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Linear attention is an efficient attention mechanism that has recently emerged as a promising alternative to conventional softmax attention. With its ability to process tokens in linear computational complexities, linear attention, in…
The BBN predictions for the abundances of the light element isotopes is reviewed and compared with recent observational data. The single unknown parameter of standard BBN is the baryon-to-photon ratio, \eta, and can be determined by the…
The Scale Invariant Vacuum (SIV) paradigm is applied to the Big-Bang Nucleosynthesis using the known analytic expressions for the expansion factor $a$ and the plasma temperature $T$ as functions of the SIV time $\tau$ since the Big-Bang…
Data from future high-precision Cosmic Microwave Background (CMB) measurements will be sensitive to the primordial Helium abundance $Y_p$. At the same time, this parameter can be predicted from Big Bang Nucleosynthesis (BBN) as a function…
We investigate the modifications to predictions for the abundances of light elements from standard Big-Bang nucleosynthesis when exotic late-decaying particles with lifetimes exceeding ~1 sec are prominent in the early Universe. Utilising a…
Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probability distributions, BNNs are capable of resolving the…
Resonant inelastic x-ray scattering (RIXS) has become a very powerful experimental technique to probe a broad range of intrinsic elementary excitations, for example, from low energy phonons and (bi-)magnons to high energy $d$-$d$,…
Bayesian posterior inference of modern multi-probe cosmological analyses incurs massive computational costs. For instance, depending on the combinations of probes, a single posterior inference for the Dark Energy Survey (DES) data had a…
An introductory review of the early evolution of the Universe relevant to the primordial synthesis of the light nuclides deuterium, helium-3, helium-4 and lithium-7. The predictions of the element abundances in the "standard", hot, big bang…
Line intensity mapping (LIM) is a promising observational method to probe large-scale fluctuations of line emission from distant galaxies. Data from wide-field LIM observations allow us to study the large-scale structure of the universe as…
X-ray absorption near edge structure (XANES) is an essential tool for elucidating the atomic-scale, local three-dimensional (3D) structure of given materials and molecules. The rapid computation of XANES based on molecular 3D structures…
In the present work the primordial Big-Bang Nucleosynthesis (BBN) and weakly interacting massive particle (WIMP) dark matter are discussed in a certain class of modified gravitational theories, namely $f(R) \sim R^n$ gravity. The new…
Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In this paper, we…
Personalized neoantigen vaccines represent a promising immunotherapy approach that harnesses tumor-specific antigens to stimulate anti-tumor immune responses. However, the design of these vaccines requires sophisticated computational…
Despite stringent constraints from Big Bang Nucleosynthesis (BBN) and cosmic microwave background (CMB) observations, it is still possible for well-motivated particle physics models to substantially alter the cosmic expansion history…
Reverberation mapping is a technique in which the mass of a Seyfert I galaxy's central supermassive black hole is estimated, along with the system's physical scale, from the timescale at which variations in brightness propagate through the…
We reexamine big bang nucleosynthesis with large-scale baryon density inhomogeneities when the length scale of the density fluctuations exceeds the neutron diffusion length ($\sim 10^7-10^8$ cm at BBN), and the amplitude of the fluctuations…
Since the first observations of interstellar molecules, astrochemical simulations have been employed to model and understand its formation and destruction path- ways. With the advent of high-resolution telescopes such as JWST and ALMA, the…
Light binary convolutional neural networks (LB-CNN) are particularly useful when implemented in low-energy computing platforms as required in many industrial applications. Herein, a framework for optimizing compact LB-CNN is introduced and…
CLAX is a JAX-based library that implements classic click models using modern gradient-based optimization. While neural click models have emerged over the past decade, complex click models based on probabilistic graphical models (PGMs) have…