arXiv subjects

Muhammad Zeeshan

Publications and source records attributed to Muhammad Zeeshan.

14 recordsLinked to original sources

arXiv2026

A Graph Theoretical Approach to Optimizing Minimum Italian Domination Sets

A classical problem in graph theory known as the Italian domination number(also called Roman 2-domination number), involves assigning labels of 0, 1,or 2 to each node v. The goal is to ensure that every node with a label of 0 has a sum of labels of the nodes in its closed neighborhood that is 2 or greater. In computer systems, it is coined encompassing a robust cyber security strategy that will protect networks from potential threats, such as hacking, malware, and unauthorized access, by deploying security measures to provide the highest level of protection while reducing the misuse of resources. Toeplitz graphs are a special kind of graphs built over Toeplitz matrices from linear algebra, which are matrices with constant straight diagonal members. In this paper, we provide a detailed analysis regarding the Italian domination numbers for every Toeplitz graph family. We provide comprehensive results on Italian domination numbers across multiple graph families and identify the specific values at which the Italian domination number alters with increasing generator values.

math.CO↗

arXiv2026

Narrow Population Inference Enhanced by Analytical Likelihood Models

The growing catalog of gravitational-wave events has revealed substantial diversity in the properties of compact-binary mergers. However, commonly used population-inference methods based on discrete posterior samples can struggle to constrain narrow population features, resulting in biased or unstable estimates of population hyperparameters. We first demonstrate this limitation using a toy population model by comparing parameter recovery with a continuous likelihood model against discrete approximations constructed from $10^3$, $10^4$, and $10^5$ samples. We then perform the same comparison using synthetic eccentric and multisource populations introduced in previous studies. Although the continuous and discrete approaches yield broadly consistent results, the continuous approximation more accurately recovers the parameters of narrow simulated populations. In particular, while both methods produce similar mass distributions, appreciable differences arise for narrowly distributed parameters such as spin and eccentricity. Our results indicate that the continuous approach provides more reliable inference for spin and eccentricity, whose narrow population distributions can be inadequately represented by finite sample sets. Continuous likelihood models therefore offer a valuable tool for improving population inference and extracting more robust information about the formation and evolution of compact-binary systems.

astro-ph.HE↗

arXiv2026

Uncovering Hierarchical Sub-Population of Binary Black Holes

Enabled by improved instruments with increasing sensitivity, the ongoing gravitational wave census now contains 259 binary black holes, numerous enough to unveil trends, substructure, and subpopulations which may provide key clues to their underlying formation mechanisms. In this work, motivated by evidence for multiple formation channels including hierarchical formation, we build a natively multi component mixture model for the binary black hole population, in which each component has an independently recovered rate, mass, spin, and spin misalignment model. (The components share a common redshift distribution.) Using a model carefully tuned to avoid parameter degeneracies, a powerlaw model plus five successively higher mass gaussians, we recover overall merger rates versus mass and trends versus redshift which are consistent with previously published results. Too, we recover previously identified overall trends versus spin: preferential alignment and low spin at low mass; large spin and isotropic spins at high mass. Critically, however, our multi-component model disagrees with previously published results, finding all components except the lowest mass are consistent with isotropy. Too, our multi-component model has a roughly hierarchical spectrum of gaussian mass peaks, but without the expected correlations between spin and mass expected from naked hierarchical formation

astro-ph.HE↗

arXiv2026

Assessing the waveform systematics from parameter estimation to population inference with eccentricity

