arXiv subjects

William Xu

Publications and source records attributed to William Xu.

4 recordsLinked to original sources

arXiv2026

Sensorimotor features of a reversal learning task bias decision behavior without disrupting individual difference structure

In computational psychiatry, task-irrelevant factors such as a task's perceptual and motor features are typically assumed not to bias decision behavior, but at most to add noise. Yet growing evidence links sensorimotor processing to decision-making through multiple pathways, challenging this assumption. We tested this directly using a two-choice probabilistic reversal learning task completed by 90 participants under two sensorimotor conditions: a stationary condition requiring only arm movements to respond, and an active condition requiring participants to walk between physically separated computers. Task performance, measured as the propensity to choose the more probable rewarding option, did not differ between conditions. However, stay rate (the tendency to repeat the previous choice) was significantly elevated in the stationary condition, but only among participants who completed the active condition first; those who completed the stationary condition first showed no such difference. Logistic regression weights capturing the influence of recent win history on choice showed the same pattern, with stationary-condition elevation restricted to the active-first group. Cross-condition correlations for both measures were strong, indicating that this order-dependent effect shifted absolute measurements without disrupting individual difference structure. Prior sensorimotor history therefore does not compromise the task's ability to extract stable cognitive variables, but does bias the values it yields - a non-random source of variance that, if it generalizes beyond this experiment, could shift patients across diagnostic thresholds in proposed clinical applications. The finding adds to a growing list of potential methodological contaminants in computational psychiatry's cognitive-task paradigms that must be characterized and mitigated before clinical translation.

q-bio.NC↗

arXiv2025

AI-Driven Optimization under Uncertainty for Mineral Processing Operations

The global capacity for mineral processing must expand rapidly to meet the demand for critical minerals, which are essential for building the clean energy technologies necessary to mitigate climate change. However, the efficiency of mineral processing is severely limited by uncertainty, which arises from both the variability of feedstock and the complexity of process dynamics. To optimize mineral processing circuits under uncertainty, we introduce an AI-driven approach that formulates mineral processing as a Partially Observable Markov Decision Process (POMDP). We demonstrate the capabilities of this approach in handling both feedstock uncertainty and process model uncertainty to optimize the operation of a simulated, simplified flotation cell as an example. We show that by integrating the process of information gathering (i.e., uncertainty reduction) and process optimization, this approach has the potential to consistently perform better than traditional approaches at maximizing an overall objective, such as net present value (NPV). Our methodological demonstration of this optimization-under-uncertainty approach for a synthetic case provides a mathematical and computational framework for later real-world application, with the potential to improve both the laboratory-scale design of experiments and industrial-scale operation of mineral processing circuits without any additional hardware.

eess.SY↗

arXiv2025

Are Targeted Data Poisoning Attacks as Effective as We Think?

Targeted data poisoning attacks manipulate model predictions on specific test samples by injecting malicious data into training. Yet existing evaluations report average attack success rates over randomly selected targets, obscuring true worst-case effectiveness. We argue that the right evaluation focuses on the hardest samples to poison. The same reasoning applies to defense: since targeted attacks leave no footprint at the distribution level, defenders should proactively identify the most vulnerable samples and apply targeted countermeasures. Given a test dataset, this paper identifies both the easiest and hardest to poison examples based on only clean model information. Specifically, we offer coarse evaluations using clean training dynamics, and fine-grained classification on poison class using poison distances and budgets. Our experiments show these metrics reliably stratify samples by poisoning vulnerability, enabling both rigorous worst-case evaluation and proactive vulnerability-aware defense.

cs.LG↗

arXiv2025

Managing Geological Uncertainty in Critical Mineral Supply Chains: A POMDP Approach with Application to U.S. Lithium Resources

The world is entering an unprecedented period of critical mineral demand, driven by the global transition to renewable energy technologies and electric vehicles. This transition presents unique challenges in mineral resource development, particularly due to geological uncertainty-a key characteristic that traditional supply chain optimization approaches do not adequately address. To tackle this challenge, we propose a novel application of Partially Observable Markov Decision Processes (POMDPs) that optimizes critical mineral sourcing decisions while explicitly accounting for the dynamic nature of geological uncertainty. Through a case study of the U.S. lithium supply chain, we demonstrate that POMDP-based policies achieve superior outcomes compared to traditional approaches, especially when initial reserve estimates are imperfect. Our framework provides quantitative insights for balancing domestic resource development with international supply diversification, offering policymakers a systematic approach to strategic decision-making in critical mineral supply chains.

cs.AI↗