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34,589 results on '"Iqbal, P."'

1. The Utilization of an Unconventional Approach to Introduce Basic Bacteriology in a Medical School Bridge Program

Author

Henna Iqbal and Kenneth Onyedibe

Abstract

Bacteria form an intense portion of reading and learning for students enrolled in microbiology education. As a part of the foundational course outline of bacteriology, bacterial classification is a significant topic of discussion. The purpose of our study was to analyze whether bacterial classification can be taught with a phylogenetic tree approach that might be more engaging and beneficial to student learners of microbiology. This methodology is unique compared to the conventional approach applied in introductory lectures of bacteriology that relies on morphology and Gram-staining to classify bacteria. The participants of this study were students enrolled in a two-semester medical school bridge program that offers a Master's degree in Pre-clinical Sciences. We presented bacterial origin and classification in the light of evolution and used a phylogenetic tree to signify clinically relevant groups of bacteria. Students were also taught the traditional bacterial classification using Gram stains and morphology. Both methods of classification were delivered in a didactic classroom session considering equal time spent and utilizing the same format. An online survey was distributed to the students after the session to collect their feedback. The results from the survey showed that 74% of participants would prefer learning bacterial classification using a combined approach that includes both Gram-staining and morphology as well as the phylogenetic tree. When asked if the study of bacterial classification through an evolutionary tree diagram is a clear and concise way of understanding bacteria, 79% of the students either agreed or strongly agreed with this statement. Interestingly, the alternative phylogenetic tree approach was considered more engaging and regarded as a means to expand the clinical knowledge of bacteria by 78% and 71% of the students, respectively. Overall, our study strongly supports the use of tree-based classification as an additional method to improve the learning of medically important groups of bacteria at varying levels of education.

Published
2024

2. Academic Dishonesty in Distance Education Courses: A Quasi-Experimental Study

Author

Nayab Iqbal and Kaukab Abid Azhar

Abstract

This research study focuses on the growing concern of academic misconduct in distance education courses. A quasi-experimental study was conducted to measure the impact of introducing webcam recording software as an online supervision tool for high-stakes exams in two separate online courses (Management and Accounting). Results revealed that overall performance decreased following implementation of the software, and persisted after taking potential confounding factors into account. Additionally, the explanatory power of the regression analysis was higher for scores under supervision, which suggests that cheating was occurring before online supervision was introduced. It can be concluded from this study that online supervision is an effective tool to combat academic dishonesty in distance education courses.

Published
2024

3. Exploring the Relationship between Test-Anxiety, Emotional Intelligence and Academic Performance among University Students

Author

Khalida Rauf and Naveed Iqbal

Abstract

This study was conducted to find the relationship of test-anxiety, emotional intelligence and academic performance of public private sector university students. A sample of 388 students, 194 male and 194 females was drawn through convenient sampling from the Public and Private universities of Karachi in Pakistan, using a cross sectional design. Test anxiety was assessed through Westside Test Anxiety Scale and emotional intelligence was assessed using The Schutte Emotional Intelligence Scale. Performance of the last two semesters attended by the students was used to assess academic performance and demographic information was gathered through the demographic form. The "Pearson Product Correlation" was calculated to find the relation between test anxiety and emotional intelligence and academic performance. t-test for independent sample was used to obtain the differences in test-anxiety and emotional intelligence and gender in Public and Private sector university students' academic performance on the respective variables. The results revealed an inverse relationship between test-anxiety and emotional intelligence, academic performance. However, the results showed a positive relation between emotional intelligence and academic performance. Gender differences were found in test anxiety but not in emotional intelligence. The students of both the sectors differed in test anxiety, whereas both the groups did not differ on the scores of emotional intelligence. It is incumbent on educational institutes to provide psychological services to students who find it hard to handle exam situations in the same way as financial assistance is provided to those who need it.

Published
2024

4. Students' Behavioral Problems in the Classroom and Coping Strategies: Experiences of Secondary School Teachers

Author

Zafar Iqbal and Irsa Zahoor

Abstract

This study aims to investigate the most prevalent and unacceptable classroom behavioral issues among public secondary school students in the Islamabad Capital Territory (ICT). Moreover, the study identified the reasons for these problem behaviors and ways to address these behaviors. The sample of the study was twenty-three (23) teachers (Female: 14 & Male: 09) having minimum sixteen years of education along with professional qualification. Teachers have wide experience of teaching ranging from 10-32 years and teach students aged between thirteen to fifteen years. In this qualitative research data was collected through semi-structured individualized interviews after getting consent from the Federal Directorate of Education and from study participants. Through thematic analysis, the main themes emerged were "lack of attention, creating a disturbance in the class, disobedience, and display of aggressive behaviors." The key causes of classroom problem behaviors were "parenting style, teachers' attitude/teaching strategies, society, home environment, and social media." Moreover, teachers also think that "lack of counseling" and "character building" along with "outdated teaching strategies and a boring classroom environment" were causes behavioral problems in the class. Teachers should incorporate student-centered teaching with engaging techniques, parents should pay attention in developing good relationship with children at home. Moreover, counseling facilities about psychological issues of teenage students' needs to be provided at school level. The limited yet empirical data can form a logical foundation for management to create a policy document addressing these issues in schools, offering guidance for parents and teachers to tackle student challenges effectively. [Note: The page range (107-128) shown on the PDF is incorrect. The correct page range is 107-129.]

Published
2024

6. Machine learning and optimization-based approaches to duality in statistical physics

Author

Ferrari, Andrea E. V., Gupta, Prateek, and Iqbal, Nabil

Subjects
Condensed Matter - Statistical Mechanics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, High Energy Physics - Theory
Abstract

The notion of duality -- that a given physical system can have two different mathematical descriptions -- is a key idea in modern theoretical physics. Establishing a duality in lattice statistical mechanics models requires the construction of a dual Hamiltonian and a map from the original to the dual observables. By using simple neural networks to parameterize these maps and introducing a loss function that penalises the difference between correlation functions in original and dual models, we formulate the process of duality discovery as an optimization problem. We numerically solve this problem and show that our framework can rediscover the celebrated Kramers-Wannier duality for the 2d Ising model, reconstructing the known mapping of temperatures. We also discuss an alternative approach which uses known features of the mapping of topological lines to reduce the problem to optimizing the couplings in a dual Hamiltonian, and explore next-to-nearest neighbour deformations of the 2d Ising duality. We discuss future directions and prospects for discovering new dualities within this framework., Comment: 27 pages + appendices, lots of plots

Published
2024

7. Qutrit Toric Code and Parafermions in Trapped Ions

Author

Iqbal, Mohsin, Lyons, Anasuya, Lo, Chiu Fan Bowen, Tantivasadakarn, Nathanan, Dreiling, Joan, Foltz, Cameron, Gatterman, Thomas M., Gresh, Dan, Hewitt, Nathan, Holliman, Craig A., Johansen, Jacob, Neyenhuis, Brian, Matsuoka, Yohei, Mills, Michael, Moses, Steven A., Siegfried, Peter, Vishwanath, Ashvin, Verresen, Ruben, and Dreyer, Henrik

Subjects
Quantum Physics, Condensed Matter - Strongly Correlated Electrons
Abstract

The development of programmable quantum devices can be measured by the complexity of manybody states that they are able to prepare. Among the most significant are topologically ordered states of matter, which enable robust quantum information storage and processing. While topological orders are more readily accessible with qudits, experimental realisations have thus far been limited to lattice models of qubits. Here, we prepare a ground state of the Z3 toric code state on 24 qutrits in a trapped ion quantum processor with fidelity per qutrit exceeding 96.5(3)%. We manipulate two types of defects which go beyond the conventional qubit toric code: a parafermion, and its bound state which is related to charge conjugation symmetry. We further demonstrate defect fusion and the transfer of entanglement between anyons and defects, which we use to control topological qutrits. Our work opens up the space of long-range entangled states with qudit degrees of freedom for use in quantum simulation and universal error-correcting codes., Comment: 8+20 pages, 15 figures

