arXiv subjects

Qiushi Chen

Publications and source records attributed to Qiushi Chen.

9 recordsLinked to original sources

arXiv2026

Sat3R: Satellite DSM Reconstruction via RPC-Aware Depth Fine-tuning

Accurate Digital Surface Model (DSM) reconstruction from satellite imagery is critical for applications such as disaster response, urban planning, and large-scale geographic mapping. Existing approaches face a fundamental trade-off: optimization-based methods achieve strong accuracy but require hours of per-scene computation, while generalizable geometry foundation models offer near-instant inference but fail to generalize to satellite imagery due to the domain gap introduced by the Rational Polynomial Camera (RPC) model and mismatched depth scale distributions. We present Sat3R, a feed-forward framework that bridges this gap via RPC-aware metric depth fine-tuning of Depth Anything V2 using the Scale-Invariant Logarithmic (SiLog) loss. By constructing physically consistent pseudo depth supervision from RPC geometry, Sat3R adapts a monocular depth foundation model to the satellite domain without per-scene optimization. Experiments on the DFC2019 benchmark demonstrate that Sat3R reduces MAE by 38% over zero-shot feed-forward baselines and achieves competitive accuracy against optimization-based methods, while delivering over 300x speedup. Sat3R demonstrates that feed-forward models, when properly adapted to the satellite domain, can match optimization-based accuracy at a fraction of the computational cost, paving the way for practical large-scale satellite DSM reconstruction.

cs.CV↗

arXiv2026

A Bayesian Multi-State Data Integration Approach for Estimating County-level Prevalence of Opioid Misuse in the United States

Drug overdose deaths, including from opioids, remain a significant public health threat to the United States (US). To abate the harms of opioid misuse, understanding its prevalence at the local level is crucial for stakeholders in communities to develop response strategies that effectively use limited resources. Although there exist several state-specific studies that provide county-level prevalence estimates, such estimates are not widely available across the country, as the datasets used in these studies are not always readily available in other states, which, therefore, has limited the wider applications of existing models. To fill this gap, we propose a Bayesian multi-state data integration approach that fully utilizes publicly available data sources to estimate county-level opioid misuse prevalence for all counties in the US. The hierarchical structure jointly models opioid misuse prevalence and overdose death outcomes, leverages existing county-level prevalence estimates in limited states and state-level estimates from national surveys, and accounts for heterogeneity across counties and states with counties' covariates and mixed effects. Furthermore, our parsimonious and generalizable modeling framework employs horseshoe+ prior to flexibly shrink coefficients and prevent overfitting, ensuring adaptability as new county-level prevalence data in additional states become available. Using real-world data, our model shows high estimation accuracy through cross-validation and provides nationwide county-level estimates of opioid misuse for the first time.

stat.AP↗

arXiv2026

Computationally Efficient Estimation of Localized Treatment Effects for Multi-Level, Multi-Component Interventions to Address the Opioid Crisis

The opioid epidemic remains a major public health challenge in the United States, requiring a multi-pronged intervention approach to mitigate harms to communities. Given the heterogeneity of the epidemic, it is crucial for policymakers to understand localized treatment effects of different intervention components and utilize limited resources efficiently. While locally calibrated simulation models can project epidemic outcomes for any given intervention policy, collecting simulation results for all intervention combinations to estimate localized treatment effects for each community is impractical because the number of combinations grows exponentially with the number of interventions and the levels at which they are applied. To tackle this, we develop a two-stage metamodel framework with a two-step sequential design for efficient sampling. The metamodel consists of a response function linking health outcomes to each intervention component's treatment effect, and a Gaussian process regression (GPR) to learn spatial and socio-economic structures of the treatment effects based on locally-contextualized covariates. With two-step sequential sampling, we leverage spatial correlations and posterior uncertainty to sequentially sample the most informative counties and treatment conditions. We apply this framework to estimate the treatment effects of buprenorphine dispensing and naloxone distribution on overdose mortality rates using a calibrated agent-based opioid epidemic model in Pennsylvania counties. Our approach achieves less than 5% average relative error using fewer than 2% of the runs required for an exhaustive simulation. Our two-stage framework provides a computationally efficient approach to support policymakers, enabling an efficient evaluation of alternative resource-allocation strategies to mitigate the opioid epidemic in local communities.

stat.AP↗

arXiv2025

Scalable Optical Links for Controlling Bosonic Quantum Processors

Superconducting quantum computing has the potential to revolutionize computational capabilities. However, scaling up large quantum processors is limited by the cumbersome and heat-conductive electronic cables that connect room-temperature control electronics to quantum processors, leading to significant signal attenuation. Optical fibers provide a promising solution, but their use has been restricted to controlling simple two-level quantum systems over short distances. Here, we demonstrate optical control of a bosonic quantum processor, achieving universal operations on the joint Hilbert space of a transmon qubit and a storage cavity. Using an array of cryogenic fiber-integrated uni-traveling-carrier photodiodes, we prepare Fock states containing up to ten photons. Additionally, remote control of bosonic modes over a transmission distance of 15 km has been achieved, with fidelities exceeding 95%. The combination of high-dimensional quantum control, multi-channel operation, and long-distance transmission addresses the key requirements for scaling superconducting quantum computers and enables architectures for distributed quantum data centers.

