arXiv subjects

Yi Lyu

Publications and source records attributed to Yi Lyu.

12 recordsLinked to original sources

arXiv2026

Streaming Chain

Blockchain and blockchain-inspired decentralized applications are on the rise thanks to their unique characteristics such as their decentralized nature, anonymity, and tamper-proof nature; however, blockchain transactions tend to experience long end-to-end latency, with a major contributor being the block creation step, which might block transaction processing. There are two approaches to ameliorate this overhead: speeding up the block creation process, or processing transactions before block creation finishes. In this project, we work towards designing a self-adaptive block creation process that automatically selects optimal configurations based on workload and hardware resources by defining mathematical models to predict transaction latency based on design and environmental parameters, developing measurement techniques to collect performance-related metrics in docker-hosted blockchain systems and observing trends to build intuition, and defining a mathematical model to predict transaction success rate under various key accessing patterns and block size configurations, validating it with simulation-based measurements.

cs.CR↗

arXiv2026

ModTrans: Translating Real-world Models for Distributed Training Simulator

Large-scale distributed training has been a research hot spot in machine learning systems for industry and academia in recent years. However, conducting experiments without physical machines and corresponding resources is difficult. One solution is to leverage distributed training simulators, but current ones like ASTRA-sim do not support importing real-world developed models, which poses challenges for ML researchers seeking to use them. Based on this challenge, we developed ModTrans, a translator supporting format translation from any real-world model to the ASTRA-sim simulator's input, removing the barrier between machine learning experts and machine learning system researchers. The experiment results show that ModTrans's cost is negligible.

cs.DC↗

arXiv2026

Educational Database Prototype: the Simplest of All

Database Management System (DBMS) is designed to help store and process large collections of data, and is incredibly flexible to perform various kinds of optimizations as long as it achieves serializability with a high-level interface available. The current undergraduate level DBMS course in UW-Madison (i.e., CS564) involves implementing specific modules of DB architecture, including B+ tree, but students may end up spending numerous amounts of effort on corner cases and not gaining a more comprehensive understanding of the internal design. Thus, we present EduDB, a simple database prototype for educational purposes that provides students a clean, concise, and comprehensive overview of the database system. We also attempt to develop an integrative series of course projects based on EduDB, which offers a platform for students to perform any optimization learned during the semester.

cs.DB↗

arXiv2026

Create Benchmarks for Data Lakes

Data lakes have emerged as a flexible and scalable solution for storing and analyzing large volumes of heterogeneous data, including structured, semi-structured, and unstructured formats. Despite their growing adoption in both industry and academia, there is a lack of standardized and comprehensive benchmarks for evaluating the performance of data lake systems. Existing benchmarks primarily target traditional data warehouses and focus on structured SQL workloads, making them insufficient for capturing the diverse workloads and access patterns typical of data lakes. In this work, we propose a new benchmarking framework for data lakes that aims to provide an objective and comparative evaluation of different data lake implementations. Our benchmark covers multiple data types and workload models, including data retrieval, aggregation, querying, and similarity search, which is a common yet underexplored operation in existing benchmarks. We measure key performance metrics such as query execution time, metadata generation time, and metadata size across different scale factors. The benchmark is designed to be extensible and reproducible, enabling users to generate datasets and evaluate data lake systems under realistic and diverse scenarios. We conduct our experiments on CloudLab and demonstrate how the proposed benchmark can be used to compare both commercial and open-source data lake platforms.

cs.DB↗

arXiv2026

Safeguard: Security Controls at the Software Defined Network Layer

Improvements in software defined networking allow for policy to be informed and modified by data-driven applications that can adjust policy to accommodate fluctuating requirements at line speed. However, there is some concern that over-correction can occur and cause unintended consequences depending on the data received. This is particularly problematic for network security features, such as machine-learning intrusion detection systems. We present Safeguard, a rule-based policy that overlaps a data-driven policy to prevent unintended responses for edge cases in network traffic. We develop a reference implementation of a network traffic classifier that enforces firewall rules for malicious traffic, and show how additional rulesets to allow known-good traffic are essential in utilizing a data-driven network policy.

cs.CR↗

arXiv2026

CTF for education

In this paper, we take a close look at how CTF can be used in cybersecurity education. We divide the CTF competitions into four different categories, which are attack-based CTFs, defense-based CTFs, jeopardy CTFs and gamified and wargames CTFs. We start our analysis by summarizing the main characteristics of different CTF types. We then compare them with each other in both learning objectives and other aspects like accessibility. We conclude that combining all four CTF formats can help participants build one's cybersecurity knowledge. By doing that, we hope that our findings will provide some useful insights for future CTF educators.

cs.CR↗

arXiv2025

Vectorial Symmetry Decoding with Single-Particle Precision via Room-Temperature Lanthanide Luminescence Polarimetry

