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19,396 results on '"Mir P"'

1. Investigating the relation between environment and internal structure of massive elliptical galaxies using strong lensing

Author: Adnan, S M Rafee, Hasan, Muhammad Jobair, Imtiaz, Ahmad Al, Robin, Sulyman H., Shwadhin, Fahim R., Shajib, Anowar J., Nahid, Mamun Hossain, Tanver, Mehedi Hasan, Akter, Tanjela, Jahan, Nusrath, Jafar, Zareef, Rashid, Mamunur, Biswas, Anik, Chowdhury, Akbar Ahmed, Feardous, Jannatul, Rahaman, Ajmi, Ridwan, Masuk, Sharma, Rahul D., Chowdhury, Zannat, and Hossain, Mir Sazzat
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Strong lensing directly probes the internal structure of the lensing galaxies. In this paper, we investigate the relation between the internal structure of massive elliptical galaxies and their environment using a sample of 15 strong lensing systems. We performed lens modeling for them using Lenstronomy and constrained the mass and light distributions of the deflector galaxies. We adopt the local galaxy density as a metric for the environment and test our results against several alternative definitions of it. We robustly find that the centroid offset between the mass and light is not correlated with the local galaxy density. This result supports using centroid offsets as a probe of dark matter theories since the environment's impact on it can be treated as negligible. Although we find a strong correlation between the position angle offset and the standard definition of the local galaxy density, consistent with previous studies, the correlation becomes weaker for alternative definitions of the local galaxy density. This result weakens the support for interpreting the position angle misalignment as having originated from interaction with the environment. Furthermore, we find the 'residual shear' magnitude in the lens model to be uncorrelated with the local galaxy density, supporting the interpretation of the residual shear originating, in part, from the inadequacy in modeling the angular structure of the lensing galaxy and not solely from the structures present in the environment or along the line of sight., Comment: 15 pages, 9 figures, 3 tables. Submitted to A&A
Published: 2024

2. Holographic Timelike Entanglement Entropy in Non-relativistic Theories

Author: Afrasiar, Mir, Basak, Jaydeep Kumar, and Giataganas, Dimitrios
Subjects: High Energy Physics - Theory
Abstract: Timelike entanglement entropy is a complex measure of information that is holographically realized by an appropriate combination of spacelike and timelike extremal surfaces. This measure is highly sensitive to Lorentz invariance breaking. In this work, we study the timelike entanglement entropy in non-relativistic theories, focusing on theories with hyperscaling violation and Lifshitz-like spatial anisotropy. The properties of the extremal surfaces, as well as the timelike entanglement entropy itself, depend heavily on the symmetry-breaking parameters of the theory. Consequently, we show that timelike entanglement can encode, to a large extent, the stability and naturalness of the theory. Furthermore, we find that timelike entanglement entropy identifies Fermi surfaces either through the logarithmic behavior of its real part or, alternatively, via its constant imaginary part, with this constant value depending on the theory's Lifshitz exponent. This provides a novel interpretation for the imaginary component of this pseudoentropy. Additionally, we examine temporal entanglement entropy, an extension of timelike entanglement entropy to Euclidean space, and provide a comprehensive discussion of its properties in these theories., Comment: 41 pages
Published: 2024

3. On the distance spectral radius bounds and improved bounds on the spectral radius of power graphs of some finite groups

Author: Mondal, Priti Prasanna, Mir, Basit Auyoob, and Atik, Fouzul
Subjects: Mathematics - Spectral Theory
Abstract: We consider the group G and construct its power graph, whose vertex set consists of the elements of G. Two distinct vertices (elements) are adjacent in the graph if and only if one element can be expressed as an integral power of the other. In the paper (Indagationes Mathematicae 29(2) (2018), pp 730 t0 737), Chattopadhyay et al. gave spectral radius bounds of the power graph of certain finite groups. In this article, we improved the bounds of the spectral radius of the power graphs of the above results. Furthermore, we provide bounds for the distance spectral radius of the power graph of the cyclic group Cn, the dihedral group D2n, and the dicyclic group Q4n. For some cases, we find the bounds are exact if and only if they pertain to a particular family of graphs.
Published: 2024