Published
2024

8. Kitaev Model on Hurwitz Tilings: A Non-Abelian Gapped Chiral Spin Liquid

Author

Mosseri, Rémy, Iqbal, Yasir, Vogeler, Roger, and Vidal, Julien

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

The Kitaev model defined on the trivalent heptagonal hyperbolic tiling is shown to be a singular case which realizes a non-Abelian gapped chiral spin liquid at the isotropic point in a regular tiling., Comment: 6 pages, 2 figures, 1 table, and Supplemental Material

Published
2024

9. bursty_dynamics: A Python Package for Exploring the Temporal Properties of Longitudinal Data

Author

Angdembe, Alisha, Iqbal, Wasim A, Hamad, Rebeen Ali, Casement, John, Consortium, AI-Multiply, Missier, Paolo, Reynolds, Nick, Henkin, Rafael, and Barnes, Michael R

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Understanding the temporal properties of longitudinal data is critical for identifying trends, predicting future events, and making informed decisions in any field where temporal data is analysed, including health and epidemiology, finance, geosciences, and social sciences. Traditional time-series analysis techniques often fail to capture the complexity of irregular temporal patterns present in such data. To address this gap, we introduce bursty_dynamics, a Python package that enables the quantification of bursty dynamics through the calculation of the Burstiness Parameter (BP) and Memory Coefficient (MC). In temporal data, BP and MC provide insights into the irregularity and temporal dependencies within event sequences, shedding light on complex patterns of disease aetiology, human behaviour, or other information diffusion over time. An event train detection method is also implemented to identify clustered events occurring within a specified time interval, allowing for more focused analysis with reduced noise. With built-in visualisation tools, bursty_dynamics provides an accessible yet powerful platform for researchers to explore and interpret the temporal dynamics of longitudinal data. This paper outlines the core functionalities of the package, demonstrates its applications in diverse research domains, and discusses the advantages of using BP, MC, and event train detection for enhanced temporal data analysis.

Published
2024

10. Quantum Token Obfuscation via Superposition

Author

Tomal, S. M. Yousuf Iqbal and Shafin, Abdullah Al

Subjects
Quantum Physics, Computer Science - Cryptography and Security
Abstract

As quantum computing advances, traditional cryptographic security measures, including token obfuscation, are increasingly vulnerable to quantum attacks. This paper introduces a quantum-enhanced approach to token obfuscation leveraging quantum superposition and multi-basis verification to establish a robust defense against these threats. In our method, tokens are encoded in superposition states, making them simultaneously exist in multiple states until measured, thus enhancing obfuscation complexity. Multi-basis verification further secures these tokens by enforcing validation across multiple quantum bases, thwarting unauthorized access. Additionally, we incorporate a quantum decay protocol and a refresh mechanism to manage the token life-cycle securely. Our experimental results demonstrate significant improvements in token security and robustness, validating this approach as a promising solution for quantum-secure cryptographic applications. This work not only highlights the feasibility of quantum-based token obfuscation but also lays the foundation for future quantum-safe security architectures., Comment: 10 pages, 1 figure, 1 table

Published
2024

11. Probing Broadband Spectral Energy Distribution and Variability of Mrk\,501 in the low flux state

Author

Tantry, Javaid, Shah, Zahir, Misra, Ranjeev, Iqbal, Naseer, and Akbar, Sikandar

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We conducted a multi-wavelength analysis of the blazar Mrk\,501, utilizing observations from \emph{Astro}Sat (SXT, LAXPC), \emph{Swift-UVOT}, and \emph{Fermi-LAT} during the period August 15, 2016 to March 27, 2022. The resulting multi-wavelength light curve revealed relatively low activity of the source across the electromagnetic spectrum. Notably, logparabola and broken power-law models provided a better fit to the joint X-ray spectra from \emph{Astro}Sat-SXT/LAXPC instruments compared to the power-law model. During the low activity state, the source showed the characteristic harder when brighter trend at the X-ray energies. To gain insights into underlying physical processes responsible for the broadband emission, we performed a detailed broadband spectral analysis using the convolved one-zone leptonic model with different forms of particle distributions such as logparabola (LP), broken power-law (BPL), power-law model with maximum energy ($\xi_{max}$), and energy-dependent acceleration (EDA) models. Our analysis revealed similar reduced-$\chi^2$ values for the four particle distributions. The LP and EDA models exhibited the lowest jet powers. The correlation analyses conducted for the LP and BPL models revealed that there is a positive correlation between jet power and bulk Lorentz factor. Specifically, in the LP model, jet power proved independent of $\gamma_{min}$, whereas in the broken power-law model, jet power decreased with an increase in $\gamma_{min}$. The jet power in the LP/EDA particle distribution is nearly 10 percent of the Eddington luminosity of a $10^7$ M$_\odot$ black hole. This result suggests that the jet could potentially be fueled by accretion processes., Comment: Accepted for publication in Journal of High Energy Astrophysics (JHEAP)

Published
2024

12. Polarization-dependent observables in $H\to \ell^{+}\ell^{-} \gamma$ in the SM

Author

Hasan, Usman, Uzair, A., Junaid, M., Tariq, Bilal, Ahmed, Ishtiaq, and Iqbal, Shahin

Subjects
High Energy Physics - Phenomenology
Abstract

The rare three body decay of a Higgs boson to a lepton-anti lepton pair and a photon has begun to attract attention, after the first evidence for the $H\to Z\gamma$ at CMS and ATLAS, which is a sub process of $H \to \ell^+ \ell^- \gamma$ . To investigate some important features of this process, we suggest that the polarized forward-backward and the photon polarization asymmetries could be useful to probe its important properties, such as the behavior of Yukawa coupling, resonance, and non-resonance contributions. Our analysis introduces a comprehensive framework to evaluate the aforementioned polarization-dependent observables. By analyzing the polarization effects of the final-state photon and lepton separately on forward-backward asymmetries, we demonstrate that loop-induced contributions play a significant role to investigate these asymmetries. Unlike the unpolarized case, where the interference effects of resonance and non-resonance effects are minimal, we show that polarization dependent observables offer a powerful tool to analyze these features of this decay mode. Furthermore, these observables can provide a handy tool for probing possible signatures of physics beyond the SM.

Published
2024

13. Insights into the Long-Term Flaring Events of Blazar PKS 0805-07: A Multi-Wavelength Analysis over the 2009-2023 Period

Author

Dar, Sikandar A., Shah, Zahir, Misra, Ranjeev, and Iqbal, Naseer

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We conducted a comprehensive temporal and spectral study of the FSRQ PKS 0805-07 by using the broadband observations from the Fermi-LAT and Swift-XRT/UVOT instruments over the period MJD 54684-60264. The 3-day binned $\gamma$-ray light curve during the active state, revealed eleven distinct peak structures with the maximum integral flux (E $>$ 100 MeV) reached $(1.56\pm 0.16)\times10^{-6}\, \text{photons cm}^{-2}\, \text{s}^{-1}$ on MJD 59904.5. The shortest observed $\gamma$-ray variability was $2.80\pm 0.77$ days. A correlation analysis between the $\gamma$-ray spectral index and flux indicated the typical trend of hardening when the source is brighter, commonly observed in blazars. We identified a lag of 121 (+27.21, -3.51) days in the spectral index relative to the flux, within the time interval MJD 59582 to 60112. The Anderson-Darling test and histogram-fit rejected the normality of the $\gamma$-ray flux distribution, and instead suggest a log-normal distribution. To gain insight into the underlying physical processes, we extracted broadband spectra from different time periods in the light curve. The spectral energy distribution during various flux states were well-reproduced using synchrotron, synchrotron-self-Compton, and external-Compton emissions from a broken power-law electron distribution. The seed photons required for the external Compton process are from IR region. A comparison of the best-fit physical parameters indicated that the variations in different flux states were primarily associated with an increase in the bulk Lorentz factor and magnetic field from low to high flux states.

