Partha Pratim

Abstract: We consider two independent branching random walks that start next to each other on the -dimensional hypercubic lattice and that carry two different colors.

This study presents LoopID, which profiles proteins at targeted interacting enhancer–promoter (E-P) pairs, identifying the histone demethylase JMJD2 as a regulator of E-P interactions through biomolecular condensate formation independent of its catalytic activity.

Communications Biology , Mar 2026 We report the realization of the first planar optical trap and imaging system using a sheet of light that allows interrogation of living specimens in a plane.

Abstract: Let \mathcal{A} denote the class of functions f that are analytic in the open unit disk \mathbb{D} D and satisfy the normalization conditions f(0) = 0

The chemical composition of stellar atmospheres provides a valuable window into the complex processes of stellar nucleosynthesis. Among chemically peculiar cool stars, many objects are the products of mass transfer in binary systems, including most carbon stars, CH stars, and CEMP-s and CEMP-r/s stars. Accurate and precise determinations of heavy-element abundances in these systems serve as powerful tracers of neutron-capture nucleosynthesis operating in the slow (s) and intermediate (i) regimes. Such measurements also place important constraints on binary evolution, mass-transfer mechanisms, the onset of early s-process enrichment, and the astrophysical sites and production pathways associated with the i-process. In this work, we investigate the origin of the extremely metal-poor star HE 1005-1439, which has previously been suggested to exhibit a surface composition enriched by a combination of s- and i-process nucleosynthesis. Using new multi-zone, detailed AGB models for both the s- and i-processes, we find that a mixed i+s scenario provides a plausible explanation for the observed abundance pattern of HE 1005-1439, although a pure i-process AGB model yields an almost equally satisfactory fit.

Hyperspectral imaging (HSI) provides rich spectral information and serves as a non-destructive technique for forensic stain analysis. Conventional approaches often exhibit degraded performance due to the high dimensionality and spectral redundancy inherent in hyperspectral data. To address this challenge, a hyperspectral dataset comprising nine beverage stains—papaya, coffee, pomegranate, orange, tea, wine, whisky, rum, and brandy—is developed. Building on this dataset, an ensemble framework that combines an optimized autoencoder (AE), channel-attention (CA)-enhanced one-dimensional convolutional neural networks (1D CNNs), and a Limited Memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS-B)-based weighted fusion strategy is proposed. The autoencoder learns compact latent representations from the 204-band hyperspectral vectors, reducing redundancy while preserving discriminative spectral features. CA emphasizes informative spectral bands and improves stain separability. Multiple 1D CNN models are trained using different latent dimensionalities, and their class probability outputs are fused through an optimized L-BFGS-B weighting scheme, where higher-performing models contribute more strongly to the final decision. Experimental results demonstrate classification accuracies of 96.54%, 97.19%, and 97.86% for the AE32 CA, AE64 CA, and AE128 CA models, respectively, with the optimized ensemble achieving an accuracy of 98.28%. Additionally, the time-dependent evolution of beverage stain reflectance is systematically analyzed using overlapped, normalized reflectance signatures acquired at time intervals of 0 min, 1 h, 2 h, 3 h, 4 h, and 5 h. The results confirm that AE-based latent compression, CA, and L-BFGS-B optimized ensemble fusion enhance hyperspectral beverage stain classification, providing an effective and extensible framework for forensic trace evidence analysis.

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X-23 ON THE HUNT! LAURA KINNEY is through playing nice. The Facility has her friends, and they have her sister. And not a damn thing on Earth will stand between her and their safety. They want "X-23, the perfect killer"? Oh, they've got it! SNIKT!

Abstract: Large language models (LLMs) frequently achieve impressive scores on standardized benchmarks, yet accuracy alone offers a limited view of their capabilities.