4. Ensuring Safety in Target Pursuit Control: A CBF-Safe Reinforcement Learning Approach

Author: Deng, Yaosheng, Gao, Junjie, and Feroskhan, Mir
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: This paper addresses the pursuit control problem for multi-agent systems, aiming to ensure collision-free tracking under input saturation and external disturbances. We propose a novel Control Barrier Function (CBF)-Safe Reinforcement Learning (CSRL) algorithm, which integrates model-free reinforcement learning with a safety filter to guarantee system safety. The framework introduces an input-constrained CBF that dynamically adjusts control bounds, enabling robust target tracking even during evasive maneuvers. A safety filter is designed to transform unsafe RL actions into safe control signals by solving a Quadratic Program (QP), ensuring the safety for sensing, collision avoidance, and input constraints of pursuers. Theoretical analysis proves the feasibility of the CBF-QP using the Karush-Kuhn-Tucker (KKT) conditions. Simulation results validate the effectiveness of the CSRL algorithm, demonstrating its ability to handle complex pursuit scenarios while maintaining safety and improving control performance., Comment: 10 pages
Published: 2024

5. Relative Hadron Yields in HRG With Medium Modification

Author: Rather, Nasir Ahmad, Mir, Sameer Ahmad, Din, Iqbal Mohi Ud, and Uddin, Saeed
Subjects: High Energy Physics - Phenomenology, Nuclear Theory
Abstract: In the framework of a constituent quark mass model, the modified baryon masses are incorporated into the hadron resonance gas (HRG) based analysis of the like mass particle ratios in ultra relativistic nucleus-nucleus collisions (URNNC) over a wide range of collision energy. In addition we have incorporated an essential feature of the hadronic interaction at short distance, i.e. the hard-core repulsion by using the standard excluded volume type approach. We have extracted the chemical freeze-out conditions. The resulting freeze-out line in our case is compared with those obtained earlier using different model approaches. The correlation between $k^{-}/k^{+}$ and $\bar p/p$ ratios is also studied., Comment: 10 pages, 13 figures, 2 tables
Published: 2024

6. Revitalizing Electoral Trust: Enhancing Transparency and Efficiency through Automated Voter Counting with Machine Learning

Author: Faris, Mir, Karim, Syeda Aynul, and Islam, Md. Juniadul
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract: In order to address issues with manual vote counting during election procedures, this study intends to examine the viability of using advanced image processing techniques for automated voter counting. The study aims to shed light on how automated systems that utilize cutting-edge technologies like OpenCV, CVZone, and the MOG2 algorithm could greatly increase the effectiveness and openness of electoral operations. The empirical findings demonstrate how automated voter counting can enhance voting processes and rebuild public confidence in election outcomes, particularly in places where trust is low. The study also emphasizes how rigorous metrics, such as the F1 score, should be used to systematically compare the accuracy of automated systems against manual counting methods. This methodology enables a detailed comprehension of the differences in performance between automated and human counting techniques by providing a nuanced assessment. The incorporation of said measures serves to reinforce an extensive assessment structure, guaranteeing the legitimacy and dependability of automated voting systems inside the electoral sphere., Comment: 13 Pages, 4 Figures
Published: 2024

7. Can Features for Phishing URL Detection Be Trusted Across Diverse Datasets? A Case Study with Explainable AI

Author: Mia, Maraz, Derakhshan, Darius, and Pritom, Mir Mehedi A.
Subjects: Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract: Phishing has been a prevalent cyber threat that manipulates users into revealing sensitive private information through deceptive tactics, designed to masquerade as trustworthy entities. Over the years, proactively detection of phishing URLs (or websites) has been established as an widely-accepted defense approach. In literature, we often find supervised Machine Learning (ML) models with highly competitive performance for detecting phishing websites based on the extracted features from both phishing and benign (i.e., legitimate) websites. However, it is still unclear if these features or indicators are dependent on a particular dataset or they are generalized for overall phishing detection. In this paper, we delve deeper into this issue by analyzing two publicly available phishing URL datasets, where each dataset has its own set of unique and overlapping features related to URL string and website contents. We want to investigate if overlapping features are similar in nature across datasets and how does the model perform when trained on one dataset and tested on the other. We conduct practical experiments and leverage explainable AI (XAI) methods such as SHAP plots to provide insights into different features' contributions in case of phishing detection to answer our primary question, "Can features for phishing URL detection be trusted across diverse dataset?". Our case study experiment results show that features for phishing URL detection can often be dataset-dependent and thus may not be trusted across different datasets even though they share same set of feature behaviors., Comment: 9 pages, 9 figures, 11th International Conference on Networking, Systems, and Security (NSysS 2024), 2024, Khulna, Bangladesh
Published: 2024
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8. New approach to search for long transient gravitational waves from inspiraling compact binary systems

