arXiv subjects

Waqar Ahmad

Publications and source records attributed to Waqar Ahmad.

12 recordsLinked to original sources

arXiv2026

Warm Hybrid Axion Inflation in $α$-Attractor Models Constrained by ACT and Future Plan experiments

We present a comprehensive study of warm hybrid inflation within the framework of $α$-attractor models, where an axionic inflaton is coupled to a waterfall field in the presence of thermal dissipation. The model is analyzed for both linear ($Υ\propto T$) and cubic ($Υ\propto T^{3}$) dissipation regimes. Confronting the theoretical predictions with the latest observational data from Planck+BICEP/Keck, P-ACT-LB-BK18 and SPT, and , we find that in the weak dissipative regime ($Q_{*} \lesssim 10^{-5}$), the scalar spectral index $n_{s} \simeq 0.965$ lies at the boundary of the combined P-ACT-LB-BK18 constraints, while the tensor-to-scalar ratio $r$ remains within observable ranges. For stronger dissipation ($Q_{*} \gtrsim 10^{-5}$), the model predicts values of $n_{s}$ well within the $1$--$2σ$ confidence region of all datasets, with tensor modes remaining fully observable in both dissipation scenarios. These results indicate that forthcoming CMB polarization experiments may be capable of detecting primordial gravitational waves, thereby providing a robust observational test of warm hybrid inflation across different dissipative regimes.

hep-ph↗

arXiv2025

Destroying the Kerr Newman MOG Black hole with Scalar Test Field

We test the weak cosmic censorship conjecture (WCCC) for the Kerr-Newman-modified gravity black hole (KN-MOG-BH) by interacting scalar test fields with the KN-MOG-BH. Neglecting backreaction effects, we first show that the scalar test fields with frequencies just above the superradiance threshold can overspin both extremal and nearly extremal KN-MOG-BHs, leading to the formation of naked singularities. Moreover, test fields can continuously push nearly extremal KN-MOG-BHs toward their extremal limit. Next, we incorporate backreaction effects, assuming that the event horizon's angular velocity rises prior to the test field's absorption. We show that backreaction prevents overspinning in the extremal KN-MOG-BH, whereas in the nearly extremal case, it fails to stop the BH from being overspun.

gr-qc↗

arXiv2025

Similarity-based Outlier Detection for Noisy Object Re-Identification Using Beta Mixtures

Object re-identification (Re-ID) methods are highly sensitive to label noise, which typically leads to significant performance degradation. We address this challenge by reframing Re-ID as a supervised image similarity task and adopting a Siamese network architecture trained to capture discriminative pairwise relationships. Central to our approach is a novel statistical outlier detection (OD) framework, termed Beta-SOD (Beta mixture Similarity-based Outlier Detection), which models the distribution of cosine similarities between embedding pairs using a two-component Beta distribution mixture model. We establish a novel identifiability result for mixtures of two Beta distributions, ensuring that our learning task is well-posed. The proposed OD step complements the Re-ID architecture combining binary cross-entropy, contrastive, and cosine embedding losses that jointly optimize feature-level similarity learning. We demonstrate the effectiveness of Beta-SOD in de-noising and Re-ID tasks for person Re-ID, on CUHK03 and Market-1501 datasets, and vehicle Re-ID, on VeRi-776 dataset. Our method shows superior performance compared to the state-of-the-art methods across various noise levels (10-30\%), demonstrating both robustness and broad applicability in noisy Re-ID scenarios. The implementation of Beta-SOD is available at: github.com/waqar3411/Beta-SOD

cs.CV↗

arXiv2025

Testing the weak cosmic censorship conjecture via test particle induced overcharging/overspinning of Kerr Newman modified gravity black hole

We investigate the weak cosmic censorship conjecture (WCCC) violation through the absorption of a charged, rotating test particle by Kerr Newman spacetime in modified gravity (KN MOG). The result is sensitive to multiple factors, including the sign of the particle's charge relative to that of the black hole, as well as the direction of rotation of the particle and the black hole. Additionally, the combined effect of the MOG parameter, the black hole's charge and angular momentum plays a significant role. Taking all these into account, we have determined the range of the particle's charge, angular momentum and energy of the particle for which the event horizon disappears. Our analysis shows that the WCCC can be violated in both extremal and non extremal KN MOG black hole, provided the particle's parameters are small and precisely adjusted. Crucially, prior work by [1] discussed the WCCC in KN spacetime without considering the modified gravity parameter α. In contrast, our study shows that incorporating α significantly enlarges the range of permissible parameters for the test particle, leading to the violation of the WCCC in both extremal and non extremal KN MOG black holes.

