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Vivek Mishra

Publications and source records attributed to Vivek Mishra.

At least 19 recordsLinked to original sources

Thermodynamics of $T_{\rm c}$ suppression in far-overdoped Tl$_2$Ba$_2$CuO$_6$

The physical origin of the suppression of superconductivity with hole doping in overdoped cuprates remains unclear. We measure the electronic specific heat of microgram-scale Tl$_2$Ba$_2$CuO$_6$ crystals and find sharp superconducting anomalies persisting far into the overdoped regime. A weak-coupling BCS-like framework incorporating the known Fermi surface and cation disorder quantitatively reproduces the observed anomalies for $T_{\rm c}=14$-$25$ K and their weak doping dependence. The results show $T_{\rm c}(p)$ to be driven predominantly by a smoothly decreasing pairing strength.

cond-mat.supr-con

Operator-norm bounds and a quadratic lower-growth example for the special Euclidean algebra se(3)

We prove operator-norm and gradient Lipschitz bounds for exponential-map parameterizations on the special Euclidean algebra se(3), providing an explicit example of intermediate polynomial growth behavior. Using the contraction property of the SO(3) left Jacobian, we show that ||exp(theta)||_op <= 1 + ||theta||_F for all theta in se(3). We then derive a self-contained O(R^2) upper bound for the gradient Lipschitz constant, with explicit constant 4.02, and construct an objective J* satisfying L_J*(R; se(3)) >= 0.0505 R^2 for R >= 2. These results place se(3) between compact Lie algebras, where the Lipschitz constant remains bounded, and Lie algebras with hyperbolic elements, where it grows exponentially. The upper and lower bounds are obtained for different objective classes; no minimax optimality claim is made.

math.FA

Pricing Global Macroeconomic Risk in Equity Markets: Evidence from Selected G20 Economies

This study investigates whether international equity markets systematically price global macroeconomic risks. The empirical analysis is conducted using monthly excess returns for ten G20 countries over the period 2000-2024. A Dynamic Factor Model (DFM) is employed to extract latent global factors from a set of macroeconomic variables capturing global inflation, real activity, monetary policy, term structure, exchange rates, volatility, and oil prices. The model selection criteria of the dynamic factor framework, which support a 3 factor specification that is parsimonious. The Fama MacBeth regressions demonstrate the low explanatory power of the 3-factor model. In contrast, a 4 factor specification results in economically large and statistically significant factor loadings, an obvious rise in explanatory power, and a significant improvement in model performance. The results indicate that a four-factor specification provides the best balance between explanatory power and model stability, significantly improving the ability to explain cross-sectional variation in excess returns , with all factors statistically significant. The Capital Asset Pricing Model, while offering a parsimonious and stable benchmark with consistently significant market betas, exhibits limited explanatory power due to its single factor structure. Overall, the findings suggest that macro driven latent factors extracted through the DFM provide a more comprehensive and empirically robust framework for international asset pricing than the CAPM, highlighting the importance of incorporating multiple sources of systematic risk in explaining cross-country equity returns.

stat.AP

A Representation Optimization Dichotomy, Lie-Algebraic Policy Optimization

Structured reinforcement learning and stochastic optimization often involve parameters evolving on matrix Lie groups such as rotations and rigid-body transformations. We establish a representation-optimization dichotomy for Lie-algebra-parameterized Gaussian policy objectives in the Lie Group MDP class: the gradient Lipschitz constant L(R), governing step size, convergence, and sample complexity of first-order methods, depends only on the algebraic type of g, uniformly over all objectives, independent of reward or transition structure. Specifically, L = O(1) for compact g (e.g., so(n), su(n)), and L = Theta(exp(2R)) for g = gl(n), with O(exp(2R)) for all algebras with a hyperbolic element. A key lower bound shows this exponential growth cannot be canceled by interaction between the exponential map and the objective, making the dichotomy intrinsic to the algebra.This yields an algorithmic consequence: for compact algebras, radius-independent smoothness enables O(1/sqrt(T)) convergence using an O(n^2 J) Lie-algebraic projection step instead of O(d_g^3) Fisher inversion. A Kantorovich alignment bound alpha >= 2 kappa / (kappa + 1) provides a computable condition under which this projection approximates natural gradient updates. Experiments on SO(3)^J and SE(3) confirm the theory: constant smoothness for compact algebras, polynomial growth for SE(3), and alignment across condition regimes. The projection step achieves 1.1-1.7x speedup over Cholesky-based Fisher inversion, with increasing gains at larger scales.