Author: Andrés-Carcasona, M., Piccinni, O. J., Martínez, M., and Mir, Ll. M.
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The search for gravitational waves generated by the inspiral phase of binaries of light compact objects holds significant promise in testing the existence of primordial black holes and/or other exotic objects. In this paper, we present a new method to detect such signals exploiting some techniques typically applied in searches for continuous quasi-monochromatic gravitational waves. We describe the signal model employed and present a new strategy to optimally construct the search grid over the parameter space investigated, significantly reducing the search computing cost. Additionally, we estimate the pipeline sensitivity corroborating the results with software injections in real data from the LIGO third observing run. The results show that the method is well suited to detect long-transient signals and standard continuous gravitational waves. According to the criteria used in the grid construction step, the method can be implemented to cover a wide parameter space with slightly reduced sensitivity and lower computational cost or to focus on a narrower parameter space with increased sensitivity at a higher computational expense. The method shows an astrophysical reach up to the Galactic Center (8kpc) for some regions of the parameter space and given search configurations.
Published: 2024

9. 5d-4d Correspondence in Twisted M-theory on a Conifold

Author: Ashwinkumar, Meer, Faizal, Mir, Shabir, Arshid, Smith, Douglas J., and Zhou, Yehao
Subjects: High Energy Physics - Theory, Mathematical Physics
Abstract: We study twisted M-theory in a general conifold background, and describe it in terms of a 5d non-commutative Chern-Simons-matter theory. In an equivalent description as twisted type IIA string theory, the matter degrees of freedom arise from topological strings stretched between stacks of D6-branes. In order to study the 5d Chern-Simons-matter theory with a boundary, we first construct and investigate the properties of a 4d non-commutative gauged chiral WZW model. We prove the gauge invariant coupling of this 4d theory to the bulk 5d Chern-Simons theory defined on $\mathbb{R}_+ \times \mathbb{C}^2 $, and further generalize our results to the 5d Chern-Simons-matter theory. We also investigate the toroidal current algebra of the 4d chiral WZW model that arises from radial quantization along one of the complex planes. Finally, we show that a gauged non-commutative chiral 4d WZW model arises from the partition function for quantum 5d non-commutative Chern-Simons theory with boundaries in the BV-BFV formalism, and further generalize this 5d-4d correspondence to the 5d non-commutative Chern-Simons-matter theory for the case of adjoint matter., Comment: 38 pages, 2 figures
Published: 2024

10. Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection

Author: Mia, Maraz, Pritom, Mir Mehedi A., Islam, Tariqul, and Hasan, Kamrul
Subjects: Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract: Intrusion detection has been a commonly adopted detective security measures to safeguard systems and networks from various threats. A robust intrusion detection system (IDS) can essentially mitigate threats by providing alerts. In networks based IDS, typically we deal with cyber threats like distributed denial of service (DDoS), spoofing, reconnaissance, brute-force, botnets, and so on. In order to detect these threats various machine learning (ML) and deep learning (DL) models have been proposed. However, one of the key challenges with these predictive approaches is the presence of false positive (FP) and false negative (FN) instances. This FPs and FNs within any black-box intrusion detection system (IDS) make the decision-making task of an analyst further complicated. In this paper, we propose an explainable artificial intelligence (XAI) based visual analysis approach using overlapping SHAP plots that presents the feature explanation to identify potential false positive and false negatives in IDS. Our approach can further provide guidance to security analysts for effective decision-making. We present case study with multiple publicly available network traffic datasets to showcase the efficacy of our approach for identifying false positive and false negative instances. Our use-case scenarios provide clear guidance for analysts on how to use the visual analysis approach for reliable course-of-actions against such threats., Comment: 10 pages, 14 figures, accepted in the MLC Workshop of the International Conference on Data Mining Conference (ICDM 2024)
Published: 2024