Published
2024

14. MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation

Author

Shah, Ovais Iqbal, Rizvi, Danish Raza, and Mir, Aqib Nazir

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Medical image segmentation is pivotal in healthcare, enhancing diagnostic accuracy, informing treatment strategies, and tracking disease progression. This process allows clinicians to extract critical information from visual data, enabling personalized patient care. However, developing neural networks for segmentation remains challenging, especially when preserving image resolution, which is essential in detecting subtle details that influence diagnoses. Moreover, the lack of transparency in these deep learning models has slowed their adoption in clinical practice. Efforts in model interpretability are increasingly focused on making these models' decision-making processes more transparent. In this paper, we introduce MAPUNetR, a novel architecture that synergizes the strengths of transformer models with the proven U-Net framework for medical image segmentation. Our model addresses the resolution preservation challenge and incorporates attention maps highlighting segmented regions, increasing accuracy and interpretability. Evaluated on the BraTS 2020 dataset, MAPUNetR achieved a dice score of 0.88 and a dice coefficient of 0.92 on the ISIC 2018 dataset. Our experiments show that the model maintains stable performance and potential as a powerful tool for medical image segmentation in clinical practice.

Published
2024

15. Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules

Author

Rahman, Md Abdur, Barek, Md Abdul, Riad, ABM Kamrul Islam, Rahman, Md Mostafizur, Rashid, Md Bajlur, Ambedkar, Smita, Miaa, Md Raihan, Wu, Fan, Cuzzocrea, Alfredo, and Ahamed, Sheikh Iqbal

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to various machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95\%. Additionally, we obtained high accuracy from Support Vector Machine (99.79\%), Random Forest (99.73\%), and Naive Bayes (95.93\%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development., Comment: I am requesting the withdrawal of my paper due to critical issues identified in the methodology/results that may impact its accuracy and reliability. I also plan to make substantial revisions that go beyond minor corrections

Published
2024

16. Understanding Communication Preferences of Information Workers in Engagement with Text-Based Conversational Agents

Author

Bhattacharjee, Ananya, Suh, Jina, Ershadi, Mahsa, Iqbal, Shamsi T., Wilson, Andrew D., and Hernandez, Javier

Subjects
Computer Science - Human-Computer Interaction
Abstract

Communication traits in text-based human-AI conversations play pivotal roles in shaping user experiences and perceptions of systems. With the advancement of large language models (LLMs), it is now feasible to analyze these traits at a more granular level. In this study, we explore the preferences of information workers regarding chatbot communication traits across seven applications. Participants were invited to participate in an interactive survey, which featured adjustable sliders, allowing them to adjust and express their preferences for five key communication traits: formality, personification, empathy, sociability, and humor. Our findings reveal distinct communication preferences across different applications; for instance, there was a preference for relatively high empathy in wellbeing contexts and relatively low personification in coding. Similarities in preferences were also noted between applications such as chatbots for customer service and scheduling. These insights offer crucial design guidelines for future chatbots, emphasizing the need for nuanced trait adjustments for each application.

Published
2024

17. Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detection

Author

Rahman, Md Abdur, Wu, Fan, Cuzzocrea, Alfredo, and Ahamed, Sheikh Iqbal

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. These attacks target LLMs applications through using carefully designed input prompts to divert the model from adhering to original instruction, thereby it could execute unintended actions. These manipulations pose serious security threats which potentially results in data leaks, biased outputs, or harmful responses. This project explores the security vulnerabilities in relation to prompt injection attacks. To detect whether a prompt is vulnerable or not, we follows two approaches: 1) a pre-trained LLM, and 2) a fine-tuned LLM. Then, we conduct a thorough analysis and comparison of the classification performance. Firstly, we use pre-trained XLM-RoBERTa model to detect prompt injections using test dataset without any fine-tuning and evaluate it by zero-shot classification. Then, this proposed work will apply supervised fine-tuning to this pre-trained LLM using a task-specific labeled dataset from deepset in huggingface, and this fine-tuned model achieves impressive results with 99.13\% accuracy, 100\% precision, 98.33\% recall and 99.15\% F1-score thorough rigorous experimentation and evaluation. We observe that our approach is highly efficient in detecting prompt injection attacks., Comment: I am requesting the withdrawal of my paper due to critical issues identified in the methodology/results that may impact its accuracy and reliability. I also plan to make substantial revisions that go beyond minor corrections

Published
2024

18. Monopole excitations in the $U(1)$ Dirac spin liquid on the triangular lattice

Author

Budaraju, Sasank, Parola, Alberto, Iqbal, Yasir, Becca, Federico, and Poilblanc, Didier

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

The $U(1)$ Dirac spin liquid might realize an exotic phase of matter whose low-energy properties are described by quantum electrodynamics in $2+1$ dimensions, where gapless modes exists but spinons and gauge fields are strongly coupled. Its existence has been proposed in frustrated Heisenberg models in presence of frustrating super-exchange interactions, by the (Abrikosov) fermionic representation of the spin operators [X.-G. Wen, Phys. Rev. B 65, 165113 (2002)}], supplemented by the Gutzwiller projection. Here, we construct charge-$Q$ monopole excitations in the Heisenberg model on the triangular lattice with nearest- ($J_1$) and next-neighbor ($J_2$) couplings. In the highly frustrated regime, singlet and triplet monopoles with $Q=1$ become gapless in the thermodynamic limit; in addition, the energies for generic $Q$ agree with field-theoretical predictions, obtained for a large number of gapless fermion modes. Finally, we consider localized gauge excitations, in which magnetic $\pi$-fluxes are concentrated in the triangular plaquettes (in analogy with $\mathbb{Z}_2$ visons), showing that these kind of states do not play a relevant role at low-energies. All our findings lend support to a stable $U(1)$ Dirac spin liquid in the $J_1-J_2$ Heisenberg model on the triangular lattice., Comment: 9 pages, 7 figures

Published
2024

19. Search for gravitational waves emitted from SN 2023ixf

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. 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C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

Published
2024

20. Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification

Author

Tomal, S. M. Yousuf Iqbal, Shafin, Abdullah Al, Afaf, Afrida, and Bhattacharjee, Debojit

Subjects
Quantum Physics, Computer Science - Artificial Intelligence
Abstract

This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achieving a perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterized gate selection, and the integration of the quantum layer with classical neural network components. This work contributes to the growing body of research on hybrid quantum-classical models and their applicability to real-world datasets., Comment: 13 pages, 2 figures, 1 table, Quantum Machine Learning

Published
2024

21. DM-Codec: Distilling Multimodal Representations for Speech Tokenization

Author

Ahasan, Md Mubtasim, Fahim, Md, Mohiuddin, Tasnim, Rahman, A K M Mahbubur, Chadha, Aman, Iqbal, Tariq, Amin, M Ashraful, Islam, Md Mofijul, and Ali, Amin Ahsan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional attributes of speech into discrete tokens remains challenging. This process demands acoustic, semantic, and contextual information for precise speech representations. Existing speech representations generally fall into two categories: acoustic tokens from audio codecs and semantic tokens from speech self-supervised learning models. Although recent efforts have unified acoustic and semantic tokens for improved performance, they overlook the crucial role of contextual representation in comprehensive speech modeling. Our empirical investigations reveal that the absence of contextual representations results in elevated Word Error Rate (WER) and Word Information Lost (WIL) scores in speech transcriptions. To address these limitations, we propose two novel distillation approaches: (1) a language model (LM)-guided distillation method that incorporates contextual information, and (2) a combined LM and self-supervised speech model (SM)-guided distillation technique that effectively distills multimodal representations (acoustic, semantic, and contextual) into a comprehensive speech tokenizer, termed DM-Codec. The DM-Codec architecture adopts a streamlined encoder-decoder framework with a Residual Vector Quantizer (RVQ) and incorporates the LM and SM during the training process. Experiments show DM-Codec significantly outperforms state-of-the-art speech tokenization models, reducing WER by up to 13.46%, WIL by 9.82%, and improving speech quality by 5.84% and intelligibility by 1.85% on the LibriSpeech benchmark dataset. The code, samples, and model checkpoints are available at https://github.com/mubtasimahasan/DM-Codec.