While masses and spins are routinely used to constrain compact binary formation channels, eccentricity provides an additional and potentially powerful diagnostic of binary origin, particularly for dynamically assembled systems. Recent advances in eccentric waveform modeling now make it possible to search for eccentric signatures in gravitational wave data; however, differences between waveform models can introduce systematic effects that may propagate into astrophysical population inference. In this work, we analyze 153 binary black holes, 2 binary neutron stars and 7 neutron star black hole binaries from the GWTC-4 catalog. We compare the source and population level inferences obtained with two eccentric waveform models, SEOBNRv5EHM and TEOBResumS-DALI, as well as with quasi circular waveform analyses. We find that the two eccentric models give broadly consistent source parameter estimates for most events, but some events exhibit subtle and coherent differences. These small, systematic offsets can accumulate in hierarchical population inference, leading to differences in inferred population properties, most notably in the redshift evolution and effective spin distribution. Because coherent event level biases can grow approximately as $\sqrt{N}$ for a catalog of N events, waveform systematics become increasingly important as gravitational wave catalogs expand. We also introduce a synthetic data framework that generates eccentric populations and corresponding RIFT posterior samples, enabling injection studies that test the recoverability of eccentric population properties.

astro-ph.HE↗

arXiv2026

Population Properties of Binary Black Holes with Eccentricity

The development of eccentric waveform models enables us to explore the growing catalog of gravitational-wave events with measurable eccentricity. This opens new opportunities to gain insight into the formation channels and evolutionary pathways of compact binary systems using eccentricity. However, most recent population analyses have been limited to quasi-circular binaries, primarily due to constraints in waveform modeling and sensitivity estimates. We are now entering an era where both of these limitations are being addressed, allowing for a more comprehensive investigation of eccentric binary populations. In this work, we perform a very first population analysis that simultaneously fits the mass, spin, redshift, and eccentricity distribution. Specifically, we use source-parameter estimation on 153 binary black holes in GWTC-4 catalog provided by the Rapid Iterative FiTting (RIFT) framework using the SEOBNRv5EHM waveform model. We extend the default O4a population model to include orbital eccentricity. We find that inferred population properties are broadly consistent with conclusions obtained in previous analyses assuming quasi-circular binaries. To assess sensitivity of our results to the most eccentric sources, we repeat our analysis excluding GW200129_065458. Consistent with our conclusions about each event and using Nonoverlapping Mixture eccentricity model, we bound the branching ratio for eccentric events to be below $0.051890$ and $0.022011$ at $90\%$ confidence with and without GW200129_065458 respectively. Using four different parametric population models for eccentricity, we argue that the rate of eccentric events is weakly constrained by observations and highly model-dependent.

astro-ph.HE↗

arXiv2025

An Implementation to Identify the Properties of Multiple Population of Gravitational Wave Sources

The rapidly increasing sensitivity of gravitational wave detectors is enabling the detection of a growing number of compact binary mergers. These events are crucial for understanding the population properties of compact binaries. However, many previous studies rely on computationally expensive inference frameworks, limiting their scalability. In this work, we present GWKokab, a JAX-based framework that enables modular model building with independent rate for each subpopulation such as BBH, BNS, and NSBH binaries. It provides accelerated inference using the normalizing flow based sampler called flowMC and is also compatible with NumPyro samplers. To validate our framework, we generated two synthetic populations, one comprising spinning eccentric binaries and the other circular binaries using a multi-source model. We then recovered their injected parameters at significantly reduced computational cost and demonstrated that eccentricity distribution can be recovered even in spinning eccentric populations. We also reproduced results from two prior studies: one on non-spinning eccentric populations, and another on the BBH mass distribution using the third Gravitational Wave Transient Catalog (GWTC-4). We anticipate that GWKokab will not only reduce computational costs but also enable more detailed subpopulation analyses such as their mass, spin, eccentricity, and redshift distributions in gravitational wave events, offering deeper insights into compact binary formation and evolution.

gr-qc↗

arXiv2025

3D Reconstruction via Incremental Structure From Motion

Accurate 3D reconstruction from unstructured image collections is a key requirement in applications such as robotics, mapping, and scene understanding. While global Structure from Motion (SfM) techniques rely on full image connectivity and can be sensitive to noise or missing data, incremental SfM offers a more flexible alternative. By progressively incorporating new views into the reconstruction, it enables the system to recover scene structure and camera motion even in sparse or partially overlapping datasets. In this paper, we present a detailed implementation of the incremental SfM pipeline, focusing on the consistency of geometric estimation and the effect of iterative refinement through bundle adjustment. We demonstrate the approach using a real dataset and assess reconstruction quality through reprojection error and camera trajectory coherence. The results support the practical utility of incremental SfM as a reliable method for sparse 3D reconstruction in visually structured environments.

