arXiv subjects

Osamah Sufyan

Publications and source records attributed to Osamah Sufyan.

3 recordsLinked to original sources

arXiv2026

Double circular dichroism high harmonic spectroscopy: An ultrafast probe for topological photocurrents

Understanding optical responses of topological matter is central to optoelectronic applications, photocurrent control, and spectroscopy. Yet schemes for sensing ultrafast photocurrents and separating bulk and surface contributions are lacking. We introduce double circular dichroism (DCD) harmonic spectroscopy as an all-optical probe of ultrafast dynamics in topological materials. Pump and probe pulses are circular with independently controlled helicities, yielding the circular dichroism of the circular dichroism: a time-resolved response that evaluates how probe-induced dichroism depends on pump helicity. While DCD vanishes in symmetric systems, it survives in broken time-reversal-symmetry materials, including Chern insulators. Simulations in a Haldane nanoflake, where a pump manipulates chiral current-carrying states and intense probe pulses drive harmonic emission, show that DCD arises from bulk and edge-localized states with opposite signs, similar magnitudes, and different amplitude scaling. Edge-associated DCD is connected to the outermost atomic layer of exponentially localized protected modes and is relatively robust to bulk-edge interference, enabling separation of bulk and edge photocurrents. Varying the electronic structure and laser parameters reveals harmonic anomalies that may probe topological attributes of photocurrents and material phase. In particular, HHG DCD oscillations under band-structure manipulation vary strongly across the phase transition and become stable and nonoscillatory in topologically trivial phases. DCD also oscillates with pump-probe delay on attosecond timescales, offering an ultrafast probe of topological photocurrents and their coherence. DCD could therefore disentangle bulk and boundary photoresponses in broken-symmetry quantum and topological matter and be implemented in photoelectron and absorption pump-probe spectroscopies and chiral systems.

cond-mat.other↗

arXiv2026

PU-UNet: Stable Multiplicative Interactions for Medical Image Segmentation

Many dense prediction networks rely on additive feature transformations and model higher-order feature interactions only implicitly. Product units provide an explicit mechanism for multiplicative feature modeling, but their logarithmic--exponential formulation can cause numerical instability, which has limited their use in deep dense prediction networks. In this work, we propose Product-Unit U-Net (PU-UNet), a residual U-Net that integrates stable product-unit residual blocks into rich low-resolution stages for medical image segmentation. The proposed formulation combines smooth positivity mapping with log-domain clipping, enabling stable multiplicative feature learning with negligible computational overhead. On ISIC 2018, Kvasir-SEG, and BUSI, PU-UNet achieves Dice scores of 0.942, 0.959, and up to 0.925, respectively. Compared with a matched Residual U-Net baseline, PU-UNet consistently improves Dice and IoU while keeping parameters, FLOPs, and inference latency nearly unchanged, and reduces the image-level false-positive rate on normal BUSI cases from 0.077 to zero. Ablation studies suggest that the gains are associated with product-unit interactions, are strongest under low-resolution placement, and benefit from the proposed stabilization design. These results suggest that stable product-unit residual learning can be an effective way to enhance U-Net-style segmentation networks with explicit multiplicative interactions.

cs.CV↗

arXiv2026

Anatomy-Informed Deep Learning for Abdominal Aortic Aneurysm Segmentation

In CT angiography, the accurate segmentation of abdominal aortic aneurysms (AAAs) is difficult due to large anatomical variability, low-contrast vessel boundaries, and the close proximity of organs whose intensities resemble vascular structures, often leading to false positives. To address these challenges, we propose an anatomy-aware segmentation framework that integrates organ exclusion masks derived from TotalSegmentator into the training process. These masks encode explicit anatomical priors by identifying non-vascular organsand penalizing aneurysm predictions within these regions, thereby guiding the U-Net to focus on the aorta and its pathological dilation while suppressing anatomically implausible predictions. Despite being trained on a relatively small dataset, the anatomy-aware model achieves high accuracy, substantially reduces false positives, and improves boundary consistency compared to a standard U-Net baseline. The results demonstrate that incorporating anatomical knowledge through exclusion masks provides an efficient mechanism to enhance robustness and generalization, enabling reliable AAA segmentation even with limited training data.

cs.CV↗