Published
2024

22. FIRE: Fact-checking with Iterative Retrieval and Verification

Author

Xie, Zhuohan, Xing, Rui, Wang, Yuxia, Geng, Jiahui, Iqbal, Hasan, Sahnan, Dhruv, Gurevych, Iryna, and Nakov, Preslav

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Fact-checking long-form text is challenging, and it is therefore common practice to break it down into multiple atomic claims. The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step. However, this method is usually not cost-effective, as it underutilizes the verification model's internal knowledge of the claim and fails to replicate the iterative reasoning process in human search strategies. To address these limitations, we propose FIRE, a novel agent-based framework that integrates evidence retrieval and claim verification in an iterative manner. Specifically, FIRE employs a unified mechanism to decide whether to provide a final answer or generate a subsequent search query, based on its confidence in the current judgment. We compare FIRE with other strong fact-checking frameworks and find that it achieves slightly better performance while reducing large language model (LLM) costs by an average of 7.6 times and search costs by 16.5 times. These results indicate that FIRE holds promise for application in large-scale fact-checking operations. Our code is available at https://github.com/mbzuai-nlp/fire.git., Comment: 13 pages, 4 figures, 8 tables

Published
2024

23. Data Driven Environmental Awareness Using Wireless Signals for Efficient Spectrum Sharing

Author

Nasiri, Hossein, Dogan-Tusha, Seda, Rochman, Muhammad Iqbal, and Ghosh, Monisha

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning
Abstract

Robust classification of the operational environment of wireless devices is becoming increasingly important for wireless network optimization, particularly in a shared spectrum environment. Distinguishing between indoor and outdoor devices can enhance reliability and improve coexistence with existing, outdoor, incumbents. For instance, the unlicensed but shared 6 GHz band (5.925 - 7.125 GHz) enables sharing by imposing lower transmit power for indoor unlicensed devices and a spectrum coordination requirement for outdoor devices. Further, indoor devices are prohibited from using battery power, external antennas, and weatherization to prevent outdoor operations. As these rules may be circumvented, we propose a robust indoor/outdoor classification method by leveraging the fact that the radio-frequency environment faced by a device are quite different indoors and outdoors. We first collect signal strength data from all cellular and Wi-Fi bands that can be received by a smartphone in various environments (indoor interior, indoor near windows, and outdoors), along with GPS accuracy, and then evaluate three machine learning (ML) methods: deep neural network (DNN), decision tree, and random forest to perform classification into these three categories. Our results indicate that the DNN model performs the best, particularly in minimizing the most important classification error, that of classifying outdoor devices as indoor interior devices.

Published
2024

24. Field-induced spin liquid in the decorated square-kagome antiferromagnet nabokoite KCu$_7$TeO$_4$(SO$_4$)$_5$Cl

Author

Gonzalez, Matías G., Iqbal, Yasir, Reuther, Johannes, and Jeschke, Harald O.

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Materials Science
Abstract

Quantum antiferromagnets realizing the square-kagome lattice are proving to be a promising platform for nontrivial phenomena in frustrated magnetism. We carry out a theoretical analysis of the recently synthesized compound nabokoite {\nab} based on a density functional theory energy mapping which establishes an unusual Heisenberg Hamiltonian with a hierarchy of couplings which suggests a description in terms of effective spin liquid models. Owing to the decoration of the square-kagome lattice with a seventh Cu site, nabokoite is characterized by highly frustrated square pyramids that are antiferromagnetically coupled in a chiral arrangement. Employing classical Monte Carlo simulations, we are able to explain the two transitions observed in the low-temperature magnetization curve from the experimental measurements. Remarkably, we show that the low-field phase is only realized when the inter-layer coupling is included. On the other hand, the intermediate-field phase is also found in a purely two-dimensional model and is described by a spin liquid featuring a subextensive degeneracy with a ferrimagnetic component. We show that this phase can be approximated by a checkerboard lattice in a magnetic field. We finally assess the effects of quantum fluctuations in zero fields using the pseudo-Majorana functional renormalization group method and predict the spin structure factor for comparison with future neutron scattering measurements.

Published
2024

25. What Am I? Evaluating the Effect of Language Fluency and Task Competency on the Perception of a Social Robot

Author

Ali, Shahira, Green, Haley N., and Iqbal, Tariq

Subjects
Computer Science - Robotics
Abstract

Recent advancements in robot capabilities have enabled them to interact with people in various human-social environments (HSEs). In many of these environments, the perception of the robot often depends on its capabilities, e.g., task competency, language fluency, etc. To enable fluent human-robot interaction (HRI) in HSEs, it is crucial to understand the impact of these capabilities on the perception of the robot. Although many works have investigated the effects of various robot capabilities on the robot's perception separately, in this paper, we present a large-scale HRI study (n = 60) to investigate the combined impact of both language fluency and task competency on the perception of a robot. The results suggest that while language fluency may play a more significant role than task competency in the perception of the verbal competency of a robot, both language fluency and task competency contribute to the perception of the intelligence and reliability of the robot. The results also indicate that task competency may play a more significant role than language fluency in the perception of meeting expectations and being a good teammate. The findings of this study highlight the relationship between language fluency and task competency in the context of social HRI and will enable the development of more intelligent robots in the future., Comment: Accepted at the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2024

Published
2024

26. A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. 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L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R. ., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. 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Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

Published
2024

27. CoHRT: A Collaboration System for Human-Robot Teamwork

Author

Sarker, Sujan, Green, Haley N., Yasar, Mohammad Samin, and Iqbal, Tariq

Subjects
Computer Science - Robotics, Computer Science - Human-Computer Interaction
Abstract

Collaborative robots are increasingly deployed alongside humans in factories, hospitals, schools, and other domains to enhance teamwork and efficiency. Systems that seamlessly integrate humans and robots into cohesive teams for coordinated and efficient task execution are needed, enabling studies on how robot collaboration policies affect team performance and teammates' perceived fairness, trust, and safety. Such a system can also be utilized to study the impact of a robot's normative behavior on team collaboration. Additionally, it allows for investigation into how the legibility and predictability of robot actions affect human-robot teamwork and perceived safety and trust. Existing systems are limited, typically involving one human and one robot, and thus require more insight into broader team dynamics. Many rely on games or virtual simulations, neglecting the impact of a robot's physical presence. Most tasks are turn-based, hindering simultaneous execution and affecting efficiency. This paper introduces CoHRT (Collaboration System for Human-Robot Teamwork), which facilitates multi-human-robot teamwork through seamless collaboration, coordination, and communication. CoHRT utilizes a server-client-based architecture, a vision-based system to track task environments, and a simple interface for team action coordination. It allows for the design of tasks considering the human teammates' physical and mental workload and varied skill labels across the team members. We used CoHRT to design a collaborative block manipulation and jigsaw puzzle-solving task in a team of one Franka Emika Panda robot and two humans. The system enables recording multi-modal collaboration data to develop adaptive collaboration policies for robots. To further utilize CoHRT, we outline potential research directions in diverse human-robot collaborative tasks., Comment: 8 Pages, Robotics Science and Systems (RSS), Safety and Normative Behaviors in Human-Robot Interaction Workshop 2024 (accepted), https://sites.google.com/view/safe-hri/accepted-papers

Published
2024

28. A functional equation for monomial functions

Author

Gselmann, Eszter and Iqbal, Mehak

Subjects
Mathematics - Number Theory, Mathematics - Commutative Algebra, Primary 39B55, Secondary 39B72
Abstract

Let $\mathbb{F}\subset \mathbb{K}$ be fields with characteristic zero, $n$ be a positive integer and $\kappa\in \mathbb{K}$. In this paper, we determine those monomials $f\colon \mathbb{F}\to \mathbb{K}$ of degree $n$ for which \[ f(x^{2})= \kappa\cdot x^{n}f(x) \] holds for all $x\in \mathbb{F}$. We show that similar to the classical results, where additive functions were considered, the monomial functions in the equation can be represented with the aid of homomorphisms and higher-order derivations.

