arXiv subjects

Yuting Zhu

Publications and source records attributed to Yuting Zhu.

At least 19 recordsLinked to original sources

arXiv2026

Optimal filtering for a giant cavity in waveguide QED systems

In waveguide quantum electrodynamics (QED) systems, a giant cavity can be engineered to interact with quantum fields by multiple distant coupling points so that its non-Markovian dynamics are quite different from traditional quantum optical cavity systems. Towards feedback control this system, this paper designs an optimal filter for the giant cavity systems to estimate its state evolution under continuous quantum measurements. Firstly, the Langevin equation in the Heisenberg picture are derived, which is a linear continuous-time system with both states and inputs delays resulting from the unconventional distant couplings. Compared to existing modeling approaches, this formulation effectively preserves the nonlocal coupling and multiple delay dynamic characteristics inherent in the original system. In particular, the presence of coupling and propagation delays leads to noncommutativity among the system operators at different times, which prevents the direct application of existing quantum filtering methods. To address this issue, an optimal filter is designed, in which the delayed-state covariance matrices are computed. By iteratively evaluating the delayed-state covariance over successive time intervals, the resulting optimal filter can be implemented in an interval-wise backward recursion algorithm. Finally, numerical simulations are conducted to evaluate the tracking performance of the proposed optimal filter for the giant cavity. By comparing between the evolutions of Wigner functions of coherent and cat states and the filter, the effectiveness of the optimal filter is validated.

quant-ph↗

arXiv2025

Positive effects and mechanisms of simulated lunar low-magnetic environment on earthworm-improved lunar soil simulant as a cultivation substrate

With the advancement of crewed deep-space missions, Bioregenerative Life Support Systems (BLSS) for lunar bases face stresses from lunar environmental factors. While microgravity and radiation are well-studied, the low-magnetic field's effects remain unclear. Earthworms ("soil scavengers") improve lunar soil simulant and degrade plant waste, as shown in our prior studies. We tested earthworms in lunar soil simulant mixed with organic waste (from "Lunar Palace 365" experiment) under three magnetic conditions: lunar-low, Earth, and high. Stronger fields increased earthworm oxidative stress (MDA) and impaired neurotransmitters. Weaker fields enhanced substrate cultivability: neutralized pH, increased nutrients, humus, and wheat seedling rate. Microbial analyses showed: (1) Higher fungal Shannon index under high fields indicated impaired digestion; (2) More positive correlations in gut networks suggested slower microbial cooperation (e.g., lignocellulose degradation); (3) Reduced Network Size, Path Length and Modularity confirmed disrupted interactions. This disproves lunar low-magnetic stress on earthworm-soil-waste systems, aiding deep-space BLSS research.

physics.geo-ph↗

arXiv2025

SARLANG-1M: A Benchmark for Vision-Language Modeling in SAR Image Understanding

Synthetic Aperture Radar (SAR) is a crucial remote sensing technology, enabling all-weather, day-and-night observation with strong surface penetration for precise and continuous environmental monitoring and analysis. However, SAR image interpretation remains challenging due to its complex physical imaging mechanisms and significant visual disparities from human perception. Recently, Vision-Language Models (VLMs) have demonstrated remarkable success in RGB image understanding, offering powerful open-vocabulary interpretation and flexible language interaction. However, their application to SAR images is severely constrained by the absence of SAR-specific knowledge in their training distributions, leading to suboptimal performance. To address this limitation, we introduce SARLANG-1M, a large-scale benchmark tailored for multimodal SAR image understanding, with a primary focus on integrating SAR with textual modality. SARLANG-1M comprises more than 1 million high-quality SAR image-text pairs collected from over 59 cities worldwide. It features hierarchical resolutions (ranging from 0.1 to 25 meters), fine-grained semantic descriptions (including both concise and detailed captions), diverse remote sensing categories (1,696 object types and 16 land cover classes), and multi-task question-answering pairs spanning seven applications and 1,012 question types. Extensive experiments on mainstream VLMs demonstrate that fine-tuning with SARLANG-1M significantly enhances their performance in SAR image interpretation, reaching performance comparable to human experts. The dataset and code will be made publicly available at https://github.com/Jimmyxichen/SARLANG-1M.