math.OC

Nonlinear Control Synchronization Method for Fractional-order Time Derivatives Chaotic Systems

"Synchronization of two dynamical systems" is the term used to describe the phenomenon when two or more systems gradually change their states or behaviors to become similar or identical. This can happen in a lot of fields, such as physics, engineering, biology, and economics. Synchronization finds applications in neurology and communication systems. It is present in both man-made and organic systems. The nonlinear control synchronization technique for fractional-order time derivative systems is described in this article, where the Adams Basford Moulton method is used for solving the fractional-order system. The reliability and ease of applicability for two chaotic systems are demonstrated by the numerical simulation. Furthermore, in this article, both systems were kept in a chaotic condition while being synchronized with each other. The effects of synchronizing time and rearranging the derivatives are the most significant sections of this article.

math.OC

Solution of variable order fractional differential equations using Homotopy Analysis Method

In the present article an endeavor is made to solve the variable order fractional diffusion equations using a powerful method viz., Homotopy Analysis method. It is demonstrated how the method can be used while solving approximately two types of variable order fractional diffusion equations having physical importance. Numerical simulation results show that the method is reliable and effective for solving fractional order diffusion equations even when the order of the derivative is varying with respect to space or time or both or it is dependent upon some other parameters.

math.GM

Field-angle-resolved heat transport in UTe$_2$: determination of nodal positions in the superconducting order parameter

One of the recurring hurdles in studying unconventional superconductivity is the challenge of efficiently and conclusively identifying the symmetry of the superconducting order parameter in a new material. Uranium ditelluride (UTe$_2$) exhibits an unprecedented number of superconducting phases as a function of pressure and magnetic field, each presumably characterized by a different symmetry of the superconducting gap function. None of these phases has had its symmetry conclusively identified so far. In this article, we report results of an extensive study of the thermal conductivity of UTe$_2$ in its low-field, low-temperature superconducting state as a function of the angle of an applied magnetic field rotated in the $b$-$c$ plane. We observe clear and substantial oscillations in the thermal conductivity as a function of field angle, which naturally suggests the existence of point nodes in the gap. Utilizing the experimentally determined Fermi surface, we are able to model this phenomenon for all the potential gap structures in UTe$_2$ and positively identify the location of these nodes as being along the crystallographic $b$-axis, implying that the superconducting order parameter belongs to the $B_{2u}$ irreducible representation of the crystal point group. The clarity of this result will accelerate the identification of other superconducting phases in UTe$_2$, and guide future studies through the use of high resolution field-angle-dependent measurements.