11. 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

12. Adversarial Domain Adaptation for Metal Cutting Sound Detection: Leveraging Abundant Lab Data for Scarce Industry Data

Author: Mostafiz, Mir Imtiaz, Kim, Eunseob, Li, Adrian Shuai, Bertino, Elisa, Jun, Martin Byung-Guk, and Shakouri, Ali
Subjects: Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspired by experienced machinists, can be employed as a cost-effective and non-intrusive monitoring method in a complex manufacturing environment. However, labeling industry data for training is costly and time-consuming. Moreover, industry data is often scarce. In this study, we propose a novel adversarial domain adaptation (DA) approach to leverage abundant lab data to learn from scarce industry data, both labeled, for training a cutting-sound detection model. Rather than adapting the features from separate domains directly, we project them first into two separate latent spaces that jointly work as the feature space for learning domain-independent representations. We also analyze two different mechanisms for adversarial learning where the discriminator works as an adversary and a critic in separate settings, enabling our model to learn expressive domain-invariant and domain-ingrained features, respectively. We collected cutting sound data from multiple sensors in different locations, prepared datasets from lab and industry domain, and evaluated our learning models on them. Experiments showed that our models outperformed the multi-layer perceptron based vanilla domain adaptation models in labeling tasks on the curated datasets, achieving near 92%, 82% and 85% accuracy respectively for three different sensors installed in industry settings., Comment: 8 pages, 3 figures, 3 tables, First two named Authors have equal contribution (Co-first author)
Published: 2024

13. 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. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. 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. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. 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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

14. FSCsec: Collaboration in Financial Sector Cybersecurity -- Exploring the Impact of Resource Sharing on IT Security

Author: Sayeed, Sayed Abu, Rahman, Mir Mehedi, Alam, Samiul, and Kshetri, Naresh
Subjects: Computer Science - Cryptography and Security
Abstract: The financial sector's dependence on digital infrastructure increases its vulnerability to cybersecurity threats, requiring strong IT security protocols with other entities. This collaboration, however, is often identified as the most vulnerable link in the chain of cybersecurity. Adopting both symbolic and substantive measures lessens the impact of IT security spending on decreasing the frequency of data security breaches in the long run. The Protection Motivation Theory clarifies actions triggered by data sharing with other organizations, and the Institutional theory aids in comprehending the intricate relationship between transparency and organizational conduct. We investigate how things like regulatory pressure, teamwork among institutions, and people's motivations to protect themselves influence cybersecurity. By using simple theories to understand these factors, this research aims to provide insights that can help financial institutions make better decisions to protect. We have also included the discussion, conclusion, and future directions in regard to collaboration in financial sector cybersecurity for exploring impact of resource sharing., Comment: 8 pages, 2 figures
Published: 2024

15. LFOSum: Summarizing Long-form Opinions with Large Language Models

Author: Nayeem, Mir Tafseer and Rafiei, Davood
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Emerging Technologies, Computer Science - Human-Computer Interaction, Computer Science - Information Retrieval
Abstract: Online reviews play a pivotal role in influencing consumer decisions across various domains, from purchasing products to selecting hotels or restaurants. However, the sheer volume of reviews -- often containing repetitive or irrelevant content -- leads to information overload, making it challenging for users to extract meaningful insights. Traditional opinion summarization models face challenges in handling long inputs and large volumes of reviews, while newer Large Language Model (LLM) approaches often fail to generate accurate and faithful summaries. To address those challenges, this paper introduces (1) a new dataset of long-form user reviews, each entity comprising over a thousand reviews, (2) two training-free LLM-based summarization approaches that scale to long inputs, and (3) automatic evaluation metrics. Our dataset of user reviews is paired with in-depth and unbiased critical summaries by domain experts, serving as a reference for evaluation. Additionally, our novel reference-free evaluation metrics provide a more granular, context-sensitive assessment of summary faithfulness. We benchmark several open-source and closed-source LLMs using our methods. Our evaluation reveals that LLMs still face challenges in balancing sentiment and format adherence in long-form summaries, though open-source models can narrow the gap when relevant information is retrieved in a focused manner.
Published: 2024