Determining the local symmetry of luminescent centers in crystals is critical for understanding and controlling their optical transitions, yet current methods are limited by stringent experimental requirements and ambiguous symmetry assignments. Here, we develop a robust computational electromagnetics framework that directly connect the local symmetry and chirality of rare-earth-doped single crystals to the polarization states of their emitted light. This framework is experimentally validated through the precise determination of point and space group symmetries using high-resolution, polarization-resolved micro-photoluminescence ({\mu}-PL) spectra. Unlike conventional approaches that usually rely on analyzing multiple transitions at cryogenic temperatures, our technique operates at room temperature, requires only a single optical transition, and enables accurate orientation of symmetry axes. This enables deterministic polarization control of nano-emitters by tailoring symmetry groups and selecting appropriate transition dipoles, eliminating the need for bulky or complex photonic structures. Additionally, we demonstrate the function of bio-sensing, via determining single particle orientations in complex cellular environments using minimal polarization measurements. These results pave the way for advances in energy transfer systems, ultra-bright rare-earth nanocrystals, nanophotonic materials, and real-time single-particle tracking in biological contexts.

physics.optics↗

arXiv2024

Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning

In the landscape of spatio-temporal data analytics, effective trajectory representation learning is paramount. To bridge the gap of learning accurate representations with efficient and flexible mechanisms, we introduce Efflex, a comprehensive pipeline for transformative graph modeling and representation learning of the large-volume spatio-temporal trajectories. Efflex pioneers the incorporation of a multi-scale k-nearest neighbors (KNN) algorithm with feature fusion for graph construction, marking a leap in dimensionality reduction techniques by preserving essential data features. Moreover, the groundbreaking graph construction mechanism and the high-performance lightweight GCN increase embedding extraction speed by up to 36 times faster. We further offer Efflex in two versions, Efflex-L for scenarios demanding high accuracy, and Efflex-B for environments requiring swift data processing. Comprehensive experimentation with the Porto and Geolife datasets validates our approach, positioning Efflex as the state-of-the-art in the domain. Such enhancements in speed and accuracy highlight the versatility of Efflex, underscoring its wide-ranging potential for deployment in time-sensitive and computationally constrained applications.

cs.LG↗

arXiv2024

VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning

Trajectory similarity search plays an essential role in autonomous driving, as it enables vehicles to analyze the information and characteristics of different trajectories to make informed decisions and navigate safely in dynamic environments. Existing work on the trajectory similarity search task primarily utilizes sequence-processing algorithms or Recurrent Neural Networks (RNNs), which suffer from the inevitable issues of complicated architecture and heavy training costs. Considering the intricate connections between trajectories, using Graph Neural Networks (GNNs) for data modeling is feasible. However, most methods directly use existing mathematical graph structures as the input instead of constructing specific graphs from certain vehicle trajectory data. This ignores such data's unique and dynamic characteristics. To bridge such a research gap, we propose VeTraSS -- an end-to-end pipeline for Vehicle Trajectory Similarity Search. Specifically, VeTraSS models the original trajectory data into multi-scale graphs, and generates comprehensive embeddings through a novel multi-layer attention-based GNN. The learned embeddings can be used for searching similar vehicle trajectories. Extensive experiments on the Porto and Geolife datasets demonstrate the effectiveness of VeTraSS, where our model outperforms existing work and reaches the state-of-the-art. This demonstrates the potential of VeTraSS for trajectory analysis and safe navigation in self-driving vehicles in the real world.

cs.LG↗

arXiv2024

CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference

In response to the rising interest in large multimodal models, we introduce Cross-Attention Token Pruning (CATP), a precision-focused token pruning method. Our approach leverages cross-attention layers in multimodal models, exemplified by BLIP-2, to extract valuable information for token importance determination. CATP employs a refined voting strategy across model heads and layers. In evaluations, CATP achieves up to 12.1X higher accuracy compared to existing token pruning methods, addressing the trade-off between computational efficiency and model precision.

cs.CL↗

arXiv2023

Reduced Axion Abundance from an Extended Symmetry

In recent work we showed that the relic dark matter abundance of QCD axions can be altered when the Peccei-Quinn (PQ) field is coupled to very light scalar/s, rendering the effective axion mass dynamical in the early universe. In this work we develop this framework further, by introducing a new extended symmetry group to protect the new particles' mass. We find that with a new global $SO(N)$ symmetry, with large $N$, we can not only account for the lightness of the new scalars, but we can reduce the axion relic abundance in a technically natural way. This opens up the possibility of large PQ scales, including approaching the GUT scale, and still naturally producing the correct relic abundance of axions. Also, in these models the effective PQ scale is relatively small in the very early universe, and so this can help towards alleviating the isocurvature problem from inflation. Furthermore, instead of possible over-closure from cosmic strings, the extended symmetry implies the formation of non-topological textures which provide a relatively small abundance.

hep-ph↗

arXiv2022

Altered Axion Abundance from a Dynamical Peccei-Quinn Scale

We build a model in which the relic abundance of axions is altered from the standard misalignment mechanism, either increased or decreased, due to the presence of a new light scalar that couples to the radial part of the Peccei-Quinn (PQ) field. The light scalar makes the effective PQ symmetry-breaking scale dynamical, altering the early-time dynamics for the axion and affecting its late-time dark matter abundance. We analyze this new mechanism semianalytically and numerically, showing that we can accommodate both lighter or heavier axion dark matter, compared to the standard treatments. We discuss implications of the model for axion searches and fundamental physics.

hep-ph↗