arXiv subjects

Muhammad Faisal

Publications and source records attributed to Muhammad Faisal.

11 recordsLinked to original sources

arXiv2026

Flat and Topological Floquet Minibands from Patterned Light in Untwisted Bilayer Graphene

Two-dimensional superlattices in van der Waals materials host flat bands and nontrivial topology, most famously at the so-called magic angles of twisted bilayer graphene, where flat bands give rise to correlated and topological phases. Yet these superlattices are usually created by twisting the layers or applying strain, and once a sample is fabricated, their period is fixed and extremely difficult to tune. Here we propose an alternative route: imprint the superlattice optically, using patterned electromagnetic fields rather than a physical twist or strain. We show that patterned in-plane cir- cularly polarized light and a combined drive consisting of a patterned out-of-plane longitudinal field and a uniform circularly polarized field produce isolated bands in both AA- and AB-stacked bilayer graphene. In AB stacking, the central bands additionally become nearly flat, capturing key features of a driven moire superlattice. In this approach, the superlattice period is set by the illumination and is straightforward to tune, and circularly polarized light breaks time-reversal symmetry. Computing the valley Chern numbers of the central bands, we find a rich topological structure with several phase transitions in both stackings. Our results establish light-induced superlattices as a flexible and tunable platform for engineering flat bands and topological phases in bilayer graphene.

cond-mat.mes-hall↗

arXiv2026

CryptDough: A Unified Analytics Engine for Secure Multiparty Computation

We present CryptDough, a unified analytics engine for secure multiparty computation (MPC). CryptDough enables multiple distrusting parties to jointly execute a data analysis pipeline on their private inputs and learn nothing beyond the result (e.g., aggregate statistics). Unlike existing MPC solutions that support a single threat model or workload type, CryptDough provides built-in support for cross-domain analytics (relational, time series, ML inference) under various threat models, all within the same system runtime. CryptDough contributes (i) a hierarchical system design that facilitates modularity and extensibility through progressive lowering of abstractions, and (ii) the concept of virtual vectors that enable users to write single-threaded code across all layers of the software stack, while pushing the complexity of communication, parallelization, and memory management down to the execution engine. We show that CryptDough generalizes the functionality of state-of-the-art MPC systems and remains competitive on the analytics they support, often outperforming them by more than $2\times$.

cs.CR↗

arXiv2026

Staggered Potential and Elliptical Light Driven Topological Phase Transitions in $\alpha$-$\mathcal{T}_{3}$ Lattice

We theoretically investigate the influence of hexagonal boron nitride (h-BN) on the electronic properties of an $\alpha$-$\text{T}_3$ lattice driven by an off-resonant elliptically polarized light field. The staggered potential $M$ breaks the sublattice inversion symmetry, transforming the initial semimetal into a trivial insulator with Chern number $C = 0$. We identify a fundamental geometric singularity at $\alpha = 1/\sqrt{2}$, independent of $M$, where the valley-resolved lower-gap threshold diverges, bounding a finite topological window where conduction--flat band inversion yields a Chern insulator with $C = 1$ carried by the flat band. Increasing the drive further closes the lower gap at the $K'$ valley, transferring the index to the valence band so that the flat band becomes trivial while the system remains $C = 1$. For $\alpha > 1/\sqrt{2}$ the lower gap closes at finite intensity, allowing a transition to $C = 2$ as the dice limit ($\alpha = 1$) is approached. The topological phases are characterized by quantized anomalous Hall plateaus at $\sigma_{xy} = e^2/h$ ($C = 1$) and $\sigma_{xy} = 2e^2/h$ ($C = 2$). The $C = 1$ plateau sits in a narrow gap and is the most fragile, while the $C = 2$ plateau is protected by a wider gap and remains robust to room temperature. A highly asymmetric thermoelectric Seebeck response further serves as an experimental fingerprint of each phase, providing a realistic framework for realizing stable high-Chern-number phases in substrate-supported $\alpha$-$\text{T}_3$ materials.

cond-mat.mes-hall↗

arXiv2026

Nonlinear Hall effect in Floquet-driven monolayer 1T$'$-MoS$_2$

We study the nonlinear Hall effect in Floquet-driven monolayer $1T'$-MoS$_2$, a low-symmetry quantum spin Hall material whose tilted Dirac bands sustain an intrinsic Berry-curvature dipole without the need for strain or trigonal warping. We show that off-resonant circularly polarized light offers a way to control both the sign and the magnitude of the nonlinear Hall response through optically induced topological phase transitions using a Floquet effective Hamiltonian and nonlinear semiclassical transport theory. We show that the anisotropic crystal symmetry enforces a selection rule in which the Berry-curvature dipole elements satisfy $D_x\equiv0$, while a finite $D_y$ originates from the intrinsic band tilt. The Berry curvature is recreated in momentum space as the Floquet drive successively inverts individual spin-valley sectors, resulting in an identical sign reversal of the nonlinear Hall conductivity and the Berry-curvature dipole at each bulk gap closing. In contrast, tuning the band tilt modifies only the magnitude of the response without changing its sign, establishing the observed sign reversal as an unambiguous transport signature of genuine Floquet topological phase transitions. We further show that the nonlinear Hall response can be controlled by the driving strength, perpendicular electric field, Fermi energy, and temperature, providing multiple experimental knobs for observation. Our findings establish the sign of the nonlinear Hall response as a universal transport fingerprint of Floquet-engineered topology and point to monolayer $1T'$-MoS$_2$ as a viable platform for all-electrical detection of nonequilibrium topological phases.