Published
2024

29. RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis

Author

Khan, Saddam Hussain and Iqbal, Rashid

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Monkeypox (MPox) has emerged as a significant global concern, with cases steadily increasing daily. Conventional detection methods, including polymerase chain reaction (PCR) and manual examination, exhibit challenges of low sensitivity, high cost, and substantial workload. Therefore, deep learning offers an automated solution; however, the datasets include data scarcity, texture, contrast, inter-intra class variability, and similarities with other skin infectious diseases. In this regard, a novel hybrid approach is proposed that integrates the learning capacity of Residual Learning and Spatial Exploitation Convolutional Neural Network (CNN) with a customized Swin Transformer (RS-FME-SwinT) to capture multi-scale global and local correlated features for MPox diagnosis. The proposed RS-FME-SwinT technique employs a transfer learning-based feature map enhancement (FME) technique, integrating the customized SwinT for global information capture, residual blocks for texture extraction, and spatial blocks for local contrast variations. Moreover, incorporating new inverse residual blocks within the proposed SwinT effectively captures local patterns and mitigates vanishing gradients. The proposed RS-FME-SwinT has strong learning potential of diverse features that systematically reduce intra-class MPox variation and enable precise discrimination from other skin diseases. Finally, the proposed RS-FME-SwinT is a holdout cross-validated on a diverse MPox dataset and achieved outperformance on state-of-the-art CNNs and ViTs. The proposed RS-FME-SwinT demonstrates commendable results of an accuracy of 97.80%, sensitivity of 96.82%, precision of 98.06%, and an F-score of 97.44% in MPox detection. The RS-FME-SwinT could be a valuable tool for healthcare practitioners, enabling prompt and accurate MPox diagnosis and contributing significantly to mitigation efforts., Comment: 37 Pages, 5 Tables, 10 Figures

Published
2024

30. EfficientCrackNet: A Lightweight Model for Crack Segmentation

Author

Zim, Abid Hasan, Iqbal, Aquib, Al-Huda, Zaid, Malik, Asad, and Kuribayash, Minoru

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Crack detection, particularly from pavement images, presents a formidable challenge in the domain of computer vision due to several inherent complexities such as intensity inhomogeneity, intricate topologies, low contrast, and noisy backgrounds. Automated crack detection is crucial for maintaining the structural integrity of essential infrastructures, including buildings, pavements, and bridges. Existing lightweight methods often face challenges including computational inefficiency, complex crack patterns, and difficult backgrounds, leading to inaccurate detection and impracticality for real-world applications. To address these limitations, we propose EfficientCrackNet, a lightweight hybrid model combining Convolutional Neural Networks (CNNs) and transformers for precise crack segmentation. EfficientCrackNet integrates depthwise separable convolutions (DSC) layers and MobileViT block to capture both global and local features. The model employs an Edge Extraction Method (EEM) and for efficient crack edge detection without pretraining, and Ultra-Lightweight Subspace Attention Module (ULSAM) to enhance feature extraction. Extensive experiments on three benchmark datasets Crack500, DeepCrack, and GAPs384 demonstrate that EfficientCrackNet achieves superior performance compared to existing lightweight models, while requiring only 0.26M parameters, and 0.483 FLOPs (G). The proposed model offers an optimal balance between accuracy and computational efficiency, outperforming state-of-the-art lightweight models, and providing a robust and adaptable solution for real-world crack segmentation.

Published
2024

31. A Scalable Data-Driven Framework for Systematic Analysis of SEC 10-K Filings Using Large Language Models

Author

Daimi, Syed Affan and Iqbal, Asma

Subjects
Computer Science - Artificial Intelligence
Abstract

The number of companies listed on the NYSE has been growing exponentially, creating a significant challenge for market analysts, traders, and stockholders who must monitor and assess the performance and strategic shifts of a large number of companies regularly. There is an increasing need for a fast, cost-effective, and comprehensive method to evaluate the performance and detect and compare many companies' strategy changes efficiently. We propose a novel data-driven approach that leverages large language models (LLMs) to systematically analyze and rate the performance of companies based on their SEC 10-K filings. These filings, which provide detailed annual reports on a company's financial performance and strategic direction, serve as a rich source of data for evaluating various aspects of corporate health, including confidence, environmental sustainability, innovation, and workforce management. We also introduce an automated system for extracting and preprocessing 10-K filings. This system accurately identifies and segments the required sections as outlined by the SEC, while also isolating key textual content that contains critical information about the company. This curated data is then fed into Cohere's Command-R+ LLM to generate quantitative ratings across various performance metrics. These ratings are subsequently processed and visualized to provide actionable insights. The proposed scheme is then implemented on an interactive GUI as a no-code solution for running the data pipeline and creating the visualizations. The application showcases the rating results and provides year-on-year comparisons of company performance., Comment: 10 pages, 7 figures

Published
2024

32. Fermionic parton theory of Rydberg $\mathbb{Z}_2$ quantum spin liquids

Author

Maity, Atanu, Iqbal, Yasir, and Samajdar, Rhine

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Quantum Gases, Quantum Physics
Abstract

Programmable quantum simulators based on neutral atom arrays today offer powerful platforms for studying strongly correlated phases of quantum matter. Here, we employ the projective symmetry group framework to describe the symmetry fractionalization patterns in a topologically ordered $\mathbb{Z}_{2}$ quantum spin liquid (QSL) synthesized in such a Rydberg array on the ruby lattice. By systematically comparing the static structure factors of $\textit{all}$ possible mean-field $\textit{Ans\"atze}$ against density-matrix renormalization group calculations, we identify a promising candidate for the precise $\mathbb{Z}_{2}$ QSL realized microscopically. We also present detailed analyses of the dynamical structure factors as a reference for future experiments and showcase how these spin correlations can differentiate between varied QSL $\textit{Ans\"atze}$., Comment: 7+17 pages, 3+12 figures

Published
2024

33. Gapped and gapless quantum spin liquids on the ruby lattice

Author

Maity, Atanu, Samajdar, Rhine, and Iqbal, Yasir

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Condensed Matter - Quantum Gases, Quantum Physics
Abstract

