arXiv subjects

Ruiqi Chen

Publications and source records attributed to Ruiqi Chen.

47 records · Page 3Linked to original sources

arXiv2025

DyRo-MCTS: A Robust Monte Carlo Tree Search Approach to Dynamic Job Shop Scheduling

Dynamic job shop scheduling, a fundamental combinatorial optimisation problem in various industrial sectors, poses substantial challenges for effective scheduling due to frequent disruptions caused by the arrival of new jobs. State-of-the-art methods employ machine learning to learn scheduling policies offline, enabling rapid responses to dynamic events. However, these offline policies are often imperfect, necessitating the use of planning techniques such as Monte Carlo Tree Search (MCTS) to improve performance at online decision time. The unpredictability of new job arrivals complicates online planning, as decisions based on incomplete problem information are vulnerable to disturbances. To address this issue, we propose the Dynamic Robust MCTS (DyRo-MCTS) approach, which integrates action robustness estimation into MCTS. DyRo-MCTS guides the production environment toward states that not only yield good scheduling outcomes but are also easily adaptable to future job arrivals. Extensive experiments show that DyRo-MCTS significantly improves the performance of offline-learned policies with negligible additional online planning time. Moreover, DyRo-MCTS consistently outperforms vanilla MCTS across various scheduling scenarios. Further analysis reveals that its ability to make robust scheduling decisions leads to long-term, sustainable performance gains under disturbances.

cs.AI↗

arXiv2025

On the control of recurrent neural networks using constant inputs

This paper investigates the controllability of a broad class of recurrent neural networks widely used in theoretical neuroscience, including models of large-scale human brain dynamics. Motivated by emerging applications in non-invasive neurostimulation such as transcranial direct current stimulation (tDCS), we study the control synthesis of these networks using constant and piecewise constant inputs. The neural model considered is a continuous-time Hopfield-type system with nonlinear activation functions and arbitrary input matrices representing inter-regional brain interactions. Our main contribution is the formulation and solution of a control synthesis problem for such nonlinear systems using specific solution representations. These representations yield explicit algebraic conditions for synthesizing constant and piecewise constant controls that solve a two-point boundary value problem in state space up to higher-order corrections with respect to the time horizon. In particular, the input is constructed to satisfy a tractable small-time algebraic relation involving the Jacobian of the nonlinear drift, ensuring that the synthesis reduces to verifying conditions on the system matrices. For canonical input matrices that directly actuate $k$ nodes, this implies that the reachable set (with constant inputs) of a given initial state is an affine subspace whose dimension equals the input rank and whose basis can be computed efficiently using a thin QR factorization. Numerical simulations illustrate the theoretical results and demonstrate the effectiveness of the proposed synthesis in guiding the design of brain stimulation protocols for therapeutic and cognitive applications.

math.OC↗

arXiv2025

Quintessential dark energy crossing the phantom divide

Motivated by recent results from the DESI collaboration, we explore two classes of quintessence models that can give rise to crossing of the dark energy equation of state through the ``phantom divide'' $w=-1$. These are models with Lagrangians that involve higher powers of the kinetic energy $\dotϕ^2$, or where the dark matter (DM) mass is a function of $ϕ$. Both have similar features with respect to the reconstructed redshift-dependent $w(z)$: moderate tuning of parameters is required to achieve the desired shape, and it is difficult or impossible for $w(z)$ to continue evolving smoothly as $z$ becomes large. Nevertheless, they give a strong improvement over $Λ$CDM in fitting the data. We point out that models of coupled dark matter and dark energy that cross the phantom divide are under pressure from constraints on long-range DM forces. They rule out the simplest renormalizable coupling of scalar DM to quintessence, but leave the fermionic case marginally allowed, while exponentially coupled models are safe from current constraints.

astro-ph.CO↗

arXiv2025

GestoBrush: Facilitating Graffiti Artists' Digital Creation Experiences through Embodied AR Interactions