quant-ph↗

arXiv2025

Efficient and tunable frequency conversion using periodically poled thin-film lithium tantalate nanowaveguides

Thin-film lithium tantalate (TFLT) has recently emerged as a promising photonic platform for chip-scale nonlinear optics due to its weaker photorefraction, higher optical damage threshold, broader transparency window, and lower birefringence compared to that of thin-film lithium niobate. Here we develop an ultralow-loss lithium tantalate integrated photonic platform and report the first functional second harmonic generator based on high-fidelity poling of z-cut TFLT. As a result, quasi-phase matching (QPM) is performed between telecom (1550 nm) and near-visible (775 nm) wavelengths in a straight waveguide and prompts strong second-harmonic generation with a normalized efficiency of 229 %/W/$cm^2$. An absolute conversion efficiency of 5.5 % is achieved with a pump power of 700 mW. Such a second-harmonic generator exhibits stable temperature tunability (-0.44 nm/$^\circ C$) which is important for applications that require precise frequency alignment such as atomic clocks and quantum frequency conversion.

physics.optics↗

arXiv2024

A chip-integrated comb-based microwave oscillator

Low-noise microwave oscillators are cornerstones for wireless communication, radar and clocks. Optical frequency combs have enabled photonic microwaves with unrivalled noise performance and bandwidth. Emerging interest is to generate microwaves using chip-based frequency combs, namely microcombs. Here, we demonstrate the first, fully integrated, microcomb-based, microwave oscillator chip. The chip, powered by a microelectronic circuit, leverages hybrid integration of a DFB laser, a nonlinear microresonator, and a high-speed photodetector. Each component represents the best of its own class, yet allows large-volume manufacturing with low cost in CMOS foundries. The hybrid chip outputs an ultralow-noise laser of 6.9 Hz linewidth, a microcomb of 10.7 GHz repetition rate, and a 10.7 GHz microwave of 6.3 mHz linewidth -- all three in one entity of 76 mm$^2$ size.The microwave phase noise reaches -75/-105/-130 dBc/Hz at 1/10/100 kHz Fourier offset frequency. Our results can reinvigorate our information society for communication, sensing, timing and precision measurement.

physics.optics↗

arXiv2022

A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism

Short-term traffic flow prediction is a vital branch of the Intelligent Traffic System (ITS) and plays an important role in traffic management. Graph convolution network (GCN) is widely used in traffic prediction models to better deal with the graphical structure data of road networks. However, the influence weights among different road sections are usually distinct in real life, and hard to be manually analyzed. Traditional GCN mechanism, relying on manually-set adjacency matrix, is unable to dynamically learn such spatial pattern during the training. To deal with this drawback, this paper proposes a novel location graph convolutional network (Location-GCN). Location-GCN solves this problem by adding a new learnable matrix into the GCN mechanism, using the absolute value of this matrix to represent the distinct influence levels among different nodes. Then, long short-term memory (LSTM) is employed in the proposed traffic prediction model. Moreover, Trigonometric function encoding is used in this study to enable the short-term input sequence to convey the long-term periodical information. Ultimately, the proposed model is compared with the baseline models and evaluated on two real word traffic flow datasets. The results show our model is more accurate and robust on both datasets than other representative traffic prediction models.

cs.LG↗

arXiv2021

Optimal Control to Handle Variations in Biomass Feedstock Characteristics and Reactor In-Feed Rate

The variations in feedstock characteristics such as moisture and particle size distribution lead to an inconsistent flow of feedstock from the biomass pre-processing system to the reactor in-feed system. These inconsistencies result in low on-stream times at the reactor in-feed equipment. This research develops an optimal process control method for a biomass pre-processing system comprised of milling and densification operations to provide the consistent flow of feedstock to a reactor's throat. This method uses a mixed-integer optimization model to identify optimal bale sequencing, equipment in-feed rate, and buffer location and size in the biomass pre-processing system. This method, referred to as the hybrid process control (HPC), aims to maximize throughput over time. We compare HPC with a baseline feed forward process control. Our case study based on switchgrass finds that HPC reduces the variation of a reactor's feeding rate by 100\% without increasing the operating cost of the biomass pre-processing system for biomass with moisture ranging from 10 to 25\%. A biorefinery can adapt HPC to achieve its design capacity.

math.OC↗

arXiv2021

Optimization Models for Integrated Biorefinery Operations

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US's cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system's reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. The Sample Average Approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory's pilot biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution increase utilization of the reactor and reduce operational costs.

math.OC↗