cs.CV↗

arXiv2024

TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation

Early detection of skin cancer relies on precise segmentation of dermoscopic images of skin lesions. However, this task is challenging due to the irregular shape of the lesion, the lack of sharp borders, and the presence of artefacts such as marker colours and hair follicles. Recent methods for melanoma segmentation are U-Nets and fully connected networks (FCNs). As the depth of these neural network models increases, they can face issues like the vanishing gradient problem and parameter redundancy, potentially leading to a decrease in the Jaccard index of the segmentation model. In this study, we introduced a novel network named TESL-Net for the segmentation of skin lesions. The proposed TESL-Net involves a hybrid network that combines the local features of a CNN encoder-decoder architecture with long-range and temporal dependencies using bi-convolutional long-short-term memory (Bi-ConvLSTM) networks and a Swin transformer. This enables the model to account for the uncertainty of segmentation over time and capture contextual channel relationships in the data. We evaluated the efficacy of TESL-Net in three commonly used datasets (ISIC 2016, ISIC 2017, and ISIC 2018) for the segmentation of skin lesions. The proposed TESL-Net achieves state-of-the-art performance, as evidenced by a significantly elevated Jaccard index demonstrated by empirical results.

eess.IV↗

arXiv2024

Eccentricity matters: Impact of eccentricity on inferred binary black hole populations

Gravitational waves (GW), emanating from binary black holes (BBH), encode vital information about their source. GW signals enable us to deduce key properties of the BBH population across the universe, including mass, spin, and eccentricity distribution. While the masses and spins of binary components are already recognized for their insights into formation, eccentricity stands out as a distinct and quantifiable indicator of formation and evolution. Yet, despite its significance, eccentricity is notably absent from most parameter estimation (PE) analyses associated with GW signals. To evaluate the precision with which the eccentricity distribution can be deduced, we generated two synthetic populations of eccentric binary black holes (EBBH) characterized by non-spinning, non-precessing dynamics and mass ranges between $10 M_\odot$ and $50 M_\odot$. This was achieved using an eccentric power law model, encompassing $100$ events with eccentricity distributions set at $\sigma_\epsilon = 0.05$ and $\sigma_\epsilon = 0.15$. This synthetic EBBH ensemble was contrasted against a circular binary black holes (CBBH) collection to discern how parameter inferences would vary without eccentricity. Employing Markov Chain Monte Carlo (MCMC) techniques, we constrained vital model parameters, including the event rate ($\mathcal{R}$), mass distribution, minimum mass ($m_{min}$), maximum mass ($m_{max}$), and the eccentricity distribution ($\sigma_\epsilon$). Our analysis demonstrates that eccentric population inference can identify the signatures of even modest eccentricities, given sufficiently many events. Conversely, our study shows that an analysis neglecting eccentricity may draw biased conclusions about population parameters for populations with the optimistic values of eccentricity distribution used in our research.

gr-qc↗

arXiv2023

Super-resolution imaging reveals resistance to mass transfer in functionalized stationary phases

Chemical separations are costly in terms of energy, time, and money. Separation methods are optimized with inefficient trial-and-error approaches that lack insight into the molecular dynamics that lead to the success or failure of a separation and, hence, ways to improve the process. We perform super-resolution imaging of fluorescent analytes in four different commercial liquid chromatography materials. Surprisingly, we observe that chemical functionalization can block over fifty percent of the porous interior of the material, rendering it inaccessible to small molecule analytes. Only in situ imaging unveils the inaccessibility when compared to the industry-accepted ex situ characterization methods. Selectively removing some of the functionalization with solvent restores pore access without significantly altering the single-molecule kinetics that underlie the separation and agree with bulk chromatography measurements. Our molecular results determine that commercial stationary phases, marketed as fully porous, are over-functionalized and provide a new avenue to characterize and direct separation material design from the bottom-up.

