arXiv subjects

Shenghan Gao

Publications and source records attributed to Shenghan Gao.

7 recordsLinked to original sources

arXiv2026

Distributed Coherent Optical Computing via Injection-Locked Photonic Networks

Coherent photonic computing uses both the phase and amplitude of light to implement linear operations such as dot products and matrix multiplication but requires phase stability between the interfering paths. This poses a challenge for such strategies when optical data is generated at a remote source due to environmental phase variations in fiber. Conventional approaches to distributed computing rely on optical-to-electrical conversion and buffering, limiting truly real-time and distributed computation. Here, we propose a new strategy via optical injection locking to enable distributed, real-time coherent optical processing without unnecessary conversions in the optical-to-electrical or analog-to-digital domains. Using a semiconductor laser rate-equation model, we explore the conditions required for stable operation by sweeping the power injection ratio, frequency detuning, and modulation conditions of the remote and injected lasers. Our results indicate that higher injection powers broaden the locking margin but more readily exhibit frequency-selective features associated with relaxation oscillations and increased amplitude-phase mixing, whereas lower injection powers yield a narrower, but more predictable operating window which remains stable under large modulation depth. End-to-end symbol-sequence simulations with balanced detection and temporal integration further confirm that reducing the injection ratio suppresses residual remote-modulation components in the injected laser output and improves computational accuracy. Overall, our study provides guidance and design trade-offs for remote coherent detection and distributed coherent photonic computing enabled by injection locking.

physics.optics↗

arXiv2026

CommSense: Facilitating Bias-Aware and Reflective Navigation of Online Comments for Rational Judgment

Online comments significantly influence users' judgments, yet their presentation, often determined by platform algorithms, can introduce biases, such as anchoring effects, which distort reasoning. While existing research emphasizes mitigating individual cognitive biases, the evolution of user judgments during comment engagement remains overlooked. This study investigates how presentation cues impact reasoning and explores interface design strategies to mitigate bias. Through a preliminary experiment (N=18) and a co-design workshop, we identified key challenges users face across a four-stage process and distilled four design requirements: pre-engagement framing, interactive organization, reflective prompts, and synthesis support. Based on these insights, we developed CommSense, an on-the-fly plugin that enhances user engagement with online comments by providing visual overviews and lightweight prompts to guide reasoning. A between-subject evaluation (N=24) demonstrates that CommSense improves bias awareness and reflective thinking, helping users produce more comprehensive, evidence-based rationales while maintaining high usability.

cs.HC↗

arXiv2026

SCSimulator: An Exploratory Visual Analytics Framework for Partner Selection in Supply Chains through LLM-driven Multi-Agent Simulation

Supply chains (SCs), complex networks spanning from raw material acquisition to product delivery, with enterprises as interconnected nodes, play a pivotal role in organizational success. However, optimizing SCs remains challenging, particularly in partner selection, a key bottleneck shaped by competitive and cooperative dynamics. This challenge constitutes a multi-objective dynamic game requiring a synergistic integration of Multi-Criteria Decision-Making and Game Theory. Traditional approaches, grounded in mathematical simplifications and managerial heuristics, fail to capture real-world intricacies and risk introducing subjective biases. Multi-agent simulation offers promise, but prior research has largely relied on fixed, uniform agent logic, limiting practical applicability. Recent advances in LLMs create opportunities to represent complex SC requirements and hybrid game logic. However, challenges persist in modeling dynamic SC relationships, ensuring interpretability, and balancing agent autonomy with expert control. We present SCSimulator, a visual analytics framework that integrates LLM-driven MAS with human-in-the-loop collaboration for SC partner selection. It simulates SC evolution via adaptive network structures and enterprise behaviors, which are visualized via interpretable interfaces. By combining CoT reasoning with XAI techniques, it generates multi-faceted, transparent explanations of decision trade-offs. Users can iteratively adjust simulation settings to explore outcomes aligned with their expectations and strategic priorities. Developed through iterative co-design with SC experts and industry managers, SCSimulator serves as a proof-of-concept, offering methodological contributions and practical insights for future research on SC decision-making and interactive AI-driven analytics. Usage scenarios and a user study demonstrate the system's effectiveness and usability.

cs.HC↗

arXiv2025

QueryGenie: Making LLM-Based Database Querying Transparent and Controllable

Conversational user interfaces powered by large language models (LLMs) have significantly lowered the technical barriers to database querying. However, existing tools still encounter several challenges, such as misinterpretation of user intent, generation of hallucinated content, and the absence of effective mechanisms for human feedback-all of which undermine their reliability and practical utility. To address these issues and promote a more transparent and controllable querying experience, we proposed QueryGenie, an interactive system that enables users to monitor, understand, and guide the LLM-driven query generation process. Through incremental reasoning, real-time validation, and responsive interaction mechanisms, users can iteratively refine query logic and ensure alignment with their intent.

cs.HC↗

arXiv2025

Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach

Influence Maximization (IM) in temporal graphs focuses on identifying influential "seeds" that are pivotal for maximizing network expansion. We advocate defining these seeds through Influence Propagation Paths (IPPs), which is essential for scaling up the network. Our focus lies in efficiently labeling IPPs and accurately predicting these seeds, while addressing the often-overlooked cold-start issue prevalent in temporal networks. Our strategy introduces a motif-based labeling method and a tensorized Temporal Graph Network (TGN) tailored for multi-relational temporal graphs, bolstering prediction accuracy and computational efficiency. Moreover, we augment cold-start nodes with new neighbors from historical data sharing similar IPPs. The recommendation system within an online team-based gaming environment presents subtle impact on the social network, forming multi-relational (i.e., weak and strong) temporal graphs for our empirical IM study. We conduct offline experiments to assess prediction accuracy and model training efficiency, complemented by online A/B testing to validate practical network growth and the effectiveness in addressing the cold-start issue.

cs.SI↗

arXiv2023

LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce

Livestream e-commerce integrates live streaming and online shopping, allowing viewers to make purchases while watching. However, effective marketing strategies remain a challenge due to limited empirical research and subjective biases from the absence of quantitative data. Current tools fail to capture the interdependence between live performances and feedback. This study identified computational features, formulated design requirements, and developed LiveRetro, an interactive visual analytics system. It enables comprehensive retrospective analysis of livestream e-commerce for streamers, viewers, and merchandise. LiveRetro employs enhanced visualization and time-series forecasting models to align performance features and feedback, identifying influences at channel, merchandise, feature, and segment levels. Through case studies and expert interviews, the system provides deep insights into the relationship between live performance and streaming statistics, enabling efficient strategic analysis from multiple perspectives.

cs.HC↗

arXiv2019

Investigation of liquid lines as terahertz emitters under ultrashort optical excitation

Recently, there has been growing interest in terahertz (THz) wave generation from liquids under optical excitation. Here, we propose and demonstrate the use of liquid lines in place of liquid films as THz emitters to boost THz signals. The geometry of the emitter eliminates the total internal reflection at the flat liquid-air interface. In addition, we observe that the polarity of the liquid has a significant influence on the THz wave generation. Alpha-pinene, a nonpolar liquid, offers much stronger THz radiation than water does. Besides paving the way to develop intense liquid THz sources, our work indicates that THz waves could be a tool for the further study of laser-liquid interaction.

physics.optics↗