cond-mat.supr-con

Beyond Homes scaling: disorder, the Planckian bound and a new universality

Beginning with high-$T_c$ cuprate materials, it has been observed that many superconductors exhibit so-called "Homes scaling", in which the zero-temperature superfluid density, $\rho_{s0}$, is proportional to the product of the normal-state dc conductivity and the superconducting transition temperature, $\sigma_\mathrm{dc} T_c$. For conventional, s-wave superconductors, such scaling has been shown to be a natural consequence of elastic-scattering disorder, not only in the extreme dirty limit but across a broad range of scattering parameters. Here we show that when an analogous calculation is carried out for elastic scattering in d-wave superconductors, a stark contrast emerges, with $\rho_{s0} \propto \left(\sigma_\mathrm{dc} T_c \right)^2$ in the dirty limit, in apparent violation of Homes scaling. Within a simple approximate Migdal--Eliashberg treatment of inelastic scattering, we show how Homes scaling is recovered. The normal-state behavior of near optimally doped cuprates is dominated by inelastic scattering, but significant deviations from Homes scaling occur for disorder-dominated cuprate systems, such as underdoped YBCO and overdoped LSCO, and in very clean materials with little inelastic scattering, such as Sr$_2$RuO$_4$. We present a revised analysis where both axes of the original Homes scaling plot are normalized by the Drude plasma weight, $\omega_{p,D}^2$, and show that new universal scaling emerges, in which the superfluid fractions of dirty s-wave and dirty d-wave superconductors coalesce to a single point at which normal-state scattering is occurring at the Planckian bound. The combined result is a new tool for classifying superconductors in terms of order parameter symmetry, as well as scattering strength and character. Although our model starts from a Fermi-liquid assumption it describes underdoped cuprates surprisingly well.

cond-mat.supr-con

Thermal conductivity of nonunitary triplet superconductors: application to UTe$_2$

There is considerable evidence that the heavy fermion material UTe$_2$ is a spin-triplet superconductor, possibly manifesting time-reversal symmetry breaking, as measured by Kerr effect and muon spin resonance experiments below the critical temperature, in some samples. Such signals can arise due to a chiral orbital state, or possible nonunitary pairing. Although experiments at low $T$ appear to be consistent with point nodes in the spectral gap, the detailed form of the order parameter and even the nodal positions are not yet determined. Thermal conductivity measurements can extend to quite low temperatures, with varying heat current direction can therefore provide information on the order parameter structure. Here we derive a general expression for the thermal conductivity of a spin triplet superconductor, and use it to compare the low-temperature behavior of various states proposed for UTe$_2$.

cond-mat.supr-con

Robust nodal behavior in the thermal conductivity of superconducting UTe$_2$

The superconducting state of the heavy-fermion metal UTe$_2$ has attracted considerable interest because of evidence for spin-triplet Cooper pairing and non-trivial topology. Progress on these questions requires identifying the presence or absence of nodes in the superconducting gap function and their dimension. In this article we report a comprehensive study of the influence of disorder on the thermal transport in the superconducting state of UTe$_2$. Through detailed measurements of the magnetic field dependence of the thermal conductivity in the zero-temperature limit, we obtain clear evidence for the presence of point nodes in the superconducting gap for all samples with transition temperatures ranging from 1.6~K to 2.1~K obtained by different synthesis methods, including a refined self-flux method. This robustness implies the presence of symmetry-imposed nodes throughout the range studied, further confirmed via disorder-dependent calculations of the thermal transport in a model with a single pair of nodes. In addition to capturing the temperature dependence of the thermal conductivity up to $T_c$, this model allows us to limit the possible locations of the nodes, suggesting a B$_{1u}$ or B$_{2u}$ symmetry for the superconducting order parameter. Additionally, comparing the new, ultra-high conductivity samples to older samples reveals a crossover between a low-field and a high field regime at a single value of the magnetic field in all samples. In the high field regime, the thermal conductivity at different disorder levels differ from each other by a simple offset, suggesting that some simple principle determines the physics of the mixed state, a fact which may illuminate trends observed in other clean nodal superconductors.

cond-mat.supr-con

Optical conductivity of overdoped cuprates from ab-initio out-of-plane impurity potentials