gr-qc↗

arXiv2024

Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads

Predicting a trip's travel time is essential for route planning and navigation applications. The majority of research is based on international data that does not apply to Pakistan's road conditions. We designed a complete pipeline for mining trajectories from sensors data. On this data, we employed state-of-the-art approaches, including a shallow artificial neural network, a deep multi-layered perceptron, and a long-short-term memory, to explore the issue of travel time prediction on frequent routes. The experimental results demonstrate an average prediction error ranging from 30 seconds to 1.2 minutes on trips lasting 10 minutes to 60 minutes on six most frequent routes in regions of Islamabad, Pakistan.

cs.AI↗

arXiv2022

Urdu Speech and Text Based Sentiment Analyzer

Discovering what other people think has always been a key aspect of our information-gathering strategy. People can now actively utilize information technology to seek out and comprehend the ideas of others, thanks to the increased availability and popularity of opinion-rich resources such as online review sites and personal blogs. Because of its crucial function in understanding people's opinions, sentiment analysis (SA) is a crucial task. Existing research, on the other hand, is primarily focused on the English language, with just a small amount of study devoted to low-resource languages. For sentiment analysis, this work presented a new multi-class Urdu dataset based on user evaluations. The tweeter website was used to get Urdu dataset. Our proposed dataset includes 10,000 reviews that have been carefully classified into two categories by human experts: positive, negative. The primary purpose of this research is to construct a manually annotated dataset for Urdu sentiment analysis and to establish the baseline result. Five different lexicon- and rule-based algorithms including Naivebayes, Stanza, Textblob, Vader, and Flair are employed and the experimental results show that Flair with an accuracy of 70% outperforms other tested algorithms.

cs.CL↗

arXiv2020

ETMA: A New Software for Event Tree Analysis with Application to Power Protection

Event Tree (ET) analysis is a widely used forward deductive safety analysis technique for decision-making at a system design stage. Existing ET tools usually provide Graphical Users Interfaces (GUI) for users to manually draw system-level ET diagrams, which consist of nodes and branches, describing all possible success and failure scenarios. However, these tools do not include some important ET analysis steps, e.g., the automatic generation and reduction of a complete system ET diagram. In this paper, we present a new Event Trees Modeling and Analysis (ETMA) tool to facilitate users to conduct a complete ET analysis of a given system. Some key features of ETMA include: (i) automatic construction of a complete ET model of real-world systems; (ii) deletion/reduction of unnecessary ET nodes and branches; (iii) partitioning of ET paths; and (iv) probabilistic analysis of the occurrence of a certain event. For illustration purposes, we utilize our ETMA tool to conduct the ET analysis of a protective fault trip circuit in power grid transmission lines. We also compared the ETMA results with Isograph, which is a well-known commercial tool for ET analysis.

eess.SP↗

arXiv2020

A Formally Verified HOL4 Algebra for Event Trees

Event Tree (ET) analysis is widely used as a forward deductive safety analysis technique for decision-making at the critical-system design stage. ET is a schematic diagram representing all possible operating states and external events in a system so that one of these possible scenarios can occur. In this report, we propose to use the HOL4 theorem prover for the formal modeling and step-analysis of ET diagrams. To this end, we developed a formalization of ETs in higher-order logic, which is based on a generic list datatype that can: (i) construct an arbitrary level of ET diagrams; (ii) reduce the irrelevant ET branches; (iii) partition ET paths; and (iv) perform the probabilistic analysis based on the occurrence of certain events. For illustration purposes, we conduct the formal ET stepwise analysis of an electrical power grid and also determine its System Average Interruption Frequency Index (SAIFI), which is an important indicator for system reliability.