16. Implications of Tarski's Undefinability Theorem on the Theory of Everything

Author: Faizal, Mir, Shabir, Arshid, and Khan, Aatif Kaisar
Subjects: Physics - History and Philosophy of Physics, High Energy Physics - Theory
Abstract: The Theory of Everything ($S_{\text{ToE}}$) seeks to unify all fundamental forces of nature, including quantum gravity, into a single theoretical framework. This theory would be defined internally using a set of axioms, and this paper proposes a set of axioms for any such theory. Furthermore, for such a theory, all scientific truth would be defined internally as consequences derivable from the rules of such a theory. This paper then examines the implications of Tarski's undefinability theorem on scientific truths derived from such axioms. We demonstrate that Tarski's theorem imposes limitations on any such formal system $S_{\text{ToE}}$. However, we also argue that the Lucas-Penrose argument suggests that non-algorithmic understanding can transcend these formal limitations., Comment: Accepted for publication in EPL
Published: 2024
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17. 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. 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K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Ruhama, N., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Saha, S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. 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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

18. Are Grid Cells Hexagonal for Performance or by Convenience?

Author: Mir, Taahaa, Yao, Peipei, Duranceau, Kateri, and Prémont-Schwarz, Isabeau
Subjects: Quantitative Biology - Neurons and Cognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract: This paper investigates whether the hexagonal structure of grid cells provides any performance benefits or if it merely represents a biologically convenient configuration. Utilizing the Vector-HaSH content addressable memory model as a model of the grid cell -- place cell network of the mammalian brain, we compare the performance of square and hexagonal grid cells in tasks of storing and retrieving spatial memories. Our experiments across different path types, path lengths and grid configurations, reveal that hexagonal grid cells perform similarly to square grid cells with respect to spatial representation and memory recall. Our results show comparable accuracy and robustness across different datasets and noise levels on images to recall. These findings suggest that the brain's use of hexagonal grids may be more a matter of biological convenience and ease of implementation rather than because they provide superior performance over square grid cells (which are easier to implement in silico)., Comment: 5 pages, accepted at Montreal AI and Neuroscience Conference 2024
Published: 2024

19. KidLM: Advancing Language Models for Children -- Early Insights and Future Directions

Author: Nayeem, Mir Tafseer and Rafiei, Davood
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Human-Computer Interaction
Abstract: Recent studies highlight the potential of large language models in creating educational tools for children, yet significant challenges remain in maintaining key child-specific properties such as linguistic nuances, cognitive needs, and safety standards. In this paper, we explore foundational steps toward the development of child-specific language models, emphasizing the necessity of high-quality pre-training data. We introduce a novel user-centric data collection pipeline that involves gathering and validating a corpus specifically written for and sometimes by children. Additionally, we propose a new training objective, Stratified Masking, which dynamically adjusts masking probabilities based on our domain-specific child language data, enabling models to prioritize vocabulary and concepts more suitable for children. Experimental evaluations demonstrate that our model excels in understanding lower grade-level text, maintains safety by avoiding stereotypes, and captures children's unique preferences. Furthermore, we provide actionable insights for future research and development in child-specific language modeling., Comment: Accepted to EMNLP 2024 (long, main)
Published: 2024

20. AssessITS: Integrating procedural guidelines and practical evaluation metrics for organizational IT and Cybersecurity risk assessment