Dopant impurity potentials determined by ab-initio supercell DFT calculations are used to calculate the optical conductivity of overdoped LSCO and Tl-2201 in the superconducting and normal states. Vertex corrections are included, to account for the effect of forward scattering on two-particle properties. This approach was previously shown to provide good, semiquantitative agreement with measurements of superfluid density in LSCO. Here we compare calculations of conductivity with measurements of THz conductivity on LSCO using identical impurity, band, and correlation parameters, and find similarly good correspondence with experiment. In the process, we delineate the impact of the different disorder mechanisms on single-particle and transport relaxation processes. In particular, we reveal the critical role of apical oxygen vacancies in transport scattering and show that transport relaxation rates in LSCO are significantly reduced when apical oxygen vacancies are annealed out. These considerations are shown to be crucial for understanding the variability of experimental results on overdoped LSCO in samples of nominally identical doping but different types. Finally, we give predictions for Tl-2201 THz conductivity experiments.

cond-mat.supr-con

Time-Series Forecasting: Unleashing Long-Term Dependencies with Fractionally Differenced Data

This study introduces a novel forecasting strategy that leverages the power of fractional differencing (FD) to capture both short- and long-term dependencies in time series data. Unlike traditional integer differencing methods, FD preserves memory in series while stabilizing it for modeling purposes. By applying FD to financial data from the SPY index and incorporating sentiment analysis from news reports, this empirical analysis explores the effectiveness of FD in conjunction with binary classification of target variables. Supervised classification algorithms were employed to validate the performance of FD series. The results demonstrate the superiority of FD over integer differencing, as confirmed by Receiver Operating Characteristic/Area Under the Curve (ROCAUC) and Mathews Correlation Coefficient (MCC) evaluations.

cs.LG

Integration of Fractional Order Black-Scholes Merton with Neural Network

This study enhances option pricing by presenting unique pricing model fractional order Black-Scholes-Merton (FOBSM) which is based on the Black-Scholes-Merton (BSM) model. The main goal is to improve the precision and authenticity of option pricing, matching them more closely with the financial landscape. The approach integrates the strengths of both the BSM and neural network (NN) with complex diffusion dynamics. This study emphasizes the need to take fractional derivatives into account when analyzing financial market dynamics. Since FOBSM captures memory characteristics in sequential data, it is better at simulating real-world systems than integer-order models. Findings reveals that in complex diffusion dynamics, this hybridization approach in option pricing improves the accuracy of price predictions. the key contribution of this work lies in the development of a novel option pricing model (FOBSM) that leverages fractional calculus and neural networks to enhance accuracy in capturing complex diffusion dynamics and memory effects in financial data.

q-fin.CP

Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation for Inventory Management Under Uncertainty

This study proposes an Ensemble Differential Evolution with Simula-tion-Based Hybridization and Self-Adaptation (EDESH-SA) approach for inven-tory management (IM) under uncertainty. In this study, DE with multiple runs is combined with a simulation-based hybridization method that includes a self-adaptive mechanism that dynamically alters mutation and crossover rates based on the success or failure of each iteration. Due to its adaptability, the algorithm is able to handle the complexity and uncertainty present in IM. Utilizing Monte Carlo Simulation (MCS), the continuous review (CR) inventory strategy is ex-amined while accounting for stochasticity and various demand scenarios. This simulation-based approach enables a realistic assessment of the proposed algo-rithm's applicability in resolving the challenges faced by IM in practical settings. The empirical findings demonstrate the potential of the proposed method to im-prove the financial performance of IM and optimize large search spaces. The study makes use of performance testing with the Ackley function and Sensitivity Analysis with Perturbations to investigate how changes in variables affect the objective value. This analysis provides valuable insights into the behavior and robustness of the algorithm.