eess.SY↗

arXiv2020

Human Activity Recognition using Multi-Head CNN followed by LSTM

This study presents a novel method to recognize human physical activities using CNN followed by LSTM. Achieving high accuracy by traditional machine learning algorithms, (such as SVM, KNN and random forest method) is a challenging task because the data acquired from the wearable sensors like accelerometer and gyroscope is a time-series data. So, to achieve high accuracy, we propose a multi-head CNN model comprising of three CNNs to extract features for the data acquired from different sensors and all three CNNs are then merged, which are followed by an LSTM layer and a dense layer. The configuration of all three CNNs is kept the same so that the same number of features are obtained for every input to CNN. By using the proposed method, we achieve state-of-the-art accuracy, which is comparable to traditional machine learning algorithms and other deep neural network algorithms.

eess.SP↗

arXiv2018

Accelerating Viterbi Algorithm using Custom Instruction Approach

In recent years, the decoding algorithms in communication networks are becoming increasingly complex aiming to achieve high reliability in correctly decoding received messages. These decoding algorithms involve computationally complex operations requiring high performance computing hardware, which are generally expensive. A cost-effective solution is to enhance the Instruction Set Architecture (ISA) of the processors by creating new custom instructions for the computational parts of the decoding algorithms. In this paper, we propose to utilize the custom instruction approach to efficiently implement the widely used Viterbi decoding algorithm by adding the assembly language instructions to the ISA of DLX, PicoJava II and NIOS II processors, which represent RISC, stack and FPGA-based soft-core processor architectures, respectively. By using the custom instruction approach, the execution time of the Viterbi algorithm is significantly improved by approximately 3 times for DLX and PicoJava II, and by 2 times for NIOS II.

cs.AR↗

arXiv2018

Formal Probabilistic Analysis of Dynamic Fault Trees in HOL4

Dynamic Fault Trees (DFTs) is a widely used failure modeling technique that allows capturing the dynamic failure characteristics of systems in a very effective manner. Simulation and model checking have been traditionally used for the probabilistic analysis of DFTs. Simulation is usually based on sampling and thus its results are not guaranteed to be complete, whereas model checking employs computer arithmetic and numerical algorithms to compute the exact values of probabilities, which contain many round-off errors. Leveraging upon the expressive and sound nature of higher-order-logic (HOL) theorem proving, we propose, in this work, a formalization of DFT gates and their probabilistic behavior as well as some of their simplification properties in HOL. This formalization would allow us to conduct the probabilistic analysis of DFTs by verifying generic mathematical expressions about their behavior in HOL. In particular, we formalize the AND, OR, Priority-AND, Functional DEPendency, Hot SPare, Cold SPare and the Warm SPare gates and also verify their corresponding probabilistic expressions in HOL. Moreover, we formally verify an important property, Pr(X<Y), using the Lebesgue integral as this relationship allows us to reason about the probabilistic properties of Priority-AND gate and the Before operator. We also formalize the notion of conditional densities in order to formally verify the probabilistic expressions of the Cold SPare and the Warm SPare gates. For illustrating the usefulness of our formalization, we use it to formally analyze the DFT of a Cardiac Assist System.

cs.LO↗

arXiv2017

Dynamic Fault Trees Analysis using an Integration of Theorem Proving and Model Checking

Dynamic fault trees (DFTs) have emerged as an important tool for capturing the dynamic behavior of system failure. These DFTs are then analyzed qualitatively and quantitatively using stochastic or algebraic methods to judge the failure characteristics of the given system in terms of the failures of its sub-components. Model checking has been recently proposed to conduct the failure analysis of systems using DFTs with the motivation to provide a rigorous failure analysis of safety-critical systems. However, model checking has not been used for the DFT qualitative analysis and the reduction algorithms used in model checking are usually not formally verified. Moreover, the analysis time grows exponentially with the increase of the number of states. These issues limit the usefulness of model checking for analyzing complex systems used in safety-critical domains, where the accuracy and completeness of analysis matters the most. To overcome these limitations, we propose a comprehensive methodology to perform the qualitative and quantitative analysis of DFTs using an integration of theorem proving and model checking based approaches. For this purpose, we formalized all the basic dynamic fault tree gates using higher-order logic based on the algebraic approach and formally verified some of the simplification properties. This formalization allows us to formally verify the equivalence between the original and reduced DFTs using a theorem prover, and conduct the qualitative analysis. We then use model checking to perform the quantitative analysis of the formally verified reduced DFT. We applied our methodology to five benchmarks and the results show that the formally verified reduced DFT was analyzed using model checking with up to six times less states and up to 133000 times faster.

cs.LO↗