arXiv subjects

Jianhao Su

Publications and source records attributed to Jianhao Su.

5 recordsLinked to original sources

arXiv2026

AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime

Edge AI model deployment is a multi-stage engineering process involving model conversion, operator compatibility handling, quantization calibration, runtime integration, and accuracy validation. In practice, this workflow is long, failure-prone, and heavily dependent on deployment expertise, particularly when targeting hardware-specific inference runtimes. This technical report presents AIPC (AI Porting Conversion), an AI agent-driven approach for constrained automation of AI model deployment. AIPC decomposes deployment into standardized, verifiable stages and injects deployment-domain knowledge into agent execution through Agent Skills, helper scripts, and a stage-wise validation loop. This design reduces both the expertise barrier and the engineering time required for hardware deployment. Using Qualcomm AI Runtime (QAIRT) as the primary scenario, this report examines automated deployment across representative vision, multimodal, and speech models. In the cases covered here, AIPC can complete deployment from PyTorch to runnable QNN/SNPE inference within 7-20 minutes for structurally regular vision models, with indicative API costs roughly in the range of USD 0.7-10. For more complex models involving less-supported operators, dynamic shapes, or autoregressive decoding structures, fully automated deployment may still require further advances, but AIPC already provides practical support for execution, failure localization, and bounded repair.

cs.SE↗

arXiv2025

The disclosure of information about the range of asset value in market

The information released to investors in financial markets has various forms. We refer to range information as information about the upper and lower bound which the payoff of a risky asset may reach in the future. This study develops rational expectation models to explore the market impacts of disclosure of range information. Our model shows that its disclosure can decrease the sensitivity of market price to private signal and increase market liquidity. The market impact of its disclosure depends on the position and precision of the disclosed range. When the linear combination of private signal and noise trading volume is distant from the disclosed range, the reaction of price to a variation in private signal will almost vanish, whereas a movement of the disclosed range can affect the price efficiently. If the midpoint of the disclosed range is higher (lower) than a criterion which is specified in this study, the disclosure will reduce (raise) asset premium.

econ.TH↗

arXiv2025

The asymmetrical Acquisition of information about the range of asset value in market

The information investors acquire in asset markets has various forms. We refer to range information as information about the upper and lower bound which the payoff of an asset may reach in the future. This paper explores the market impacts of investors' asymmetrical acquisition of range information. Uninformed traders are inherently unable to directly obtain the private signal held by informed traders. This study shows that when range information is released to investors asymmetrically, uninformed traders who can only obtain rougher range information will not trade assets under the max-min ambiguity aversion criterion. Investors' asymmetrical acquisition of range information can cause that market liquidity and the sensitivity of market price to private signal vary continuously with the signal and noise trading volume. We also reveal that investors' asymmetrical acquisition of range information can increase market liquidity and the sensitivity of price under some conditions and decrease them under some other conditions.

econ.TH↗

arXiv2023

AquaSAM: Underwater Image Foreground Segmentation

The Segment Anything Model (SAM) has revolutionized natural image segmentation, nevertheless, its performance on underwater images is still restricted. This work presents AquaSAM, the first attempt to extend the success of SAM on underwater images with the purpose of creating a versatile method for the segmentation of various underwater targets. To achieve this, we begin by classifying and extracting various labels automatically in SUIM dataset. Subsequently, we develop a straightforward fine-tuning method to adapt SAM to general foreground underwater image segmentation. Through extensive experiments involving eight segmentation tasks like human divers, we demonstrate that AquaSAM outperforms the default SAM model especially at hard tasks like coral reefs. AquaSAM achieves an average Dice Similarity Coefficient (DSC) of 7.13 (%) improvement and an average of 8.27 (%) on mIoU improvement in underwater segmentation tasks.

cs.CV↗

arXiv2020

Corporate Governance, Noise Trading and Liquidity of Stocks

Our main task is to study the effect of corporate governance on the market liquidity of listed companies' stocks. We establish a theoretical model that contains the heterogeneity of investors' beliefs to explain the mechanisms by which corporate governance improves liquidity of the corporate stocks. In this process we found that the existence of noise traders who are semi-informed in the market is an important condition for corporate governance to have the effect of improving liquidity of the stocks. We further find that the strength of this effect is affected by the degree of noise traders' participation in market transactions. Our model reveals that corporate governance and the degree of noise traders' participation in transactions have a synergistic effect on improving the liquidity of the stocks.

q-fin.TR↗