math.OC

Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns

This work adopts a novel approach to determine the risk and return of crude oil stocks by employing Arbitrage Pricing Theory (APT) and Quantile Regression (QR).The APT identifies the underlying risk factors likely to impact crude oil returns.Subsequently, QR estimates the relationship between the factors and the returns across different quantiles of the distribution. The West Texas Intermediate (WTI) crude oil price is used in this study as a benchmark for crude oil prices. WTI price fluctuations can have a significant impact on the performance of crude oil stocks and, subsequently, the global economy.To determine the proposed models stability, various statistical measures are used in this study.The results show that changes in WTI returns can have varying effects depending on market conditions and levels of volatility. The study highlights the impact of structural discontinuities on returns, which can be caused by changes in the global economy and the demand for crude oil.The inclusion of pandemic, geopolitical, and inflation-related explanatory variables add uniqueness to this study as it considers current global events that can affect crude oil returns.Findings show that the key factors that pose major risks to returns are industrial production, inflation, the global price of energy, the shape of the yield curve, and global economic policy uncertainty.This implies that while making investing decisions in WTI futures, investors should pay particular attention to these elements

q-fin.ST

Multiple Independent DE Optimizations to Tackle Uncertainty and Variability in Demand in Inventory Management

To determine the effectiveness of metaheuristic Differential Evolution optimization strategy for inventory management (IM) in the context of stochastic demand, this empirical study undertakes a thorough investigation. The primary objective is to discern the most effective strategy for minimizing inventory costs within the context of uncertain demand patterns. Inventory costs refer to the expenses associated with holding and managing inventory within a business. The approach combines a continuous review of IM policies with a Monte Carlo Simulation (MCS). To find the optimal solution, the study focuses on meta-heuristic approaches and compares multiple algorithms. The outcomes reveal that the Differential Evolution (DE) algorithm outperforms its counterparts in optimizing IM. To fine-tune the parameters, the study employs the Latin Hypercube Sampling (LHS) statistical method. To determine the final solution, a method is employed in this study which combines the outcomes of multiple independent DE optimizations, each initiated with different random initial conditions. This approach introduces a novel and promising dimension to the field of inventory management, offering potential enhancements in performance and cost efficiency, especially in the presence of stochastic demand patterns.

cs.NE

A Novel Approach with Monte-Carlo Simulation and Hybrid Optimization Approach for Inventory Management with Stochastic Demand

This study addresses the difficulties associated with inventory management of products with stochastic demand. The objective is to find the optimal combination of order quantity and reorder point that maximizes profit while considering ethical considerations in inventory management. The ethical considerations are risk assessment, social responsibility, environmental sustainability, and customer satisfaction. Monte Carlo simulation (MCS) is used in this study to generate a distribution of demand and lead times for the inventory items, which is then used to estimate the potential profit and risk associated with different inventory policies. This work proposes a hybrid optimization approach combining Gaussian process regression and conditioning function to efficiently search the high-dimensional space of potential continuous review (r, Q) and periodic review (p, Q) values to find the optimal combination that maximizes profit while considering ethical considerations. The findings show that both the (r, Q) and (p, Q) approaches can effectively manage inventory with stochastic demand, but the (r, Q) approach performs better (profits up by 12.73%) when demand is more volatile. The study adds quantifiable risk assessment and sensitivity analysis to these considerations, considering the variation in demand and expected output in profit percentage. The results provide useful information for making ethical and responsible choices in supply chain analytics, boosting efficiency and profits.

cs.CE

Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) models have recently attracted a great deal of interest from a variety of application sectors. Despite significant developments in this area, there are still no standardized methods or approaches for understanding AI model outputs. A systematic and cohesive framework is also increasingly necessary to incorporate new techniques like discriminative and generative models to close the gap. This paper contributes to the discourse on XAI by presenting an empirical evaluation based on a novel framework: Sampling - Variational Auto Encoder (VAE) - Ensemble Anomaly Detection (SVEAD). It is a hybrid architecture where VAE combined with ensemble stacking and SHapley Additive exPlanations are used for imbalanced classification. The finding reveals that combining ensemble stacking, VAE, and SHAP can. not only lead to better model performance but also provide an easily explainable framework. This work has used SHAP combined with Permutation Importance and Individual Conditional Expectations to create a powerful interpretability of the model. The finding has an important implication in the real world, where the need for XAI is paramount to boost confidence in AI applications.

cs.LG