Graffiti has long documented the socio-cultural landscapes of urban spaces, yet increasing global regulations have constrained artists' creative freedom, prompting exploration of digital alternatives. Augmented Reality (AR) offers opportunities to extend graffiti into digital environments while retaining spatial and cultural significance, but prior research has largely centered on audience engagement rather than the embodied creative processes of graffiti artists. To address this, we developed GestoBrush, a mobile AR prototype that turns smartphones into virtual spray cans, enabling graffiti creation through embodied gestures. A co-design workshop underscored the role of embodiment-physical engagement with surroundings and body-driven creative processes-in digital workflows. We evaluated GestoBrush with six graffiti artists and findings suggested that embodied AR interactions supporting artists bypass real-world constraints and explore new artistic possibilities, whose AR artworks created enhanced senses of intuitiveness, immersion, and expressiveness. This work highlight how embodied AR tools can bridge the gap between physical graffiti practice and digital expression, suggesting pathways for designing immersive creative systems that respect the cultural ethos of street art while expanding its possibilities in virtual spaces.

cs.HC↗

arXiv2025

FlyMeThrough: Human-AI Collaborative 3D Indoor Mapping with Commodity Drones

Indoor mapping data is crucial for routing, navigation, and building management, yet such data are widely lacking due to the manual labor and expense of data collection, especially for larger indoor spaces. Leveraging recent advancements in commodity drones and photogrammetry, we introduce FlyMeThrough -- a drone-based indoor scanning system that efficiently produces 3D reconstructions of indoor spaces with human-AI collaborative annotations for key indoor points-of-interest (POI) such as entrances, restrooms, stairs, and elevators. We evaluated FlyMeThrough in 12 indoor spaces with varying sizes and functionality. To investigate use cases and solicit feedback from target stakeholders, we also conducted a qualitative user study with five building managers and five occupants. Our findings indicate that FlyMeThrough can efficiently and precisely create indoor 3D maps for strategic space planning, resource management, and navigation.

cs.HC↗

arXiv2025

Mechanism of Quercetin in Inhibiting Triple-Negative Breast Cancer by Regulating T Cell-Related Targets: An Analysis Based on Single-Cell Sequencing and Network Pharmacology

Objective: To investigate the mechanism by which quercetin inhibits triple-negative breast cancer (TNBC) through regulating T-cell-related targets, providing a novel strategy for TNBC immunotherapy.Methods: Single-cell RNA sequencing (GSE161529 dataset) and network pharmacology were integrated. PCA and UMAP clustering identified T-cell subsets and differentially expressed genes in TNBC microenvironment. TNBC-related targets were screened via CTD and OMIM databases, with functional pathways analyzed by GO/KEGG enrichment. Molecular docking and PPI networks validated interactions between quercetin and core targets.Results: Quercetin intersected with 79 TNBC targets, including AKT1, EGFR, and MMP9, enriched in EGFR inhibitor resistance and endocrine resistance pathways. Molecular docking revealed the highest affinity between quercetin and GSK3B (-13.2 kJ/mol). AKT1 and MMP9 expression correlated with patient survival.Conclusion: Quercetin may reverse TNBC immunosuppression by multi-target modulation of T-cell function, but clinical application requires solutions for its low bioavailability, such as delivery systems or combination therapies.

q-bio.MN↗

arXiv2025

SlideAudit: A Dataset and Taxonomy for Automated Evaluation of Presentation Slides

Automated evaluation of specific graphic designs like presentation slides is an open problem. We present SlideAudit, a dataset for automated slide evaluation. We collaborated with design experts to develop a thorough taxonomy of slide design flaws. Our dataset comprises 2400 slides collected and synthesized from multiple sources, including a subset intentionally modified with specific design problems. We then fully annotated them using our taxonomy through strictly trained crowdsourcing from Prolific. To evaluate whether AI is capable of identifying design flaws, we compared multiple large language models under different prompting strategies, and with an existing design critique pipeline. We show that AI models struggle to accurately identify slide design flaws, with F1 scores ranging from 0.331 to 0.655. Notably, prompting techniques leveraging our taxonomy achieved the highest performance. We further conducted a remediation study to assess AI's potential for improving slides. Among 82.0% of slides that showed significant improvement, 87.8% of them were improved more with our taxonomy, further demonstrating its utility.

