arXiv subjects

Cheng-Wei Lin

Publications and source records attributed to Cheng-Wei Lin.

6 recordsLinked to original sources

arXiv2026

Evidence Subspace Projection: Measuring How Much Evidence Explains Deepfake Detection in Self-Supervised Speech Models

Self-supervised learning (SSL) models are widely used as feature extractors for state-of-the-art audio deepfake detection, but it remains unclear how to directly and quantitatively connect what SSL models capture to detection decisions. To address this gap, we propose Evidence Subspace Projection, a method that represents both evidence factors (e.g., attack category, codec, gender, transmission) and authenticity labels in a shared space constructed from SSL models' neuron activation patterns. By projecting the decision vector onto each evidence subspace, we obtain a scalar ratio that quantifies the explanatory power of each evidence type. We evaluate SSL models in raw, fine-tuned, and post-trained settings on multiple datasets. The results confirm findings from established studies, validating the proposed method, and reveal new insights into model behavior.

cs.SD↗

arXiv2024

FineWeb-zhtw: Scalable Curation of Traditional Chinese Text Data from the Web

The quality and size of a pretraining dataset significantly influence the performance of large language models (LLMs). While there have been numerous efforts in the curation of such a dataset for English users, there is a relative lack of similar initiatives for Traditional Chinese. Building upon this foundation of FineWeb, we introduce FineWeb-zhtw, a dataset tailored specifically for Traditional Chinese users. We came up with multiple stages of meticulously designed filters to cater to the linguistic difference between English and Traditional Chinese, to ensure comprehensiveness and quality. We determined effectiveness from querying dataset samples with three main objectives. Our code and datasets are publicly available.

cs.CL↗

arXiv2022

Coarse-to-Fine Point Cloud Registration with SE(3)-Equivariant Representations

Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose differences, or leverage global shapes, which leads to inconsistency when facing distribution variances such as partial overlapping. Combining the advantages of both types of methods, we adopt a coarse-to-fine pipeline that concurrently handles both issues. We first reduce the pose differences between input point clouds by aligning global features; then we match the local features to further refine the inaccurate alignments resulting from distribution variances. As global feature alignment requires the features to preserve the poses of input point clouds and local feature matching expects the features to be invariant to these poses, we propose an SE(3)-equivariant feature extractor to simultaneously generate two types of features. In this feature extractor, representations that preserve the poses are first encoded by our novel SE(3)-equivariant network and then converted into pose-invariant ones by a pose-detaching module. Experiments demonstrate that our proposed method increases the recall rate by 20% compared to state-of-the-art methods when facing both pose differences and distribution variances.

cs.CV↗

arXiv2021

Search for Axion(-like) Particles in Heavy-Ion Collisions

We propose a novel way to search for axion(-like) particles in heavy-ion collisions using prompt photons as the probe and the property of conversion between photon and axion(-like) particles under a strong magnetic field generated in the non-central collisions. The expected result reveals that a new phase space region of the coupling constant for photon and axion(-like) particles can be covered in the future high energy nuclear colliders.

hep-ph↗

arXiv2016

Temperature-dependent permittivity of annealed and unannealed gold films

Due to local field enhancement and subwavelength confinements, nano-plasmonics provide numerous novel applications. Simultaneously, as an efficient nanoscale heat generator from inherent absorption, thermo-plasmonics is emerging as an important branch. However, although significant temperature increase is involved in applications, detailed characterization of metal permittivity at different temperatures and corresponding thermo-derivative are lacking. In this work, we extract the permittivity of gold from 300K to the annealing temperature of 570K. By comparing annealed and unannealed films, more than one-order difference in thermo-derivative of permittivity is revealed, resulting in unexpectedly large variation of plasmonic properties. Our result is valuable not only for characterizing extensively used unannealed nanoparticles, but also for designing future thermo-nano-plasmonic systems.

physics.optics↗

arXiv2015

Schmitt-Trigger-based Recycling Sensor and Robust and High-Quality PUFs for Counterfeit IC Detection

We propose Schmitt-Trigger (ST) based recycling sensor that are tailored to amplify the aging mechanisms and detect fine grained recycling (minutes to seconds). We exploit the susceptibility of ST to process variations to realize high-quality arbiter PUF. Conventional SRAM PUF suffer from environmental fluctuation-induced bit flipping. We propose 8T SRAM PUF with a back-to-back PMOS latch to improve robustness by 4X. We also propose a low-power 7T SRAM with embedded Magnetic Tunnel Junction (MTJ) devices to enhance the robustness (2.3X to 20X).

cs.CR↗