arXiv subjects

Saikat Saha

Publications and source records attributed to Saikat Saha.

8 recordsLinked to original sources

arXiv2026

Position: AI Leaderboards Are Underserving the Global South: A Case Study from India

This position paper argues that AI leaderboards are structurally ill-suited to serving the Global South because they lack independent governance, conflict-of-interest policies, and mechanisms for metric evolution. The barrier is not missing data; high-quality regional benchmarks already exist: IndicSUPERB, MILU, and LAHAJA for India; IrokoBench for Africa; AlGhafa for Arabic. The barrier is institutional design. Global leaderboards do not include these benchmarks, and no governance mechanism compels them to do so. Commercial pressure corrects leaderboard failures when paying customers in the Global North are affected. The Global South lacks equivalent leverage. Without governance, failures affecting Hindi, Swahili, or Arabic speakers persist indefinitely as documented but unaddressed gaps. Using India as a case study (1.4 billion people, 22 scheduled languages, high-quality benchmarks, but no trusted aggregation), we report findings from a consultation with 58 AI practitioners showing consistent preference for formal governance and disclosure-based conflict management. The solution is not more data but better institutions: regional leaderboards with independent governance from the start.

cs.AI↗

arXiv2026

Quantized orbital and spin Hall transport: interplay between $sp$-hybridization, altermagnetism and spin-orbit coupling

We here explore the emergence of orbital and spin Hall effects, originating beyond the $L$-$S$ coupling, and investigate the interplay between inter-orbit hybridization, relativistic Rashba spin-orbit coupling (SOC), and non-relativistic SOC, namely altermagnetic (AM) order, in a two-dimensional model Hamiltonian. The orbital (spin) Hall responses are remarkably found to be quantized within a window of Fermi energy when the strength of AM order (Rashba SOC) exceeds (falls below) the scale set by $sp$-hybridization. Importantly, orbital and spin Hall quantizations are independent of Rashba SOC and AM order, respectively, while the uniform profiles of finite orbital and vanishingly small spin moments of bands around the Fermi energy. The microscopic origin of such quantization comes from the Fermi surface-activated orbital and spin Berry curvatures. The extent of the quantized regime is strongly controlled by the intra-orbital coupling strength. As the temperature increases, the quantization is significantly compromised in the spin Hall case. We extend our analysis to the orbital and spin Nernst coefficients where the pronounced dip-peak structures signal the existence of the quantization leading to experimental relevance.

cond-mat.mes-hall↗

arXiv2026

Orbital and Spin Nernst Effects in Monolayers of Transition Metal Dichalcogenides

In recent years, orbitronic effects have attracted growing attention as complementary counterparts to the well-established spintronic phenomena. In this work, we demonstrate that monolayers of transition metal dichalcogenides provide an excellent platform for the observation of the orbital Nernst effect, a relatively less explored phenomenon describing the generation of a transverse orbital current in response to an applied temperature gradient. We show that, similar to its electrical counterpart, viz., the orbital Hall effect, the orbital Nernst effect does not require the presence of spin-orbit coupling. Analytical results based on a low-energy valley model offer key insights into the underlying mechanisms, highlighting in particular the crucial role of electronic states at the Fermi energy for the emergence of this effect. The inclusion of spin-orbit coupling further gives rise to a spin Nernst effect, which scales with the strength of spin-orbit coupling and vanishes in its absence. We substantiate our analytical findings with full Brillouin-zone tight-binding results for two representative systems, monolayer 2H MoS$_2$ and 2H NbS$_2$. Our results show that while both orbital and spin Nernst conductivities in MoS$_2$ require electron or hole doping, both effects are intrinsically present in metallic NbS$_2$. Our work reveals the central role of orbital and spin Berry curvatures, identifies doping as an effective route for tuning orbital and spin Nernst responses, and proposes a possible experimental setup for detecting these effects in monolayer transition metal dichalcogenides.

cond-mat.mes-hall↗

arXiv2024

Microstructural features governing fracture of a two-dimensional amorphous solid identified by machine learning

Brittle fracturing of materials is common in natural and industrial processes over a variety of length scales. Knowledge of individual particle dynamics is vital to obtain deeper insight into the atomistic processes governing crack propagation in such materials, yet it is challenging to obtain these details in experiments. We propose an experimental approach where isotropic dilational strain is applied to a densely packed monolayer of attractive colloidal microspheres, resulting in fracture. Using brightfield microscopy and particle tracking, we examine the microstructural evolution of the monolayer during fracturing. Furthermore, using a quantified representation of the microstructure in combination with a machine learning algorithm, we calculate the likelihood of regions of the monolayer to be on a crack line, which we term Weakness. From this analysis, we identify the most important contributions to crack propagation and find that local density is more important than orientational order. Our methodology and results provide a basis for further research on microscopic processes during the fracturing process.