The ruby lattice has been the subject of much interest recently due its realization in Rydberg atom arrays, where its rich variety of frustrated interactions gives rise to topologically ordered quantum spin liquids. Similarly, numerical studies of ruby-lattice spin models, with both isotropic and anisotropic interactions, have provided evidence of gapped and gapless spin-liquid ground states with different low-energy gauge structures. Motivated by these findings, we perform a projective symmetry group (PSG) classification of U(1) and $\mathbb{Z}_{2}$ fermionic spinon mean-field theories$\unicode{x2014}$respecting space-group and time-reversal symmetries$\unicode{x2014}$for $S=1/2$ spins. We obtain a total of 50 U(1) and 64 $\mathbb{Z}_{2}$ PSGs, and upon restricting their realization via mean-field $\textit{Ans\"atze}$ with up to second-nearest-neighbor singlet amplitudes (relevant to the models studied here), only 8 U(1) and 18 $\mathbb{Z}_{2}$ distinct states are obtained. We present the singlet fields for all $\textit{Ans\"atze}$ up to third-nearest-neighboring bonds and discuss their spinon dispersions as well as their dynamical spin structure factors. Building on this information, we also obtain the phase diagram of the Heisenberg model in the presence of first ($J_{1}$), second ($J_{1}'$), and third ($J_{2}$) neighbor antiferromagnetic couplings within a self-consistent mean-field approximation., Comment: 21 pages, 10 figures

Published
2024

34. Training Language Models to Self-Correct via Reinforcement Learning

Author

Kumar, Aviral, Zhuang, Vincent, Agarwal, Rishabh, Su, Yi, Co-Reyes, John D, Singh, Avi, Baumli, Kate, Iqbal, Shariq, Bishop, Colton, Roelofs, Rebecca, Zhang, Lei M, McKinney, Kay, Shrivastava, Disha, Paduraru, Cosmin, Tucker, George, Precup, Doina, Behbahani, Feryal, and Faust, Aleksandra

Subjects
Computer Science - Machine Learning
Abstract

Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Current methods for training self-correction typically depend on either multiple models, a more advanced model, or additional forms of supervision. To address these shortcomings, we develop a multi-turn online reinforcement learning (RL) approach, SCoRe, that significantly improves an LLM's self-correction ability using entirely self-generated data. To build SCoRe, we first show that variants of supervised fine-tuning (SFT) on offline model-generated correction traces are often insufficient for instilling self-correction behavior. In particular, we observe that training via SFT falls prey to either a distribution mismatch between mistakes made by the data-collection policy and the model's own responses, or to behavior collapse, where learning implicitly prefers only a certain mode of correction behavior that is often not effective at self-correction on test problems. SCoRe addresses these challenges by training under the model's own distribution of self-generated correction traces and using appropriate regularization to steer the learning process into learning a self-correction behavior that is effective at test time as opposed to fitting high-reward responses for a given prompt. This regularization process includes an initial phase of multi-turn RL on a base model to generate a policy initialization that is less susceptible to collapse, followed by using a reward bonus to amplify self-correction. With Gemini 1.0 Pro and 1.5 Flash models, we find that SCoRe achieves state-of-the-art self-correction performance, improving the base models' self-correction by 15.6% and 9.1% respectively on MATH and HumanEval.

Published
2024

35. Latent fingerprint enhancement for accurate minutiae detection

Author

Wahab, Abdul, Khan, Tariq Mahmood, Iqbal, Shahzaib, AlShammari, Bandar, Alhaqbani, Bandar, and Razzak, Imran

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Identification of suspects based on partial and smudged fingerprints, commonly referred to as fingermarks or latent fingerprints, presents a significant challenge in the field of fingerprint recognition. Although fixed-length embeddings have shown effectiveness in recognising rolled and slap fingerprints, the methods for matching latent fingerprints have primarily centred around local minutiae-based embeddings, failing to fully exploit global representations for matching purposes. Consequently, enhancing latent fingerprints becomes critical to ensuring robust identification for forensic investigations. Current approaches often prioritise restoring ridge patterns, overlooking the fine-macroeconomic details crucial for accurate fingerprint recognition. To address this, we propose a novel approach that uses generative adversary networks (GANs) to redefine Latent Fingerprint Enhancement (LFE) through a structured approach to fingerprint generation. By directly optimising the minutiae information during the generation process, the model produces enhanced latent fingerprints that exhibit exceptional fidelity to ground-truth instances. This leads to a significant improvement in identification performance. Our framework integrates minutiae locations and orientation fields, ensuring the preservation of both local and structural fingerprint features. Extensive evaluations conducted on two publicly available datasets demonstrate our method's dominance over existing state-of-the-art techniques, highlighting its potential to significantly enhance latent fingerprint recognition accuracy in forensic applications.

Published
2024

36. AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation

Author

Naveed, Asim, Naqvi, Syed S., Khan, Tariq M., Iqbal, Shahzaib, Wani, M. Yaqoob, and Khan, Haroon Ahmed

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

In computer-aided diagnosis tools employed for skin cancer treatment and early diagnosis, skin lesion segmentation is important. However, achieving precise segmentation is challenging due to inherent variations in appearance, contrast, texture, and blurry lesion boundaries. This research presents a robust approach utilizing a dilated convolutional residual network, which incorporates an attention-based spatial feature enhancement block (ASFEB) and employs a guided decoder strategy. In each dilated convolutional residual block, dilated convolution is employed to broaden the receptive field with varying dilation rates. To improve the spatial feature information of the encoder, we employed an attention-based spatial feature enhancement block in the skip connections. The ASFEB in our proposed method combines feature maps obtained from average and maximum-pooling operations. These combined features are then weighted using the active outcome of global average pooling and convolution operations. Additionally, we have incorporated a guided decoder strategy, where each decoder block is optimized using an individual loss function to enhance the feature learning process in the proposed AD-Net. The proposed AD-Net presents a significant benefit by necessitating fewer model parameters compared to its peer methods. This reduction in parameters directly impacts the number of labeled data required for training, facilitating faster convergence during the training process. The effectiveness of the proposed AD-Net was evaluated using four public benchmark datasets. We conducted a Wilcoxon signed-rank test to verify the efficiency of the AD-Net. The outcomes suggest that our method surpasses other cutting-edge methods in performance, even without the implementation of data augmentation strategies.

Published
2024

37. Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design

Author

Iqbal, Sahel, Abdulsamad, Hany, Pérez-Vieites, Sara, Särkkä, Simo, and Corenflos, Adrien

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology
Abstract

This paper introduces the Inside-Out Nested Particle Filter (IO-NPF), a novel, fully recursive, algorithm for amortized sequential Bayesian experimental design in the non-exchangeable setting. We frame policy optimization as maximum likelihood estimation in a non-Markovian state-space model, achieving (at most) $\mathcal{O}(T^2)$ computational complexity in the number of experiments. We provide theoretical convergence guarantees and introduce a backward sampling algorithm to reduce trajectory degeneracy. IO-NPF offers a practical, extensible, and provably consistent approach to sequential Bayesian experimental design, demonstrating improved efficiency over existing methods.

Published
2024

38. Efficient Classification of Histopathology Images

Author

Nouyed, Mohammad Iqbal, Hartley, Mary-Anne, Doretto, Gianfranco, and Adjeroh, Donald A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This work addresses how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. We use images with annotated tumor regions to identify a set of tumor patches and a set of benign patches in a cancerous slide. Due to the variable nature of region of interest the tumor positive regions may refer to an extreme minority of the pixels. This creates an important problem during patch-level classification, where the majority of patches from an image labeled as 'cancerous' are actually tumor-free. This problem is different from semantic segmentation which associates a label to every pixel in an image, because after patch extraction we are only dealing with patch-level labels.Most existing approaches address the data imbalance issue by mitigating the data shortage in minority classes in order to prevent the model from being dominated by the majority classes. These methods include data re-sampling, loss re-weighting, margin modification, and data augmentation. In this work, we mitigate the patch-level class imbalance problem by taking a divide-and-conquer approach. First, we partition the data into sub-groups, and define three separate classification sub-problems based on these data partitions. Then, using an information-theoretic cluster-based sampling of deep image patch features, we sample discriminative patches from the sub-groups. Using these sampled patches, we build corresponding deep models to solve the new classification sub-problems. Finally, we integrate information learned from the respective models to make a final decision on the patches. Our result shows that the proposed approach can perform competitively using a very low percentage of the available patches in a given whole-slide image., Comment: 12 pages, 2 figures, Accepted paper for the 27th International Conference on Pattern Recognition (ICPR) 2024

