Reassessing The Fundamentals: New Constraints on the Evolution, Ages and Masses of Neutron Stars

The ages and masses of neutron stars (NSs) are two fundamental threads that make pulsars accessible to other sub-disciplines of astronomy and physics. A realistic and accurate determination of these two derived parameters play an important role in understanding of advanced stages of stellar evolution and the physics that govern relevant processes. Here I summarize new constraints on the ages and masses of NSs with an evolutionary perspective. I show that the observed P-Pdot demographics is more diverse than what is theoretically predicted for the standard evolutionary channel. In particular, standard recycling followed by dipole spin-down fails to reproduce the population of millisecond pulsars with higher magnetic fields (B > 4 x 10^{8} G) at rates deduced from observations. A proper inclusion of constraints arising from binary evolution and mass accretion offers a more realistic insight into the age distribution. By analytically implementing these constraints, I propose a "modified" spin-down age for millisecond pulsars that gives estimates closer to the true age. Finally, I independently analyze the peak, skewness and cutoff values of the underlying mass distribution from a comprehensive list of radio pulsars for which secure mass measurements are available. The inferred mass distribution shows clear peaks at 1.35 Msun and 1.50 Msun for NSs in double neutron star (DNS) and neutron star-white dwarf (NS-WD) systems respectively. I find a mass cutoff at 2 Msun for NSs with WD companions, which establishes a firm lower bound for the maximum mass of NSs.Comment: 4 pages, 4 figures; To appear in the AIP proceedings of "Astrophysics of Neutron Stars-2010", eds. E. Gogus, T. Belloni, U. Erta

Do all millisecond pulsars share a common heritage?

The discovery of millisecond pulsations from neutron stars in low mass X-ray binary (LMXB) systems has substantiated the theoretical prediction that links millisecond radio pulsars (MSRPs) and LMXBs. Since then, the process that produces millisecond radio pulsars from LMXBs, followed by spin-down due to dipole radiation has been conceived as the 'standard evolution' of millisecond pulsars. However, the question whether all the observed millisecond radio pulsars could be produced by LMXBs has not been quantitatively addressed until now. The standard evolutionary process produces millisecond pulsars with periods (P) and spin-down rates (Pdot) that are not entirely independent. The possible P-Pdot values that millisecond radio pulsars can attain are jointly constrained. In order to test whether the observed millisecond radio pulsars are the unequivocal descendants of millisecond X-ray pulsars (MSXP), we have produced a probability map that represents the expected distribution of millisecond radio pulsars for the standard model. We show with more than 95 % confidence that the fastest spinning millisecond radio pulsars with high magnetic fields, e.g. PSR B1937+21, cannot be produced by the observed millisecond X-ray pulsars within the framework of the standard model.Comment: Full resolution color figures available at: http://www.kiziltan.org/research.html. To appear in the American Institute of Physics (AIP) proceedings, 8 pages, 2 figures, 1 tabl

Topology-Aware Focal Loss for 3D Image Segmentation

The efficacy of segmentation algorithms is frequently compromised by topological errors like overlapping regions, disrupted connections, and voids. To tackle this problem, we introduce a novel loss function, namely Topology-Aware Focal Loss (TAFL), that incorporates the conventional Focal Loss with a topological constraint term based on the Wasserstein distance between the ground truth and predicted segmentation masks' persistence diagrams. By enforcing identical topology as the ground truth, the topological constraint can effectively resolve topological errors, while Focal Loss tackles class imbalance. We begin by constructing persistence diagrams from filtered cubical complexes of the ground truth and predicted segmentation masks. We subsequently utilize the Sinkhorn-Knopp algorithm to determine the optimal transport plan between the two persistence diagrams. The resultant transport plan minimizes the cost of transporting mass from one distribution to the other and provides a mapping between the points in the two persistence diagrams. We then compute the Wasserstein distance based on this travel plan to measure the topological dissimilarity between the ground truth and predicted masks. We evaluate our approach by training a 3D U-Net with the MICCAI Brain Tumor Segmentation (BraTS) challenge validation dataset, which requires accurate segmentation of 3D MRI scans that integrate various modalities for the precise identification and tracking of malignant brain tumors. Then, we demonstrate that the quality of segmentation performance is enhanced by regularizing the focal loss through the addition of a topological constraint as a penalty term

EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG

One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities within an end-to-end discriminative setting. We propose a novel end-to-end machine learning pipeline, EEG-NeXt, which facilitates transfer learning by: i) aligning the EEG trials from different subjects in the Euclidean-space, ii) tailoring the techniques of deep learning for the scalograms of EEG signals to capture better frequency localization for low-frequency, longer-duration events, and iii) utilizing pretrained ConvNeXt (a modernized ResNet architecture which supersedes state-of-the-art (SOTA) image classification models) as the backbone network via adaptive finetuning. On publicly available datasets (Physionet Sleep Cassette and BNCI2014001) we benchmark our method against SOTA via cross-subject validation and demonstrate improved accuracy in cognitive activity classification along with better generalizability across cohorts

ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery

In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds. Most VS methods to date have focused on using canonical compound representations (e.g., SMILES strings, Morgan fingerprints) or generating alternative fingerprints of the compounds by training progressively more complex variational autoencoders (VAEs) and graph neural networks (GNNs). Although VAEs and GNNs led to significant improvements in VS performance, these methods suffer from reduced performance when scaling to large virtual compound datasets. The performance of these methods has shown only incremental improvements in the past few years. To address this problem, we developed a novel method using multiparameter persistence (MP) homology that produces topological fingerprints of the compounds as multidimensional vectors. Our primary contribution is framing the VS process as a new topology-based graph ranking problem by partitioning a compound into chemical substructures informed by the periodic properties of its atoms and extracting their persistent homology features at multiple resolution levels. We show that the margin loss fine-tuning of pretrained Triplet networks attains highly competitive results in differentiating between compounds in the embedding space and ranking their likelihood of becoming effective drug candidates. We further establish theoretical guarantees for the stability properties of our proposed MP signatures, and demonstrate that our models, enhanced by the MP signatures, outperform state-of-the-art methods on benchmark datasets by a wide and highly statistically significant margin (e.g., 93% gain for Cleves-Jain and 54% gain for DUD-E Diverse dataset).Comment: NeurIPS, 2022 (36th Conference on Neural Information Processing Systems

EEG-NEXT: A MODERNIZED CONVNET FOR THE CLASSIFICATION OF COGNITIVE ACTIVITY FROM EEG

One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities within an end-to-end discriminative setting. We propose a novel end-to-end machine learn- ing pipeline, EEG-NeXt, which facilitates transfer learning by: i) aligning the EEG trials from different subjects in the Euclidean-space, ii) tailoring the techniques of deep learning for the scalograms of EEG signals to capture better frequency localization for low-frequency, longer-duration events, and iii) utilizing pretrained ConvNeXt (a mod- ernized ResNet architecture which supersedes state-of-the-art (SOTA) image classification models) as the backbone network via adaptive finetuning. On publicly available datasets (Physionet Sleep Cassette and BNCI2014001) we benchmark our method against SOTA via cross-subject validation and demonstrate improved accuracy in cog- nitive activity classification along with better generalizability across cohorts