cond-mat.soft↗

arXiv2022

Selection of turbulence models via multiscaling analysis of an axisymmetric pipe flow and heat transfer

To fully evaluate a turbulent flow, Direct Numerical Simulation (DNS) is the most accurate method by far and requires considerable computational power and time; not optimum for industry standards. Developing an alternative model, providing results with reasonable accuracy would resolve this issue. Reynolds Averaged Navier Stokes (RANS) modeling has proven its worth in addressing this phenomenon. In this study, we investigated the RANS turbulence models from COMSOL for fully developed single-phase flow in a two-dimensional axisymmetric pipe domain with constant heating at the wall and periodic boundary conditions at the inlet and outlet. Heat transfer in the fluid module has been added to address the heat transfer phenomenon. We evaluated the computed results with existing DNS data to match the accuracy of the RANS models. RANS simulations are conducted for friction Reynolds number, i.e., Re_τ= 180, 314, and 395 with varying Prandtl numbers, i.e., Pr = 0.71, 2, 5, and 7. Multiscaling analyses in the flow's inner, outer, and meso scaling regions are performed for fluid and heat transfer profiles, i.e., mean streamwise velocity, Reynolds shear stress, mean streamwise temperature, and turbulent heat flux, to compare with the DNS data. The investigation reports the scaling analysis's effectiveness and shows that RANS turbulence models can be used to describe such flow with reasonable accuracy.

physics.flu-dyn↗

arXiv2017

Revisiting ignited-quenched transition and the non-Newtonian rheology of a sheared dilute gas-solid suspension

The hydrodynamics and rheology of a sheared dilute gas-solid suspension, consisting of inelastic hard-spheres suspended in a gas, are analysed using anisotropic Maxwellian as the single particle distribution function. The closed-form solutions for granular temperature and three invariants of the second-moment tensor are obtained as functions of the Stokes number ($St$), the mean density ($ν$) and the restitution coefficient ($e$). Multiple states of high and low temperatures are found when the Stokes number is small, thus recovering the "ignited" and "quenched" states, respectively, of Tsao \& Koch (J. Fluid Mech.,1995). The phase diagram is constructed in the three-dimensional ($ν, St, e$)-space that delineates the regions of ignited and quenched states and their coexistence. Analytical expressions for the particle-phase shear viscosity and the normal stress differences are obtained, along with related scaling relations on the quenched and ignited states. At any $e$, the shear-viscosity undergoes a discontinuous jump with increasing shear rate (i.e.~ discontinuous shear-thickening) at the "quenched-ignited" transition. The first (${\mathcal N}_1$) and second (${\mathcal N}_2$) normal-stress differences also undergo similar first-order transitions: (i) ${\mathcal N}_1$ jumps from large to small positive values and (ii) ${\mathcal N}_2$ from positive to negative values with increasing $St$, with the sign-change of ${\mathcal N}_2$ identified with the system making a transition from the quenched to ignited states. The superior prediction of the present theory over the standard Grad's method and the Chapman-Enskog solution is demonstrated via comparisons of transport coefficients with simulation data for a range of Stokes number and restitution coefficient.

cond-mat.soft↗

arXiv2015

Noise Robust Online Inference for Linear Dynamic Systems

We revisit the Bayesian online inference problems for the linear dynamic systems (LDS) under non- Gaussian environment. The noises can naturally be non-Gaussian (skewed and/or heavy tailed) or to accommodate spurious observations, noises can be modeled as heavy tailed. However, at the cost of such noise robustness, the performance may degrade when such spurious observations are absent. Therefore, any inference engine should not only be robust to noise outlier, but also be adaptive to potentially unknown and time varying noise parameters; yet it should be scalable and easy to implement. To address them, we envisage here a new noise adaptive Rao-Blackwellized particle filter (RBPF), by leveraging a hierarchically Gaussian model as a proxy for any non-Gaussian (process or measurement) noise density. This leads to a conditionally linear Gaussian model (CLGM), that is tractable. However, this framework requires a valid transition kernel for the intractable state, targeted by the particle filter (PF). This is typically unknown. We outline how such kernel can be constructed provably, at least for certain classes encompassing many commonly occurring non-Gaussian noises, using auxiliary latent variable approach. The efficacy of this RBPF algorithm is demonstrated through numerical studies.

stat.CO↗

arXiv2013

Online inference in Markov modulated nonlinear dynamic systems: a Rao-Blackwellized particle filtering approach

The Markov modulated (switching) state space is an important model paradigm in applied statistics. In this article, we specifically consider Markov modulated nonlinear state-space models and address the online Bayesian inference problem for such models. In particular, we propose a new Rao-Blackwellized particle filter for the inference task which is our main contribution here. The detailed descriptions including an algorithmic summary are subsequently presented.

stat.CO↗