Automated Bruch’s Membrane Opening Segmentation in Cases of Optic Disc Swelling in Combined 2D and 3D SD-OCT Images Using Shape-Prior and Texture Information

When the optic disc is swollen, the visibility of the Bruch’s membrane opening (BMO) is often drastically reduced in spectral-domain optical coherence tomography (SD-OCT) volumes. Recent work pro- posed a semi-automated method to segment the BMO using combined information from 2D high-definition raster and 3D volumetric SD-OCT scans; however, manual placement of six landmark points was required. In this work, we propose a fully automated approach to segment the BMO from 2D high-definition and 3D volumetric SD-OCT scans. Using the topographic shape of the internal limiting membrane and textural information near Bruch’s membrane, two BMO points are first estimated in the high-definition central B-scan and then registered into the corresponding volumetric scan. Utilizing the information from both the high- definition BMO estimates and the standard-definition SD-OCT volume, the cost image was created. A graph-based algorithm with soft shape-based constraints is further applied to segment the BMO contour on the SD-OCT en-face image domain. Using a set of 23 volumes with reasonably centered raster scans and swelling larger than 14.42 mm3, the fully automated approach was significantly more accurate than a traditional approach utilizing information only from the SD-OCT volume (RMS error of 7.18 vs. 21.37 in pixels; p < 0.05) and had only a slightly higher (and not significantly different) error than the previously proposed semi-automated approach (RMS error of 7.18 vs. 5.30 in pixels; p = 0.08)

Red Teaming Language Model Detectors with Language Models

The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent works have proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the robustness and reliability of these LLM detectors under adversarial attacks. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation. In both strategies, we leverage an auxiliary LLM to generate the word replacements or the instructional prompt. Different from previous works, we consider a challenging setting where the auxiliary LLM can also be protected by a detector. Experiments reveal that our attacks effectively compromise the performance of all detectors in the study with plausible generations, underscoring the urgent need to improve the robustness of LLM-generated text detection systems.Comment: Preprint. Accepted by TAC

Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification

Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and that generalizes to previously unseen re-identification tasks. The main challenge is that at inference time the representation must generalize to previously unseen identities. To address this problem, we apply continual meta metric learning to lifelong object re-identification. To prevent forgetting of previous tasks, we use knowledge distillation and explore the roles of positive and negative pairs. Based on our observation that the distillation and metric losses are antagonistic, we propose to remove positive pairs from distillation to robustify model updates. Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on extensive intra-domain experiments on person and vehicle re-identification datasets, as well as inter-domain experiments on the LReID benchmark. Our experiments demonstrate that DwoPP significantly outperforms the state-of-the-art. The code is here: https://github.com/wangkai930418/DwoPP_codeComment: BMVC 202

Near-Surface Attenuation and Velocity Structures in Taiwan from Wellhead and Borehole Recordings Comparisons

By analyzing the data from 28 seismic borehole stations deployed by the Central Weather Bureau Seismic Network throughout Taiwan from 2007 to 2014, we estimated the near-surface velocity (Vp and Vs) and attenuation (Qp and Qs) structures from surface to depths of approximately 300 m. To ensure that the deeper recordings were on the ray path between the seismic source and upper receiver, only events with an incidence angle of less than 35° were selected. Local magnitudes of analyzed events were between 1.1 and 6.6. The subsurface Qp and Qs were well modeled in the 5 - 40 Hz frequency band using the spectral ratio of direct P- and S-waves, respectively, at each station, under frequency-independent Q and ω2 source model assumptions. The estimated Vp in the Coastal Plain, the Western Foothills, the Longitudinal Valley, and the Yilan Plain were approximately 1000 - 2000 m s-1, which was lower than the Vp of 2500 - 4000 m s-1 in the Central Mountain Range. In addition, the Vs in the plain areas were lower than that in the Central Mountain Range. The low Vp and Vs and high Vp/Vs ratio in the Coastal Plain and the Western Foothills can be attributed to the unconsolidated soil and high pore-fluid content of subsurface sediments in the plain areas. In contrast to the velocity distribution, low Qp and Qs were observed in the Central Mountain Range. The low Qp and Qs with low Vp/Vs and low Qs/Qp ratios in the Central Mountain Range was consistent with the high thermal temperature observed in the field investigation. The obtained velocity and attenuation structures near surface could also provide important constraints in validation of the crustal structure of Taiwan