Published
2024

39. TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation

Author

Iqbal, Shahzaib, Khan, Tariq M., Naqvi, Syed S., Naveed, Asim, and Meijering, Erik

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning has shown great potential for automated medical image segmentation to improve the precision and speed of disease diagnostics. However, the task presents significant difficulties due to variations in the scale, shape, texture, and contrast of the pathologies. Traditional convolutional neural network (CNN) models have certain limitations when it comes to effectively modelling multiscale context information and facilitating information interaction between skip connections across levels. To overcome these limitations, a novel deep learning architecture is introduced for medical image segmentation, taking advantage of CNNs and vision transformers. Our proposed model, named TBConvL-Net, involves a hybrid network that combines the local features of a CNN encoder-decoder architecture with long-range and temporal dependencies using biconvolutional long-short-term memory (LSTM) networks and vision transformers (ViT). This enables the model to capture contextual channel relationships in the data and account for the uncertainty of segmentation over time. Additionally, we introduce a novel composite loss function that considers both the segmentation robustness and the boundary agreement of the predicted output with the gold standard. Our proposed model shows consistent improvement over the state of the art on ten publicly available datasets of seven different medical imaging modalities.

Published
2024

40. Rigid-Body Attitude Control on $\mathsf{SO(3)}$ using Nonlinear Dynamic Inversion

Author

Khan, Hafiz Zeeshan Iqbal, Aslam, Farooq, Haydar, Muhammad Farooq, and Riaz, Jamshed

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

This paper presents a cascaded control architecture, based on nonlinear dynamic inversion (NDI), for rigid body attitude control. The proposed controller works directly with the rotation matrix parameterization, that is, with elements of the Special Orthogonal Group $\mathsf{SO(3)}$, and avoids problems related to singularities and non-uniqueness which affect other commonly used attitude representations such as Euler angles, unit quaternions, modified Rodrigues parameters, etc. The proposed NDI-based controller is capable of imposing desired linear dynamics of any order for the outer attitude loop and the inner rate loop, and gives control designers the flexibility to choose higher-order dynamic compensators in both loops. In addition, sufficient conditions are presented in the form of linear matrix inequalities (LMIs) which ensure that the outer loop controller renders the attitude loop almost globally asymptotically stable (AGAS) and the rate loop globally asymptotically stable (GAS). Furthermore, the overall cascaded control architecture is shown to be AGAS in the case of attitude error regulation. Lastly, the proposed scheme is compared with an Euler angles-based NDI scheme from literature for a tracking problem involving agile maneuvering of a multicopter in a high-fidelity nonlinear simulation., Comment: 7 pages, 6 figures, accepted in IEEE Conference on Decision and Control (CDC), 2024

Published
2024

41. SITAR: Semi-supervised Image Transformer for Action Recognition

Author

Iqbal, Owais, Chakraborty, Omprakash, Hussain, Aftab, Panda, Rameswar, and Das, Abir

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recognizing actions from a limited set of labeled videos remains a challenge as annotating visual data is not only tedious but also can be expensive due to classified nature. Moreover, handling spatio-temporal data using deep $3$D transformers for this can introduce significant computational complexity. In this paper, our objective is to address video action recognition in a semi-supervised setting by leveraging only a handful of labeled videos along with a collection of unlabeled videos in a compute efficient manner. Specifically, we rearrange multiple frames from the input videos in row-column form to construct super images. Subsequently, we capitalize on the vast pool of unlabeled samples and employ contrastive learning on the encoded super images. Our proposed approach employs two pathways to generate representations for temporally augmented super images originating from the same video. Specifically, we utilize a 2D image-transformer to generate representations and apply a contrastive loss function to minimize the similarity between representations from different videos while maximizing the representations of identical videos. Our method demonstrates superior performance compared to existing state-of-the-art approaches for semi-supervised action recognition across various benchmark datasets, all while significantly reducing computational costs., Comment: Accepted at ICPR 2024

Published
2024

42. LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation

Author

Farooq, Hamza, Zafar, Zuhair, Saadat, Ahsan, Khan, Tariq M, Iqbal, Shahzaib, and Razzak, Imran

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Accurate segmentation of skin lesions within dermoscopic images plays a crucial role in the timely identification of skin cancer for computer-aided diagnosis on mobile platforms. However, varying shapes of the lesions, lack of defined edges, and the presence of obstructions such as hair strands and marker colors make this challenge more complex. \textcolor{red}Additionally, skin lesions often exhibit subtle variations in texture and color that are difficult to differentiate from surrounding healthy skin, necessitating models that can capture both fine-grained details and broader contextual information. Currently, melanoma segmentation models are commonly based on fully connected networks and U-Nets. However, these models often struggle with capturing the complex and varied characteristics of skin lesions, such as the presence of indistinct boundaries and diverse lesion appearances, which can lead to suboptimal segmentation performance.To address these challenges, we propose a novel lightweight network specifically designed for skin lesion segmentation utilizing mobile devices, featuring a minimal number of learnable parameters (only 0.8 million). This network comprises an encoder-decoder architecture that incorporates conformer-based focal modulation attention, self-aware local and global spatial attention, and split channel-shuffle. The efficacy of our model has been evaluated on four well-established benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, ISIC 2018, and PH2. Empirical findings substantiate its state-of-the-art performance, notably reflected in a high Jaccard index.

Published
2024

43. Teachers' Experiences in 'Sekolah Penggerak' Program: A Retrospective Case Study

Author

Rian Vebrianto, Neni Hermita, Dedi Irawan, Iqbal Miftakhul Mujtahid, and Musa Thahir

Abstract

The "Sekolah Penggerak" program is one of the Indonesian Government's efforts to realize the vision of Indonesian education, namely an advanced Indonesia that is sovereign, independent, and possesses the Pancasila personality. As the leading actors in education, teachers play an important role in implementing educational programs. This study was carried out to examine the experiences of teachers involved in the "Sekolah Penggerak" program regarding the impact of implementing the program. This study is qualitative with an exploratory case study design and a retrospective approach. The participants in this study were 15 mover teachers willing to participate, selected using a purposive sampling technique. To collect data, an open-ended questionnaire, and semi-structured interviews were used. The results of the study reveal that, in general, the "Sekolah Penggerak" program has a positive impact on teachers, namely helping them change mindsets to be more innovative in utilizing student-focused learning, and on students, namely increasing their motivation and critical skills. The experiences after participating in the "Sekolah Penggerak" program also direct teachers to be involved in collaborative reflective practice of program implementation to produce teachers who are more professional so that this program runs more effectively in the future.

Published
2024

44. Application of the Collaborative Learning Model to Improve 21st-Century Civic Skills

Author

Suyato, Yayuk Hidayah, Lutfia Septiningrum, and Iqbal Arpannudin

Abstract

The study examines the application of the collaborative learning model to improve 21st-century civic skills. The 21st century presents a number of unique challenges in the world of education. Rapid changes in technology, economics, and politics have placed new demands on individual learning and development. One of the key aspects of preparing students for these challenges is the development of strong civic skills. These skills include not only an understanding of the duties and rights of citizens but also the ability to collaborate with diverse individuals and groups in order to create positive change in society. The application of the Collaborative Learning Model has emerged as a highly relevant approach to developing civic skills in the modern era. This model emphasizes cooperation, active engagement, and knowledge sharing between students, which encourages them to collaborate in solving social problems, understanding cultural differences, and designing innovative solutions. In this study, the research method he research method in this study was a pseudo-experiment with a non-equivalent control group design. The data analysis technique used is the independent sample t-test with a significance level of 5%. Results showed that the model applied significantly influenced students' 21st-century citizenship skills, and collaborative learning models effectively improved these skills. This study concludes that the Collaborative Learning Model can be used to improve students' 21st-century civic skills.

