arXiv subjects

Shuo Dai

Publications and source records attributed to Shuo Dai.

7 recordsLinked to original sources

arXiv2026

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization

Rapidly shifting operational scenarios driven by uncertain Distributed Energy Resource (DER) profiles render conventional distribution network optimization methods either computationally expensive or poorly generalizable. This paper introduces GridRAG, a pioneering retrieval-augmented framework that transforms optimization into a ``retrieve-and-refine'' paradigm. GridRAG first embeds scenario features and optimal solutions into a joint representation space to ensure semantic consistency. Based on the hybrid semantic information, the similar historical scenarios are then retrieved from a pre-constructed database. Then an SDEdit-style diffusion module is integrated to refine retrieved solutions by modeling the conditional distribution over near-feasible manifolds. This process effectively pulls retrieved solutions into near-optimal attraction basins, providing a high-quality warm-start for the final solver. Validated on three optimization tasks across four standard topologies, GridRAG demonstrates superior cross-scenario generalization and a multi-fold speedup in solution time compared to existing learning-based and model-based baselines. Our code is available at https://github.com/YuxuanCEE/GridRAG.

eess.SY↗

arXiv2026

Mind the Intention: Task-Aware Backdoor Attacks for Forecast-Driven Distribution Network Operations

Accurate distributed energy resources (DERs) forecasting is critical for downstream optimal operations. However, such forecast-based operation can be highly vulnerable to cyberattacks. While existing research mainly focuses on adversarial attacks, we pivot to a more controllable and persistent threat: backdoor attacks. In time series forecasting, a backdoored model generates an attacker-specified target pattern whenever a trigger is embedded in historical inputs. This paradigm naturally fits the entire DER forecast-optimization-operation chain. In this paper, we investigate whether and how backdoor attacks can compromise distribution network operations and propose GridTroj, a unified backdoor framework tailored for this scenario. Unlike standard time series backdoor approaches that train a poisoned model to match a predefined target only in terms of forecasting error, GridTroj explicitly incorporates the attacker's intention and optimizes the attack toward operational disruption. Specifically, GridTroj coordinates two key modules. The Intention Planner designs operation-damaging targets and poisoning strategies, while the Backdoor Realizer constructs the corresponding network architecture and training strategy to learn the trigger-target association. Experiments on three downstream optimization tasks demonstrate that GridTroj can effectively compromise grid operations and outperforms existing baselines. Our code is available at https://github.com/YuxuanCEE/GridTroj.

cs.CR↗

arXiv2026

Beyond Information Redundancy: Expanding Cross-Modal Knowledge Representation for Power Load Time Series Forecasting

Load forecasting is pivotal for stable power systems. Conventional uni-modal methods suffer from representation drift under data scarcity. While recent multi-modal approaches attempt to alleviate this, they exhibit severe information redundancy, merely recycling time series data via superficial intra-modal transformations. In this paper, we argue that the essence of multi-modal time series learning should expand representation manifolds via complementary cross-modal knowledge enrichment rather than duplicating redundant information, especially for few-shot scenarios prevalent in power systems. To this end, we propose KEMM-Net, a Knowledge-Enriched Multi-Modal Network for power load forecasting. KEMM-Net first constructs textual and visual embeddings to strengthen load time series representations from different knowledge perspectives. It then introduces a Partial Information Decomposition (PID)-guided cross-modal contrastive learning mechanism to achieve cross-modal semantic alignment and balance redundant, synergistic, and unique information for forecasting. Extensive experiments on real-world public datasets demonstrate that KEMM-Net consistently outperforms strong deep learning and multi-modal baselines, particularly in few-shot settings. Our code is available at https://anonymous.4open.science/r/KEMM-Net-2898.

eess.SP↗

arXiv2026

Bifacial weakness with paresthesias (BFP) secondary to trauma: a case report

This case details the diagnosis and treatment process of a patient with bilateral facial nerve palsy accompanied with limb sensory disturbance secondary to head trauma, who was ultimately diagnosed with Bifacial weakness with paresthesias (BFP) , a rare variant of Guillain-Barr\'e Syndrome(GBS) . The patient underwent plasma exchange therapy and showed favorable recovery . In this article, for the first time we report a case of BFP secondary to trauma.

q-bio.OT↗

arXiv2025

Mediated Transmission of Quantum Synchronization in Star Networks

Synchronization transmission describes the emergence of coherence between two uncoupled oscillators mediated by their mutual coupling to an intermediate one. In classical star networks, such mediated coupling gives rise to remote synchronization--where nonadjacent leaf nodes synchronize through a nonsynchronous hub--and to explosive synchronization, characterized by an abrupt collective transition to coherence. In the quantum regime, analogous effects can arise from the interplay between 1:1 phase locking and 2:1 phase-locking blockade in coupled spin-1 particles. In this work, we investigate a star network composed of spin-1 particles. For identical oscillators, symmetric and asymmetric dissipation lead to distinct transmission behaviors: remote synchronization and quasi-explosive synchronization appear in different coupling regimes, a phenomenon absent in classical counterparts. For nonidentical networks, we find that at large detuning remote synchronization emerges in the weak-coupling regime and evolves into quasi-explosive synchronization as the coupling increases, consistent with classical star-network dynamics. These findings reveal the rich dynamical characteristics of mediated quantum synchronization and point toward new possibilities for exploring synchronization transmission in larger and more complex quantum systems.

quant-ph↗

arXiv2025

Universal Manipulation of Quantum Synchronization in Spin Oscillator Networks

Quantum synchronization (QS) in open many-body systems offers a promising route for controlling collective quantum dynamics, yet existing manipulation schemes often rely on dissipation engineering, which distorts limit cycles, lacks scalability, and is strongly system-dependent. Here, we propose a universal and scalable method for continuously tuning QS from maximal synchronization under isotropic interactions to complete synchronization blockade (QSB) under fully anisotropic coupling in spin oscillator networks. Our approach preserves intrinsic limit cycles and applies to both few-body and macroscopic systems. We analytically show that QS arises solely from spin flip-flop processes and their higher-order correlations, while anisotropic interactions induce non-synchronizing coherence. A geometric QS measure reveals a macroscopic QSB effect in the thermodynamic limit. The proposed mechanism is experimentally feasible using XYZ interactions and optical pumping, and provides a general framework for programmable synchronization control in complex quantum networks and dynamical phases of matter.

quant-ph↗

arXiv2022

Traffic Analytics Development Kits (TADK): Enable Real-Time AI Inference in Networking Apps

Sophisticated traffic analytics, such as the encrypted traffic analytics and unknown malware detection, emphasizes the need for advanced methods to analyze the network traffic. Traditional methods of using fixed patterns, signature matching, and rules to detect known patterns in network traffic are being replaced with AI (Artificial Intelligence) driven algorithms. However, the absence of a high-performance AI networking-specific framework makes deploying real-time AI-based processing within networking workloads impossible. In this paper, we describe the design of Traffic Analytics Development Kits (TADK), an industry-standard framework specific for AI-based networking workloads processing. TADK can provide real-time AI-based networking workload processing in networking equipment from the data center out to the edge without the need for specialized hardware (e.g., GPUs, Neural Processing Unit, and so on). We have deployed TADK in commodity WAF and 5G UPF, and the evaluation result shows that TADK can achieve a throughput up to 35.3Gbps per core on traffic feature extraction, 6.5Gbps per core on traffic classification, and can decrease SQLi/XSS detection down to 4.5us per request with higher accuracy than fixed pattern solution.

cs.NI↗