physics.app-ph↗

arXiv2023

Efficacy of mannan-oligosaccharide and live yeast feed additives on performance, rumen morphology, serum biochemical parameters and muscle morphometric characteristics in buffalo calves

The objective of the current study was to assess the effect of dietary supplementations of mannan-oligosaccharide, live yeast, and a combination of these two additives on growth performance, histo-morphology of the rumen, and muscle morphometric attributes in buffalo calves. A total of twenty buffalo calves (average weight of 25 kg) having 3 months of age were distributed according to a complete randomized design. All animals were individually stalled in the shed and were fed ad-libitum. Experimental animals were divided into four groups for 67 days: Control group(without the inclusion of dietary supplementation); MOS group (Mannan oligosaccharide 5 g/clave/day; Yeast group (Live yeast 2g/calve/day) and Mixed group (MOS + Live Yeast 2.5g + 1g )/calve/day. Experimental results revealed that combined supplementation of MOS and Yeast and MOS alone resulted in an increased number of short-chain fatty acids in the rumen as well as ruminal pH (P<0.05). Results showed a significant improvement in average daily gain and FCR of MOS and Mixed supplemented groups (P<0.05). Histomorphological evaluation of rumen mucosal epithelium showed a significant improvement in the mixed-supplemented group (P<0.05) as compared to the yeast-supplemented and control groups. Muscle quality parameters such as meat texture showed significant improvement in MOS and mix-supplemented groups. Histological examination of longissimus dorsi muscle cross-section showed a significantly higher(P<0.05) muscle fiber and muscle fascicle diameter in both MOS and mix-supplemented calves groups. In conclusion, the results of this experiment revealed that the dietary addition of MOS, Live yeast, and their combination have positive effects on growth performance, rumen histology indices, and muscle morphometric features in buffalo calves.

q-bio.TO↗

arXiv2023

EMP-EVAL: A Framework for Measuring Empathy in Open Domain Dialogues

Measuring empathy in conversation can be challenging, as empathy is a complex and multifaceted psychological construct that involves both cognitive and emotional components. Human evaluations can be subjective, leading to inconsistent results. Therefore, there is a need for an automatic method for measuring empathy that reduces the need for human evaluations. In this paper, we proposed a novel approach EMP-EVAL, a simple yet effective automatic empathy evaluation method. The proposed technique takes the influence of Emotion, Cognitive and Emotional empathy. To the best knowledge, our work is the first to systematically measure empathy without the human-annotated provided scores. Experimental results demonstrate that our metrics can correlate with human preference, achieving comparable results with human judgments.

cs.AI↗

arXiv2011

A Capacity Improvement Method for CDMA based Mesh Networks in SUI Multipath Fading Channels

Code Division Multiple Access (CDMA) is the most promising candidate for wideband data access. This is due to the advantage of soft limit on the number of active mobile devices. Many wireless mesh systems impose an upper bound on the BER performance which restricts the increase in number of mobile users. Capacity is further reduced in Multipath Fading Environment (MFE). This paper presents an effective method of improving the capacity of a CDMA based mesh network by managing the transmitted powers of the mobile devices and using MMSE based Multiuser Detection (MUD). The proposed scheme improves the capacity two times as compared to the conventional CDMA based mesh network. Simulation results have been presented to demonstrate the effectiveness of the proposed scheme.

cs.IT↗

arXiv2011

An Efficient Hybrid Power Control Algorithm for Capacity Improvement of CDMA-based Fixed Wireless Applications

In Fixed Wireless Applications (FWA), the Code Division Multiple Access (CDMA) is the most promising candidate for wideband data access. The reason is the soft limit on the number of active mobile devices. Many Fixed Wireless Applications impose an upper bound on the BER performance which restricts the increase in number of mobile users. The number of active mobile users or Capacity is further reduced in Multipath Fading Environment (MFE). This paper presents an effective method of improving the capacity of CDMA based Fixed Wireless Networks by using a hybrid power control algorithm. The proposed scheme improves the capacity two times as compared to the conventional CDMA based networks. Simulation results have been presented to demonstrate the effectiveness of the proposed scheme.

cs.IT↗