cond-mat.mes-hall↗

arXiv2025

ORQ: Complex Analytics on Private Data with Strong Security Guarantees

We present ORQ, a system that enables collaborative analysis of large private datasets using cryptographically secure multi-party computation (MPC). ORQ protects data against semi-honest or malicious parties and can efficiently evaluate relational queries with multi-way joins and aggregations that have been considered notoriously expensive under MPC. To do so, ORQ eliminates the quadratic cost of secure joins by leveraging the fact that, in practice, the structure of many real queries allows us to join records and apply the aggregations "on the fly" while keeping the result size bounded. On the system side, ORQ contributes generic oblivious operators, a data-parallel vectorized query engine, a communication layer that amortizes MPC network costs, and a dataflow API for expressing relational analytics -- all built from the ground up. We evaluate ORQ in LAN and WAN deployments on a diverse set of workloads, including complex queries with multiple joins and custom aggregations. When compared to state-of-the-art solutions, ORQ significantly reduces MPC execution times and can process one order of magnitude larger datasets. For our most challenging workload, the full TPC-H benchmark, we report results entirely under MPC with Scale Factor 10 -- a scale that had previously been achieved only with information leakage or the use of trusted third parties.

cs.CR↗

arXiv2024

From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing

Face Recognition (FR) has advanced significantly with the development of deep learning, achieving high accuracy in several applications. However, the lack of interpretability of these systems raises concerns about their accountability, fairness, and reliability. In the present study, we propose an interactive framework to enhance the explainability of FR models by combining model-agnostic Explainable Artificial Intelligence (XAI) and Natural Language Processing (NLP) techniques. The proposed framework is able to accurately answer various questions of the user through an interactive chatbot. In particular, the explanations generated by our proposed method are in the form of natural language text and visual representations, which for example can describe how different facial regions contribute to the similarity measure between two faces. This is achieved through the automatic analysis of the output's saliency heatmaps of the face images and a BERT question-answering model, providing users with an interface that facilitates a comprehensive understanding of the FR decisions. The proposed approach is interactive, allowing the users to ask questions to get more precise information based on the user's background knowledge. More importantly, in contrast to previous studies, our solution does not decrease the face recognition performance. We demonstrate the effectiveness of the method through different experiments, highlighting its potential to make FR systems more interpretable and user-friendly, especially in sensitive applications where decision-making transparency is crucial.

cs.CV↗

arXiv2021

An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation

LiDARs and cameras are the two main sensors that are planned to be included in many announced autonomous vehicles prototypes. Each of the two provides a unique form of data from a different perspective to the surrounding environment. In this paper, we explore and attempt to answer the question: is there an added benefit by fusing those two forms of data for the purpose of semantic segmentation within the context of autonomous driving? We also attempt to show at which level does said fusion prove to be the most useful. We evaluated our algorithms on the publicly available SemanticKITTI dataset. All fusion models show improvements over the base model, with the mid-level fusion showing the highest improvement of 2.7% in terms of mean Intersection over Union (mIoU) metric.

cs.CV↗

arXiv2021

Secrecy: Secure collaborative analytics on secret-shared data

We present a relational MPC framework for secure collaborative analytics on private data with no information leakage. Our work targets challenging use cases where data owners may not have private resources to participate in the computation, thus, they need to securely outsource the data analysis to untrusted third parties. We define a set of oblivious operators, explain the secure primitives they rely on, and analyze their costs in terms of operations and inter-party communication. We show how these operators can be composed to form end-to-end oblivious queries, and we introduce logical and physical optimizations that dramatically reduce the space and communication requirements during query execution, in some cases from quadratic to linear or from linear to logarithmic with respect to the cardinality of the input. We implement our framework on top of replicated secret sharing in a system called Secrecy and evaluate it using real queries from several MPC application areas. Our experiments demonstrate that the proposed optimizations can result in over 1000x lower execution times compared to baseline approaches, enabling Secrecy to outperform state-of-the-art frameworks and compute MPC queries on millions of input rows with a single thread per party.

cs.DB↗

arXiv2019

EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency

The existing approaches for salient motion segmentation are unable to explicitly learn geometric cues and often give false detections on prominent static objects. We exploit multiview geometric constraints to avoid such shortcomings. To handle the nonrigid background like a sea, we also propose a robust fusion mechanism between motion and appearance-based features. We find dense trajectories, covering every pixel in the video, and propose trajectory-based epipolar distances to distinguish between background and foreground regions. Trajectory epipolar distances are data-independent and can be readily computed given a few features' correspondences between the images. We show that by combining epipolar distances with optical flow, a powerful motion network can be learned. Enabling the network to leverage both of these features, we propose a simple mechanism, we call input-dropout. Comparing the motion-only networks, we outperform the previous state of the art on DAVIS-2016 dataset by 5.2% in the mean IoU score. By robustly fusing our motion network with an appearance network using the input-dropout mechanism, we also outperform the previous methods on DAVIS-2016, 2017 and Segtrackv2 dataset.

cs.CV↗

arXiv2019

Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model

These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the countrys environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation R, and Root Mean Square Error (RMSE). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision making.

physics.ao-ph↗

arXiv2019

A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

Drought is a complex stochastic natural hazard caused by prolonged shortage of rainfall. Several environmental factors are involved in determining drought classes at the specific monitoring station. Therefore, efficient sequence processing techniques are required to explore and predict the periodic information about the various episodes of drought classes. In this study, we proposed a new weighting scheme to predict the probability of various drought classes under Weighted Markov Chain (WMC) model. We provide a standardized scheme of weights for ordinal sequences of drought classifications by normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme are given by including temporal ordinal data on drought classes determined by the standardized precipitation temperature index (SPTI). Experimental results show that the proposed weighting scheme for WMC model is sufficiently flexible to address actual changes in drought classifications by restructuring the transient behavior of a Markov chain. In summary, this paper proposes a new weighting scheme to improve the accuracy of the WMC, specifically in the field of hydrology.

stat.AP↗