw images for training. We propose a unified end-to-end framework, called JERS, to jointly optimize the extraction, registration and segmentation tasks, allowing feedback among them. Specifically, we use a group of extraction, registration and segmentation modules to learn the extraction mask, transformation and segmentation mask, where modules are interconnected and mutually reinforced by self-supervision. Empirical results on real-world datasets demonstrate that our proposed method performs exceptionally in the extraction, registration and segmentation tasks. Our code and data can be found at https://github.com/Anonymous4545/JERS

cs.CV↗

arXiv2022

ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data

Brain extraction and registration are important preprocessing steps in neuroimaging data analysis, where the goal is to extract the brain regions from MRI scans (i.e., extraction step) and align them with a target brain image (i.e., registration step). Conventional research mainly focuses on developing methods for the extraction and registration tasks separately under supervised settings. The performance of these methods highly depends on the amount of training samples and visual inspections performed by experts for error correction. However, in many medical studies, collecting voxel-level labels and conducting manual quality control in high-dimensional neuroimages (e.g., 3D MRI) are very expensive and time-consuming. Moreover, brain extraction and registration are highly related tasks in neuroimaging data and should be solved collectively. In this paper, we study the problem of unsupervised collective extraction and registration in neuroimaging data. We propose a unified end-to-end framework, called ERNet (Extraction-Registration Network), to jointly optimize the extraction and registration tasks, allowing feedback between them. Specifically, we use a pair of multi-stage extraction and registration modules to learn the extraction mask and transformation, where the extraction network improves the extraction accuracy incrementally and the registration network successively warps the extracted image until it is well-aligned with the target image. Experiment results on real-world datasets show that our proposed method can effectively improve the performance on extraction and registration tasks in neuroimaging data. Our code and data can be found at https://github.com/ERNetERNet/ERNet

cs.CV↗

arXiv2020

Semantic SLAM with Autonomous Object-Level Data Association

It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM). Such semantic information can enable a robot to better distinguish places with similar low-level geometric and visual features and perform high-level tasks that use semantic information about objects to be manipulated and environments to be navigated. While semantic SLAM has gained increasing attention, there is little research on semanticlevel data association based on semantic objects, i.e., object-level data association. In this paper, we propose a novel object-level data association algorithm based on bag of words algorithm, formulated as a maximum weighted bipartite matching problem. With object-level data association solved, we develop a quadratic-programming-based semantic object initialization scheme using dual quadric and introduce additional constraints to improve the success rate of object initialization. The integrated semantic-level SLAM system can achieve high-accuracy object-level data association and real-time semantic mapping as demonstrated in the experiments. The online semantic map building and semantic-level localization capabilities facilitate semantic-level mapping and task planning in a priori unknown environment.

cs.RO↗

arXiv2020

A Receding-Horizon MDP Approach for Performance Evaluation of Moving Target Defense in Networks

In this paper, we study the problem of assessing the effectiveness of a proactive defense-by-detection policy with a network-based moving target defense. We model the network system using a probabilistic attack graph--a graphical security model. Given a network system with a proactive defense strategy, an intelligent attacker needs to perform reconnaissance repeatedly to learn about the locations of intrusion detection systems and re-plan optimally to reach the target while avoiding detection. To compute the attacker's strategy for security evaluation, we develop a receding-horizon planning algorithm using a risk-sensitive Markov decision process with a time-varying reward function. Finally, we implement both defense and attack strategies in a synthetic network and analyze how the frequency of network randomization and the number of detection systems can influence the success rate of the attacker. This study provides insights for designing proactive defense strategies against online and multi-stage attacks by a resourceful attacker.

cs.CR↗