arXiv subjects

Zhuoran Sun

Publications and source records attributed to Zhuoran Sun.

5 recordsLinked to original sources

arXiv2026

Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting

Probabilistic forecasting of multivariate time series is challenging due to non-stationarity, inter-variable dependencies, and distribution shifts. While recent diffusion and flow matching models have shown promise, they often ignore informative priors such as conditional means and covariances. In this work, we propose Conditionally Whitened Generative Models (CW-Gen), a framework that incorporates prior information through conditional whitening. Theoretically, we establish sufficient conditions under which replacing the traditional terminal distribution of diffusion models, namely the standard multivariate normal, with a multivariate normal distribution parameterized by estimators of the conditional mean and covariance improves sample quality. Guided by this analysis, we design a novel Joint Mean-Covariance Estimator (JMCE) that simultaneously learns the conditional mean and sliding-window covariance. Building on JMCE, we introduce Conditionally Whitened Diffusion Models (CW-Diff) and extend them to Conditionally Whitened Flow Matching (CW-Flow). Experiments on five real-world datasets with six state-of-the-art generative models demonstrate that CW-Gen consistently enhances predictive performance, capturing non-stationary dynamics and inter-variable correlations more effectively than prior-free approaches. Empirical results further demonstrate that CW-Gen can effectively mitigate the effects of distribution shift.

stat.ML↗

arXiv2026

A Hybrid BLE-Wi-Fi Communication Architecture for Adaptive Imaging in Wireless Capsule Endoscopy

Wireless capsule endoscopy (WCE) is fundamentally constrained by limited wireless bandwidth, resulting in low imaging resolution and frame rate, which can cause motion blur and missed lesions. Although adaptive frame-rate schemes have been explored to accommodate transient gastrointestinal (GI) motility, these approaches typically require sacrificing image resolution. The use of higher-frequency communication bands is further limited by increased tissue attenuation. To address these challenges, we propose a hybrid Bluetooth Low Energy (BLE) and WiFi communication architecture that combines the low-power operation of BLE with the high data throughput of WiFi. We systematically evaluate the performance of BLE and WiFi under tissue-mimicking conditions by measuring throughput, received signal strength indicator (RSSI), and power consumption. The results demonstrate that amplified BLE with an adaptive transmission power control strategy provides a stable frame rate at low power consumption, while 2.4 GHz WiFi operating in station mode is the most suitable high-throughput communication configuration for WCE. Compared with WiFi, BLE reduces power consumption by approximately ten times, whereas WiFi achieves up to ten times higher throughput. To reconcile these complementary trade-offs, we further introduce a hybrid system with a frame-boundary-synchronized switching mechanism to ensure lossless data transmission during BLE and WiFi transitions. Experimental results show that the switching latency from BLE to WiFi is approximately 92.66 ms, which is longer than the WiFi-to-BLE switching latency of 15.49 ms when transmitting 10 kB image payloads. Overall, the proposed hybrid BLE and WiFi system enables robust, lossless, and energy-efficient mode switching, supports adaptive imaging, and advances the development of next-generation autonomous WCE platforms.

eess.SY↗

arXiv2026

Reevaluating Bluetooth Low Energy for Ingestible Electronics

Bluetooth Low Energy (BLE) is widely used in wearable devices but has rarely been considered for ingestible electronics because of severe tissue attenuation at 2.4 GHz. In this work, we systematically reevaluate BLE for ingestible applications by benchmarking it against representative Sub-GHz schemes in terms of tissue attenuation, power consumption, throughput, reliability, bidirectional communication, latency, and system integration. Water-phantom and in vivo measurements confirm stronger attenuation at 2.4 GHz, by approximately 40 dB; however, front-end-module (FEM)-assisted BLE compensates for this loss while maintaining practical received signal levels and energy efficiency. For ingestible sensing applications requiring below 100 kbps, BLE with a 20 dBm transmission power achieves lower power consumption than the evaluated Sub-GHz alternatives. BLE also provides protocol-level reliability through retransmission and in-order packet delivery, whereas Sub-GHz links can suffer packet loss under high attenuation. In addition, BLE supports bidirectional communication with limited throughput degradation, while the evaluated Sub-GHz implementation shows a substantial reduction. End-to-end measurements further demonstrate lower BLE latency because of its native compatibility with modern computing infrastructure. Finally, BLE offers advantages in antenna form factor, ecosystem support, and system integration. These results demonstrate that properly configured BLE is a practical communication solution for next-generation ingestible electronics.

eess.SY↗

arXiv2025

Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation

Parameter-Efficient Fine-Tuning (PEFT) is a technique that allows us to adapt powerful Foundation Models (FMs) to diverse downstream tasks while preserving and unleashing their inherent capabilities. However, we have observed that existing PEFT methods, which are often designed with natural imagery in mind, struggle when applied to Remote Sensing (RS) scenarios. This is primarily due to their inability to handle artifact influences, a problem particularly severe in RS image features. To tackle this challenge, we introduce Earth-Adapter, the first PEFT method specifically designed for RS artifacts conquering. Earth-Adapter introduces a novel Mixture of Frequency Adaptation process that combines a Mixture of Adapter (MoA) with Discrete Fourier Transformation (DFT). By utilizing DFT, Earth-Adapter can decompose features into different frequency components, precisely separating artifacts from original features. The MoA then dynamically assigns weights to each adapter expert, allowing for the combination of features across various frequency domains. These simple-yet-effective approaches enable Earth-Adapter to more efficiently overcome the disturbances caused by artifacts than previous PEFT methods, significantly enhancing the FMs' performance on RS scenarios. Experiments on Domain Adaptation (DA), and Domain Generalization (DG) semantic segmentation benchmarks showcase the Earth-Adapter's effectiveness. Compared with baseline Rein, Earth-Adapter significantly improves 9.0% mIoU in DA and 3.1% mIoU in DG benchmarks. Our code will be released at https://github.com/VisionXLab/Earth-Adapter.

cs.CV↗

arXiv2024

Conditional Diffusion Models Based Conditional Independence Testing

Conditional independence (CI) testing is a fundamental task in modern statistics and machine learning. The conditional randomization test (CRT) was recently introduced to test whether two random variables, $X$ and $Y$, are conditionally independent given a potentially high-dimensional set of random variables, $Z$. The CRT operates exceptionally well under the assumption that the conditional distribution $X|Z$ is known. However, since this distribution is typically unknown in practice, accurately approximating it becomes crucial. In this paper, we propose using conditional diffusion models (CDMs) to learn the distribution of $X|Z$. Theoretically and empirically, it is shown that CDMs closely approximate the true conditional distribution. Furthermore, CDMs offer a more accurate approximation of $X|Z$ compared to GANs, potentially leading to a CRT that performs better than those based on GANs. To accommodate complex dependency structures, we utilize a computationally efficient classifier-based conditional mutual information (CMI) estimator as our test statistic. The proposed testing procedure performs effectively without requiring assumptions about specific distribution forms or feature dependencies, and is capable of handling mixed-type conditioning sets that include both continuous and discrete variables. Theoretical analysis shows that our proposed test achieves a valid control of the type I error. A series of experiments on synthetic data demonstrates that our new test effectively controls both type-I and type-II errors, even in high dimensional scenarios.

stat.ML↗