Published
2024

45. Paperless Assignments and Challenges Involved: Voice of Distance Learners

Author

Muhammad Haseeb Ul Hassan and Zafar Iqbal

Abstract

The study was planned to explore the challenges faced by distance learners during paperless assignment process. This initiative was introduced a couple of years ago at a distance learning university in Pakistan. Target population comprised on distance learners of M. Phil Education and MBA (col) sessions 2017 to 2019 programs running online through Learning Management System (LMS). Total Population Sampling (TPS) technique was followed and all distance learners of both programs were engaged. It was mixed-method research comprising both structured and open-ended information. The instruments employed were two questionnaires: one on Likert Scale and the other was open-ended. The reliability coefficient was 0.71, and the open-ended questionnaire was validated through six subject experts. The structured questionnaire was sent to all 563 students and in response 132 students filled out the online questionnaire. Quantitative data was analyzed through descriptive statistics while qualitative data was analyzed through NVivo software. The main problems highlighted were study material relevance and late delivery, understanding and processing of paperless assignments, access and skills of ICT, financial constraints, evaluation of paperless assignments, and instructors' feedback. Students suggested that textbooks should be revised and delivered in time, connectivity issues should be resolved, free internet packages should be introduced, LMS training should be conducted, in time feedback in descriptive form by instructors must be provided to the distance learners.

Published
2024

46. Technophobia as a Technology Inhibitor for Digital Learning in Education: A Systematic Literature Review

Author

Amjad Ur Rehman, Asif Mahmood, Shahid Bashir, and Mazhar Iqbal

Abstract

This study aims to determine how technophobia, or dread of technology, hinders education and offers future research recommendations. Utilizing systematic reviews, researchers scoured numerous research sources for articles on technophobia in education. The admissions criteria were established, and 18 research works that met those criteria were selected for the study. The results suggest that fear of technology makes it more difficult for people to embrace and utilize it in education, and the COVID-19 pandemic has accelerated the trend toward online education, bringing new challenges and obstacles. The study suggests additional research into the causes of technophobia, its effects on students, the efficacy of teacher training programs, and the effect of teaching methods on students' dread of technology. Lastly, the study suggests that students, instructors, and education support personnel devise effective strategies to combat technophobia.

Published
2024

47. The Inclusion of Disaster Risk Reducation in Classroom and Extra-Curricular Activities: A Case of Rural Balochistan, Pakistan

Author

Khadija Jaffar, Amjad Reba, Hazri Jamil, Seema Azeem, and Muhammad Iqbal Khan

Abstract

Purpose -- Balochistan, which makes up roughly 44 percent of Pakistan's total land area, is home to 6 percent of the nation's inhabitants. Due to long distances and low population density, service delivery is particularly challenging. The province's educational services are impacted by natural disasters like earthquakes, floods, droughts, and migration. Disaster risk reduction is a widely recognized concept that emphasizes appropriate education to lower an individual's personal, familial, and communal vulnerability. The role of the school is crucial in Disaster Risk Reducation (DRR) education. As a result, the study's goal was to explore the approaches adopted for the inclusion of DDR through teaching in classroom and school activities. Methodology -- A focus group discussion with three groups of Pakistan studies and Geography teachers was conducted that consisted of 10 male and 14 female members. Findings -- Findings demonstrate that the current textbook continues to teach students less about disaster risk reduction; teachers include knowledge from their personal experience in planning lessons about DRR. School assemblies, child clubs and activities designed by school management, and social organizations play a prominent role in DRR education. Further, the role of teachers and school management was identified in psycho-social support during disasters and pandemics. Significance -- The study concludes that in addition to extracurricular activities and the teacher's role, prior disaster experience, school, and social organization played a significant role in DRR education in rural Balochistan. The study results will assist curriculum developers, policymakers, and education leadership in developing more effective school disaster management plans. The results will also clarify how schools and teachers can close the knowledge gap in disaster preparedness education. Organizations working on disaster risk education and education in emergencies will also benefit from additional research to respond to the need readily and effectively.

Published
2024

48. The Effect of Learning Management System on Reading Comprehension Across 3 Types of Readers

Author

Shynta Amalia, Muhammad Iqbal Ramdhani, Syafryadin, Eka Apriani, and Saad Boulahnane

Abstract

Through the application of the experimental research method and the factorial design, the purpose of this study was to investigate the effects of the authoring tools SoftChalk medium and Hot Potatoes medium in the learning management system (LMS), as well as the types of readers (Avid, Passive, and Reluctant), on the reading comprehension achievement (ReComA) of second year university students (sophomores). In order to identify the different sorts of readers that took part in the study, a quick survey was given to all of the participants. Both the pre-test and post-test were administered to sixty second-year students in order to evaluate the students' ReComA. The findings suggested that the use of the Hot Potatoes medium was more successful in enhancing the students' ReComA and its features. On the other hand, the majority of components of reading comprehension contributed to ReComA (total) in both the SoftChalk and Hot Potatoes groups. In addition, despite the fact that there was no significant interaction on students' ReComA, the mean difference between the post-test and the whole sample indicated that the achievement of the Avid reader and Reluctant reader students in both the SoftChalk and Hot Potatoes groups were higher than the achievement of the Passive reader students.

Published
2024

49. Cooperative Model, Digital Game, and Augmented Reality-Based Learning to Enhance Students' Critical Thinking Skills and Learning Motivation

Author

Iqbal A. Rizki, Nadi Suprapto, Hanandita V. Saphira, Yusril Alfarizy, Riski Ramadani, Aulia Dwi Saputri, and Dewi Suryani

Abstract

Low levels of critical thinking skills and learning motivation, particularly in physics learning, pose significant challenges that must be promptly addressed to ensure students' future success. To tackle this issue, the present study endeavors to develop a Cooperative Model, Digital Game, and Augmented Reality (CAP)-based learning, which is both valid and practical, to enhance these crucial aspects effectively. This study used Educational Design Research through a non-equivalent control group design in the data collection process. Using purposive sampling method, 54 high school students were divided into two classes were involved. The data collection period was from July to August 2022. The research instruments used include syllabus, lesson plan, Adventuring Physics media, critical thinking test instrument, motivation questionnaire, student response questionnaire, validation questionnaire, and observation sheet. Statistical tests used include descriptive, paired, and independent t-tests. The results reveal that the learning products and instruments exhibit strong validity and reliability, alongside a high degree of practicality in their implementation. The experimental group demonstrated a notable improvement both in critical thinking skills and learning motivation. Additionally, students' feedback regarding this learning approach has been overwhelmingly positive. Consequently, this research highlights the importance of fostering more engaging and enjoyable innovations through digital learning in physics education to achieve optimal learning outcomes and enhance 21st-century skills.

Published
2024

50. Challenges of Participation in Adult Basic Literacy: Practical Implications for Practitioners

Author

Sidra Noreen and Zafar Iqbal

Abstract

In Pakistan, adult literacy programs are offered to enable learners to function effectively to attain individual, family, and social sustainability, but these programs are continuously reporting low participation. This study aimed to explore the reasons behind low participation, employing a descriptive phenomenological design to examine the challenges experienced by adult learners, literacy teachers, and administrators. Forty-five participants (30 learners, 10 teachers, and 5 administrators) were selected by using a purposive sampling technique. Data were gathered through focus group discussions and semi-structured interviews, and a thematic analysis was conducted. Numerous challenges were found, including less attractive, non-relevant literacy content, inappropriate presentation methods, lack of proper mechanisms for teachers' training, and learners' evaluation. Based on the evidence, practical implications were proposed, with a particular emphasis on adult learners and literacy teachers.

Published
2024
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