arXiv subjects

Zhang Hao

Publications and source records attributed to Zhang Hao.

4 recordsLinked to original sources

arXiv2026

Testing the Validity of Instrumental Variable Sets in Causal Additive Models with Non-Constant Effects

Instrumental variable (IV) methods are powerful for causal effect estimation with unmeasured confounding, but in practice researchers often face a set of candidate IVs whose validity is difficult to determine from observational data. This paper studies the problem of testing the validity of IV sets under Causal Additive Models with Non-Constant Effects (CAM-NCE). To address this problem, we propose a testable condition, termed the Cross Auxiliary-based independence Test (CAT) condition, for assessing IV set validity from observational data. We show that, under the completeness condition, if the CAT condition is violated, the corresponding set cannot be a valid IV set. Furthermore, under a cross distributional non-degeneracy condition, we establish that the CAT condition becomes both necessary and sufficient for characterizing valid IV sets under CAM-NCE. We then extend the CAT condition to settings with covariates and develop a practical finite-sample algorithm for testing the validity of candidate IV sets. Extensive experiments on synthetic data and three real-world datasets demonstrate the effectiveness and practical utility of the proposed method.

stat.ME↗

arXiv2026

Mud-Standoff Effect Correction Based on Open-Short Calibration and Resistivity Consistency-Constrained Iterative Inversion for Oil-Based Mud Imagers

We propose a mud-standoff effect correction method and a set of approximate apparent resistivity inversion methods suitable for oil-based mud micro-resistivity imaging logging. To calibrate the influence of the mud layer on electrode measurement signals, this study integrates the Open-Short calibration(OSC) method with the three-layer impedance model of the oil-based mud resistivity imager. By treating the electrode and the mud layer as an integrated whole and simulating the open/short-circuit states via the finite element method, the independent extraction of the mud layer impedance signal is achieved, and the formation impedance signal is separated from the total impedance. For fast inversion of formation resistivity, standoff thickness (mud layer thickness), and relative permittivity of formation, a resistivity consistency-constrained iterative inversion method is further proposed. In this method, the formation impedance is first converted into resistivity, and then the consistency residual of the resistivity at different frequencies is used as the objective function to invert the approximate apparent resistivity of the formation through an iterative optimization algorithm. The effectiveness of the finite element-simulated OSC method, the objective function construction scheme, and the consistency inversion method is verified through both numerical models and an example of field data.

physics.geo-ph↗

arXiv2024

Training-Free Action Recognition and Goal Inference with Dynamic Frame Selection

We introduce VidTFS, a Training-free, open-vocabulary video goal and action inference framework that combines the frozen vision foundational model (VFM) and large language model (LLM) with a novel dynamic Frame Selection module. Our experiments demonstrate that the proposed frame selection module improves the performance of the framework significantly. We validate the performance of the proposed VidTFS on four widely used video datasets, including CrossTask, COIN, UCF101, and ActivityNet, covering goal inference and action recognition tasks under open-vocabulary settings without requiring any training or fine-tuning. The results show that VidTFS outperforms pretrained and instruction-tuned multimodal language models that directly stack LLM and VFM for downstream video inference tasks. Our VidTFS with its adaptability shows the future potential for generalizing to new training-free video inference tasks.

cs.CV↗

arXiv2020

Deep Learning for MIMO Channel Estimation: Interpretation, Performance, and Comparison

Deep learning (DL) has emerged as an effective tool for channel estimation in wireless communication systems, especially under some imperfect environments. However, even with such unprecedented success, DL methods are often regarded as black boxes and are lack of explanations on their internal mechanisms, which severely limits further improvement and extension. In this paper, we present a preliminary theoretical analysis on DL based channel estimation for multiple-antenna systems to understand and interpret its internal mechanism. Deep neural network (DNN) with rectified linear unit (ReLU) activation function is mathematically equivalent to a piecewise linear function. Hence, the corresponding DL estimator can achieve universal approximation to a large family of functions by making efficient use of piecewise linearity. We demonstrate that DL based channel estimation does not restrict to any specific signal model and approaches to the minimum mean-squared error (MMSE) estimation in various scenarios without requiring any prior knowledge of channel statistics. Therefore, DL based channel estimation outperforms or is at least comparable with traditional channel estimation, depending on the types of channels. Simulation results confirm the accuracy of the proposed interpretation and demonstrate the effectiveness of DL based channel estimation under both linear and nonlinear signal models.

eess.SP↗