cs.CV↗

arXiv2024

The Power of Linear Programming in Sponsored Listings Ranking: Evidence from a Large-Scale Field Experiment

Sponsored product advertisements constitute a major revenue source for online marketplaces such as Amazon, Walmart, and Alibaba. A key operational challenge in these systems lies in the Sponsored Listings Ranking (SLR) problem, that is, determining which items to include and how to rank them to balance short-term revenue with long-term relevance and user experience. Industry practice predominantly relies on score-based algorithms, which construct heuristic composite scores to rank items efficiently within strict real-time latency constraints. However, such methods offer limited control over objective trade-offs and cannot readily accommodate additional operational constraints. We propose and evaluate a Linear Programming (LP)-based algorithm as a principled alternative to score-based approaches. We first formulate the SLR problem as a constrained mixed integer programming (MIP) model and develop a dual-based algorithm that approximately solves its LP relaxation within 0.1 second, satisfying production-level latency requirements. In collaboration with a leading online marketplace, we conduct a 19-day field experiment encompassing approximately 329 million impressions. The LP-based algorithm significantly outperforms the industry-standard benchmark in key marketplace metrics, demonstrating both higher revenue and maintained relevance. Mechanism analyses reveal that the performance gains are most pronounced when the revenue-relevance tradeoff is stronger. Our framework also generalizes to settings with inventory, sales, or fairness constraints, offering a flexible and deployable optimization paradigm. The LP-based algorithm was deployed in production at our partner marketplace in January 2023, marking a rare large-scale implementation of a mathematically grounded ranking algorithm in real-world online advertising.

econ.GN↗

arXiv2024

Quantum gyroscopes based on double-mode surface-acoustic-wave cavities

Recent progress shows that a surface-acoustic-wave (SAW) cavity can not only induce quantum acoustic dynamics but also can form optomechanical-like systems. Its operating frequencies in the microwave band make it resistant to the thermal noise of surrounding environments, while its radiation-pressure couplings make it susceptible to weak forces. Based on these advantages, we propose a gyroscope comprising coupled microwave-SAW cavities. In this paper, we systematically consider the three indices including range, signal-to-noise ratio, and sensitivity, which are the most important to gyroscopes but only partially considered in existing works. Additionally, we establish the fundamental limits of sensitivity when the quantum input is in the vacuum state and the squeezed vacuum state. We find that squeezing improves sensitivity and can surpass the standard quantum limit. However, this improvement can only reach up to $\sqrt{2}/2$ even as the squeezed parameter approaches infinity, which is rarely noted in recent works. Finally, we also offer analytical constraints for cooperativity and squeezed parameters. These constraints can be utilized to design gyroscopes based on coupled cavities in experiments.

quant-ph↗

arXiv2023

Optimizing Scalable Targeted Marketing Policies with Constraints

Targeted marketing policies target different customers with different marketing actions. While most research has focused on training targeting policies without managerial constraints, in practice, many firms face managerial constraints when implementing these policies. For example, firms may face volume constraints on the maximum or minimum number of actions they can take, or on the minimum acceptable outcomes for different customer segments. They may also face similarity (fairness) constraints that require similar actions with different groups of customers. Traditional optimization methods face challenges when solving problems with either many customers or many constraints. We show how recent advances in linear programming can be adapted to the targeting of marketing actions. We provide a theoretical guarantee comparing how the proposed algorithm scales compared to state-of-the-art benchmarks (primal simplex, dual simplex and barrier methods). We also extend existing guarantees on optimality and computation speed, by adapting them to accommodate the characteristics of targeting problems. We implement the proposed algorithm using data from a field experiment with over 2 million customers, and six different marketing actions (including a no action ``Control''). We use this application to evaluate the computation speed and range of problems the algorithm can solve, comparing it to benchmark methods. The findings confirm that the algorithm makes it feasible to train large-scale targeting problems that include volume and similarity constraints.

