arXiv subjects

Muhammad Qasim

Publications and source records attributed to Muhammad Qasim.

8 recordsLinked to original sources

arXiv2026

Adaptive Penalization and Bootstrap-Smoothed Inference for Two-Sample Mendelian Randomization with Summary Data

Two-sample Mendelian randomization (MR) uses genetic variants as instrumental variables to estimate causal effects from observational data using summary association statistics. However, horizontal pleiotropy can invalidate standard MR estimators and lead to biased causal inference. Pleiotropy-robust methods have been proposed to address this issue, including regularization-based approaches such as MR-Lasso. However, MR-Lasso may fail to identify invalid instruments consistently, and its post-selection inference can be unreliable. In this paper, we develop two lasso-type procedures for two-sample MR with summary-level data. The first, MR-ALasso, extends MR-Lasso by introducing adaptive penalty weights for pleiotropic effects in order to improve the identification of valid and invalid instruments. The second, MR-ALasso-B, combines adaptive lasso selection with bootstrap smoothing to improve post-selection inference. We establish theoretical results for MR-ALasso under the two-sample summary data framework, including invalid instrument identification consistency and oracle-type post-selection behavior. Simulation studies show that MR-ALasso generally improves upon MR-Lasso in estimation accuracy and invalid-instrument identification, whereas MR-ALasso-B substantially improves coverage and type-I error control relative to naive post-selection inference. A real-data application based on bidirectional analyses of multiple complex traits further illustrates the practical usefulness of the proposed methods. We provide an R package, MRAlasso, to facilitate implementation.

stat.ME↗

arXiv2026

Adaptive Norm-Based Regularization for Neural Networks

In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regularization strategies that extend classical ridge- and lasso-type penalties to neural network models. The first strategy modifies weight decay by incorporating the covariance structure of the input features into a ridge-type $\ell_2$ penalty, allowing regularization to account for feature dependence. The second combines an $\ell_1$ sparsity penalty with covariance-aware $\ell_2$ regularization, producing neural network weights that are both sparse and structurally informed. Monte Carlo simulations are used to evaluate these methods under different data-generating settings, followed by two real-data applications on building cooling-load prediction and leukemia cell-type classification from high-dimensional gene expression data. Across simulated and real-data examples, the proposed regularizers improve predictive performance on unseen data and provide more effective complexity control than standard norm-based penalties, particularly when features are correlated or high-dimensional.

stat.ML↗

arXiv2026

Comparison of Maximum Likelihood Classification Before and After Applying Weierstrass Transform

The aim of this paper is to use Maximum Likelihood (ML) Classification on multispectral data by means of qualitative and quantitative approaches. Maximum Likelihood is a supervised classification algorithm which is based on the Classical Bayes theorem. It makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class means vector and covariance matrix are the key inputs to the function and can be estimated from training pixels of a particular class. As Maximum Likelihood need some assumptions before it has to be applied on the data. In this paper we will compare the results of Maximum Likelihood Classification (ML) before apply the Weierstrass Transform and apply Weierstrass Transform and will see the difference between the accuracy on training pixels of high resolution Quickbird satellite image. Principle Component analysis (PCA) is also used for dimension reduction and also used to check the variation in bands. The results shows that the separation between mean of the classes in the decision space is to be the main factor that leads to the high classification accuracy of Maximum Likelihood (ML) after using Weierstrass Transform than without using it.

stat.AP↗

arXiv2025

Plasmascopy of ultrafast hot charges in solids

We demonstrate an electric field-resolved approach for probing ultrafast dynamics of photoinjected charges in solids. Direct access to the electric field of few-cycle pulses enables us to measure a broadband response of a medium with associated plasma frequency. We prepare an ensemble of photoinjected hot charge carriers with energies sufficient to trigger impact ionization and establish a framework to measure its dynamics. Our study reveals the first time-resolved observation of the short-lived ultrafast impact ionization in germanium counteracted by trapping of mobile charges at later times. This approach provides a promising route for studying ultrafast many-body physics in photoexcited solids, with predictions from advanced theoretical models.

cond-mat.other↗

arXiv2024

The normal decomposition of a morphism in categories without zeros

For a morphism f in a category C with sufficiently many finite limits and colimits, we discuss an elementary construction of a decomposition of f through objects P and N which, if C happens to have a zero object, amounts to the standard decomposition of f through P = Coker(ker f) and N = Ker(coker f). In this way we obtain natural notions of normal monomorphism and normal epimorphism also in non-pointed categories, as special types of regular mono- and epimorphisms. We examine the factorization behaviour of these classes of morphisms in general, compare the generalized normal decompositions with other types of threefold factorizations, and illustrate them in some every-day categories. The concrete construction of normal decompositions in the slices or coslices of these categories can be challenging. Amongst many others, in this regard, we consider particularly the categories of T1-spaces and of groups.

math.CT↗

arXiv2020

Transient optical gain in strong-field-excited solids

Multiphoton excitation of a solid by a few-cycle, intense laser pulse forms a very non-equilibrium distribution of charge carriers, where occupation probabilities do not necessarily decrease with energy. We show that, under certain conditions, significant population inversion can emerge between pairs of valence- or conduction-band states, where transitions between the Bloch states are dipole-allowed. This population inversion leads to stimulated emission in a laser-excited solid at frequencies where the unperturbed solid is transparent. We establish the optimal conditions for observing the strong-field-induced optical gain.

cond-mat.mes-hall↗

arXiv2018

The notion of closedness and D-connectedness in Quantale-valued approach spaces

In this paper, we characterize the local T0 and T1 separation axioms for quantale-valued gauge space, show how these concepts are related to each other and apply them to L-approach space and L-approach system. Furthermore, we give the characterization of a closed point and D-connectedness in quantale-valued gauge space. Finally, we compare all these concepts with other.

math.GN↗

arXiv2018

Ensemble properties of charge carriers injected by an ultrashort laser pulse

The average effective mass of charge carriers produced by an intense ultrashort laser pulse in a transparent solid increases significantly as the excitation mechanism changes from multiphoton transitions to interband tunneling. We theoretically investigate this phenomenon for several dielectrics and semiconductors. For diamond as a representative dielectric, we present a detailed analysis of the laser-induced change of optical properties. When the concentration of free carriers is high, we find that the average effective mass controls not only the intraband charge-carrier transport but also the interband contributions to the optical response. We observe that the excitation-induced birefringence is particularly large for parameters where the plasma response compensates for the linear response of an unperturbed solid.

cond-mat.mes-hall↗