cs.HC↗

arXiv2025

"What's Happening"- A Human-centered Multimodal Interpreter Explaining the Actions of Autonomous Vehicles

Public distrust of self-driving cars is growing. Studies emphasize the need for interpreting the behavior of these vehicles to passengers to promote trust in autonomous systems. Interpreters can enhance trust by improving transparency and reducing perceived risk. However, current solutions often lack a human-centric approach to integrating multimodal interpretations. This paper introduces a novel Human-centered Multimodal Interpreter (HMI) system that leverages human preferences to provide visual, textual, and auditory feedback. The system combines a visual interface with Bird's Eye View (BEV), map, and text display, along with voice interaction using a fine-tuned large language model (LLM). Our user study, involving diverse participants, demonstrated that the HMI system significantly boosts passenger trust in AVs, increasing average trust levels by over 8%, with trust in ordinary environments rising by up to 30%. These results underscore the potential of the HMI system to improve the acceptance and reliability of autonomous vehicles by providing clear, real-time, and context-sensitive explanations of vehicle actions.

cs.HC↗

arXiv2024

A Power-Efficient Hardware Implementation of L-Mul

Multiplication is a core operation in modern neural network (NN) computations, contributing significantly to energy consumption. The linear-complexity multiplication (L-Mul) algorithm is specifically proposed as an approximate multiplication method for emerging NN models, such as large language model (LLM), to reduce the energy consumption and computational complexity of multiplications. However, hardware implementation designs for L-Mul have not yet been reported. Additionally, 8-bit floating-point (FP8), as an emerging data format, offers a better dynamic range compared to traditional 8-bit integer (INT8), making it increasingly popular and widely adopted in NN computations. This paper thus presents a power-efficient FPGAbased hardware implementation (approximate FP8 multiplier) for L-Mul. The core computation is implemented using the dynamic reconfigurable lookup tables and carry chains primitives available in AMD Xilinx UltraScale/UltraScale+ technology. The accuracy and resource utilization of the approximate multiplier are evaluated and analyzed. Furthermore, the FP8 approximate multiplier is deployed in the inference phase of representative NN models to validate its effectiveness.

cs.AR↗

arXiv2024

DFORM: Diffeomorphic vector field alignment for assessing dynamics across learned models

Dynamical system models such as Recurrent Neural Networks (RNNs) have become increasingly popular as hypothesis-generating tools in scientific research. Evaluating the dynamics in such networks is key to understanding their learned generative mechanisms. However, comparison of learned dynamics across models is challenging due to their inherent nonlinearity and because a priori there is no enforced equivalence of their coordinate systems. Here, we propose the DFORM (Diffeomorphic vector field alignment for comparing dynamics across learned models) framework. DFORM learns a nonlinear coordinate transformation which provides a continuous, maximally one-to-one mapping between the trajectories of learned models, thus approximating a diffeomorphism between them. The mismatch between DFORM-transformed vector fields defines the orbital similarity between two models, thus providing a generalization of the concepts of smooth orbital and topological equivalence. As an example, we apply DFORM to models trained on a canonical neuroscience task, showing that learned dynamics may be functionally similar, despite overt differences in attractor landscapes.

cs.LG↗

arXiv2021

Observers Pupillary Responses in Recognising Real and Posed Smiles: A Preliminary Study

Pupillary responses (PR) change differently for different types of stimuli. This study aims to check whether observers PR can recognise real and posed smiles from a set of smile images and videos. We showed the smile images and smile videos stimuli to observers, and recorded their pupillary responses considering four different situations, namely paired videos, paired images, single videos, and single images. When the same smiler was viewed by observers in both real and posed smile forms, we refer them as paired; otherwise we use the term single. The primary analysis on pupil data revealed that the differences of pupillary response between real and posed smiles are more significant in case of paired videos compared to others. This result is found from timeline analysis, KS-test, and ANOVA test. Overall, our model can recognise real and posed smiles from observers pupillary responses instead of smilers responses. Our research will be applicable in affective computing and computer-human interaction for measuring emotional authenticity.

cs.HC↗