math.OC↗

arXiv2023

Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising

Deep learning is a very promising technique for low-dose computed tomography (LDCT) image denoising. However, traditional deep learning methods require paired noisy and clean datasets, which are often difficult to obtain. This paper proposes a new method for performing LDCT image denoising with only LDCT data, which means that normal-dose CT (NDCT) is not needed. We adopt a combination including the self-supervised noise2noise model and the noisy-as-clean strategy. First, we add a second yet similar type of noise to LDCT images multiple times. Note that we use LDCT images based on the noisy-as-clean strategy for corruption instead of NDCT images. Then, the noise2noise model is executed with only the secondary corrupted images for training. We select a modular U-Net structure from several candidates with shared parameters to perform the task, which increases the receptive field without increasing the parameter size. The experimental results obtained on the Mayo LDCT dataset show the effectiveness of the proposed method compared with that of state-of-the-art deep learning methods. The developed code is available at https://github.com/XYuan01/Self-supervised-Noise2Noise-for-LDCT.

eess.IV↗

arXiv2022

Synthesis of the Supremal Covert Attacker Against Unknown Supervisors by Using Observations

In this paper, we consider the problem of synthesizing the supremal covert damage-reachable attacker, in the setup where the model of the supervisor is unknown to the adversary but the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system. The synthesized attacker needs to ensure both the damage-reachability and the covertness against all the supervisors which are consistent with the given set of observations. There is a gap between the de facto supremality, assuming the model of the supervisor is known, and the supremality that can be attained with a limited knowledge of the model of the supervisor, from the adversary's point of view. We consider the setup where the attacker can exercise sensor replacement/deletion attacks and actuator enablement/disablement attacks. The solution methodology proposed in this work is to reduce the synthesis of the supremal covert damage-reachable attacker, given the model of the plant and the finite set of observations, to the synthesis of the supremal safe supervisor for certain transformed plant, which shows the decidability of the observation-assisted covert attacker synthesis problem. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.

eess.SY↗

arXiv2021

Spatial Non-Locality Induced Non-Markovian EIT in a Single Giant Atom

In recent experiments, electromagnetically induced transparency (EIT) were observed with giant atoms, but nothing unconventional were found from the transmission spectra. In this letter, we show that unconventional EIT does exist in giant atoms, and indicate why it has not been observed so far. Different from these existing works, this letter presents a consistent theory including a real space method and a time delayed master equation for observing unconventional EIT. We discover that this phenomenon is a quantum effect which cannot be correctly described in a semi-classical way as those in recent works. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom, which is crucial for a future experimental observation. This new phenomenon results from inherent non-locality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time. Our theory establishes a framework for application of nonlocal systems to quantum information processing.

quant-ph↗

arXiv2021

Observation-Assisted Heuristic Synthesis of Covert Attackers Against Unknown Supervisors

In this work, we address the problem of synthesis of covert attackers in the setup where the model of the plant is available, but the model of the supervisor is unknown, to the adversary. To compensate the lack of knowledge on the supervisor, we assume that the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system, which can be used for assisting the synthesis. We present a heuristic algorithm for the synthesis of covert damage-reachable attackers, based on the model of the plant and the (finite) set of observations, by a transformation into solving an instance of the partial-observation supervisor synthesis problem. The heuristic algorithm developed in this paper may allow the adversary to synthesize covert attackers without having to know the model of the supervisor, which could be hard to obtain in practice. For simplicity, we shall only consider covert attackers that are able to carry out sensor replacement attacks and actuator disablement attacks. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.

eess.SY↗

arXiv2021

Synthesis of Maximally Permissive Covert Attackers Against Unknown Supervisors by Using Observations

In this paper, we consider the problem of synthesis of maximally permissive covert damage-reachable attackers in the setup where the model of the supervisor is unknown to the adversary but the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system. The synthesized attacker needs to ensure both the damage-reachability and the covertness against all the supervisors which are consistent with the given set of observations. There is a gap between the de facto maximal permissiveness, assuming the model of the supervisor is known, and the maximal permissiveness that can be attained with a limited knowledge of the model of the supervisor, from the adversary's point of view. We consider the setup where the attacker can exercise sensor replacement/deletion attacks and actuator enablement/disablement attacks. The solution methodology proposed in this work is to reduce the synthesis of maximally permissive covert damage-reachable attackers, given the model of the plant and the finite set of observations, to the synthesis of maximally permissive safe supervisors for certain transformed plant, which shows the decidability of the observation-assisted covert attacker synthesis problem. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.

eess.SY↗

arXiv2021

Privacy-Preserving Co-synthesis Against Sensor-Actuator Eavesdropping Intruder

In this work, we investigate the problem of privacy-preserving supervisory control against an external passive intruder via co-synthesis of dynamic mask, edit function, and supervisor for opacity enforcement and requirement satisfaction. We attempt to achieve the following goals: 1) the system secret cannot be inferred by the intruder, i.e., opacity of secrets against the intruder, and the existence of the dynamic mask and the edit function should not be discovered by the intruder, i.e., covertness of dynamic mask and edit function against the intruder; 2) the closed-loop system behaviors should satisfy some safety and nonblockingness requirement. We assume the intruder can eavesdrop both the sensing information generated by the sensors and the control commands issued to the actuators, and we refer to such an intruder as a sensor-actuator eavesdropping intruder. Our approach is to model the co-synthesis problem as a distributed supervisor synthesis problem in the Ramadge-Wonham supervisory control framework, and we propose an incremental synthesis heuristic to incrementally synthesize a dynamic mask, an edit function, and a supervisor, which consists of three steps: 1) we first synthesize an ensemble ME of dynamic mask and edit function to ensure the opacity and the covertness against a sensor eavesdropping but command non-eavesdropping intruder, and marker-reachability; 2) we then decompose ME into a dynamic mask and an edit function by using a constraint-based approach, with the help of a Boolean satisfiability (SAT) solver; 3) finally, we synthesize a supervisor such that opacity and covertness can be ensured against the sensor-actuator eavesdropping intruder, and at the same time safety and nonblockingness requirement can be ensured. The effectiveness of our approach is illustrated on an example about the enforcement of location privacy for an autonomous vehicle.

eess.SY↗

arXiv2021

Privacy-Preserving Supervisory Control of Discrete-Event Systems via Co-Synthesis of Edit Function and Supervisor for Opacity Enforcement and Requirement Satisfaction

This paper investigates the problem of co-synthesis of edit function and supervisor for opacity enforcement in the supervisory control of discrete-event systems (DES), assuming the presence of an external (passive) intruder, where the following goals need to be achieved: 1) the external intruder should never infer the system secret, i.e., the system is opaque, and never be sure about the existence of the edit function, i.e., the edit function remains covert; 2) the controlled plant behaviors should satisfy some safety and nonblockingness requirements, in the presence of the edit function. We focus on the class of edit functions that satisfy the following properties: 1) the observation capability of the edit function in general can be different from those of the supervisor and the intruder; 2) the edit function can implement insertion, deletion, and replacement operations; 3) the edit function performs bounded edit operations, i.e., the length of each string output of the edit function is upper bounded by a given constant. We propose an approach to solve this co-synthesis problem by modeling it as a distributed supervisor synthesis problem in the Ramadge-Wonham supervisory control framework. By taking the special structure of this distributed supervisor synthesis problem into consideration and to improve the possibility of finding a non-empty distributed supervisor, we propose two novel synthesis heuristics that incrementally synthesize the supervisor and the edit function. The effectiveness of our approach is illustrated on an example in the enforcement of the location privacy.

eess.SY↗

arXiv2021

Networked Supervisor Synthesis Against Lossy Channels with Bounded Network Delays as Non-Networked Synthesis

In this work, we study the problem of supervisory control of networked discrete event systems. We consider lossy communication channels with bounded network delays, for both the control channel and the observation channel. By a model transformation, we transform the networked supervisor synthesis problem into the classical (non-networked) supervisor synthesis problem (for non-deterministic plants), such that the existing supervisor synthesis tools can be used for synthesizing networked supervisors. In particular, we can use the (state-based) normality property for the synthesis of the supremal networked supervisors, whose existence is guaranteed by construction due to our consideration of command non-deterministic supervisors. The effectiveness of our approach is illustrated on a mini-guideway example that is adapted from the literature, for which the supremal networked supervisor has been synthesized in the synthesis tools SuSyNA and TCT.

eess.SY↗

arXiv2021

Synthesis of Covert Sensor Attacks in Networked Discrete-Event Systems with Non-FIFO Channels

In this paper, we investigate the covert sensor attack synthesis problem in the framework of supervisory control of networked discrete-event systems (DES), where the observation channel and the control channel are assumed to be non-FIFO and have bounded network delays. We focus on the class of sensor attacks satisfying the following properties: 1) the attacker might not have the same observation capability as the networked supervisor; 2) the attacker aims to remain covert, i.e., hide its presence against the networked monitor; 3) the attacker could insert, delete, or replace compromised observable events; 4) it performs bounded sensor attacks, i.e., the length of each string output of the sensor attacker is upper bounded by a given constant. The solution methodology proposed in this work is to solve the covert sensor attack synthesis problem for networked DES by modeling it as the well studied Ramadge-Wonham supervisor synthesis problem, and the constructions work for both the damage-reachable attacks and the damage-nonblocking attacks. In particular, we show the supremal covert sensor attack exists in the networked setup and can be effectively computed by using the normality property based synthesis approach.

eess.SY↗

arXiv2020

Overview of Networked Supervisory Control with Imperfect Communication Channels

This paper presents an overview of the networked supervisory control framework for discrete event systems with imperfect communication networks, which can be divided into the centralized supervisory control setup and the decentralized supervisory control setup. We review the state-of-art networked control frameworks with observation channel delays and control channel delays, for untimed and timed models. Data losses in communication channels are also considered. The review of the state-of-art networked control frameworks will be focused on the following parts: 1) the construction of the networked control closed-loop system 2) the condition to ensure the existence of a networked supervisor 3) the synthesis procedure for networked-delay resilient supervisor 4) the possibility of improving the synthesis efficiency.

eess.SY↗

arXiv2019

Synthesis of Covert Actuator Attackers for Free

In this paper, we shall formulate and address a problem of covert actuator attacker synthesis for cyber-physical systems that are modelled by discrete-event systems. We assume the actuator attacker partially observes the execution of the closed-loop system and is able to modify each control command issued by the supervisor on a specified attackable subset of controllable events. We provide straightforward but in general exponential-time reductions, due to the use of subset construction procedure, from the covert actuator attacker synthesis problems to the Ramadge-Wonham supervisor synthesis problems. It then follows that it is possible to use the many techniques and tools already developed for solving the supervisor synthesis problem to solve the covert actuator attacker synthesis problem for free. In particular, we show that, if the attacker cannot attack unobservable events to the supervisor, then the reductions can be carried out in polynomial time. We also provide a brief discussion on some other conditions under which the exponential blowup in state size can be avoided. Finally, we show how the reduction based synthesis procedure can be extended for the synthesis of successful covert actuator attackers that also eavesdrop the control commands issued by the supervisor.

eess.SY↗

arXiv2018

Supervisor Obfuscation Against Actuator Enablement Attack

In this paper, we propose and address the problem of supervisor obfuscation against actuator enablement attack, in a common setting where the actuator attacker can eavesdrop the control commands issued by the supervisor. We propose a method to obfuscate an (insecure) supervisor to make it resilient against actuator enablement attack in such a way that the behavior of the original closed-loop system is preserved. An additional feature of the obfuscated supervisor, if it exists, is that it has exactly the minimum number of states among the set of all the resilient and behavior-preserving supervisors. Our approach involves a simple combination of two basic ideas: 1) a formulation of the problem of computing behavior-preserving supervisors as the problem of computing separating finite state automata under controllability and observability constraints, which can be efficiently tackled by using modern SAT solvers, and 2) the use of a recently proposed technique for the verification of attackability in our setting, with a normality assumption imposed on both the actuator attackers and supervisors.

eess.SY↗