Author: Rahman, Mir Mehedi, Kshetri, Naresh, Sayeed, Sayed Abu, and Rana, Md Masud
Subjects: Computer Science - Cryptography and Security
Abstract: In today's digitally driven landscape, robust Information Technology (IT) risk assessment practices are essential for safeguarding systems, digital communication, and data. This paper introduces 'AssessITS', an actionable method designed to provide organizations with comprehensive guidelines for conducting IT and cybersecurity risk assessments. Drawing extensively from NIST 800-30 Rev 1, COBIT 5, and ISO 31000, 'AssessITS' bridges the gap between high-level theoretical standards and practical implementation challenges. The paper outlines a step-by-step methodology that organizations can simply adopt to systematically identify, analyze, and mitigate IT risks. By simplifying complex principles into actionable procedures, this framework equips practitioners with the tools needed to perform risk assessments independently, without too much reliance on external vendors. The guidelines are developed to be straightforward, integrating practical evaluation metrics that allow for the precise quantification of asset values, threat levels, vulnerabilities, and impacts on confidentiality, integrity, and availability. This approach ensures that the risk assessment process is not only comprehensive but also accessible, enabling decision-makers to implement effective risk mitigation strategies customized to their unique operational contexts. 'AssessITS' aims to enable organizations to enhance their IT security strength through practical, actionable guidance based on internationally recognized standards., Comment: 25 pages, 8 figures, 7 tables
Published: 2024

21. An Empirical Study of Large Language Models for Type and Call Graph Analysis

Author: Venkatesh, Ashwin Prasad Shivarpatna, Sunil, Rose, Sabu, Samkutty, Mir, Amir M., Reis, Sofia, and Bodden, Eric
Subjects: Computer Science - Software Engineering
Abstract: Large Language Models (LLMs) are increasingly being explored for their potential in software engineering, particularly in static analysis tasks. In this study, we investigate the potential of current LLMs to enhance call-graph analysis and type inference for Python and JavaScript programs. We empirically evaluated 24 LLMs, including OpenAI's GPT series and open-source models like LLaMA and Mistral, using existing and newly developed benchmarks. Specifically, we enhanced TypeEvalPy, a micro-benchmarking framework for type inference in Python, with auto-generation capabilities, expanding its scope from 860 to 77,268 type annotations for Python. Additionally, we introduced SWARM-CG and SWARM-JS, comprehensive benchmarking suites for evaluating call-graph construction tools across multiple programming languages. Our findings reveal a contrasting performance of LLMs in static analysis tasks. For call-graph generation in Python, traditional static analysis tools like PyCG significantly outperform LLMs. In JavaScript, the static tool TAJS underperforms due to its inability to handle modern language features, while LLMs, despite showing potential with models like mistral-large-it-2407-123b and GPT-4o, struggle with completeness and soundness in both languages for call-graph analysis. Conversely, LLMs demonstrate a clear advantage in type inference for Python, surpassing traditional tools like HeaderGen and hybrid approaches such as HiTyper. These results suggest that while LLMs hold promise in type inference, their limitations in call-graph analysis highlight the need for further research. Our study provides a foundation for integrating LLMs into static analysis workflows, offering insights into their strengths and current limitations., Comment: Pre-print: Submitted to EMSE journal for review
Published: 2024

22. An Evaluation of Virtual Learning Environments in Three Open Universities in Asia

Author: Kamran Mir, Roberto B. Figueroa, and Aminudin Zuhairi
Abstract: Open universities have provided quality higher education through open and distance education to serve learners who could not attend traditional in-person universities. While open universities vary in terms of providing their services to learners at a distance, many have shifted to e-learning using cost-effective platforms. A common platform that is essential for online teaching and learning is the learning management system (LMS). While there are common processes observed among open universities in the way they use their LMS, there are also subtle differences caused by each institution's unique and special context. Their contextual challenges often drive them to develop technologies or techniques that may easily be adopted by others. Thus, benchmarking activities are often recommended for mutual improvement. In this regard, a comparative study of open universities in Indonesia, the Philippines, and Pakistan was conducted for decision-makers to understand how they could improve in providing quality education to their learners through Moodle, which is their primary LMS. The Delone and Mclean Model was used to systematically compare system, information, and service quality in e-learning delivered through their LMS. The authors hope that the results and recommendations from this study may help other educational institutions overcome similar challenges in their learning platforms.
Published: 2024

23. Considerations for Conducting Systematic Reviews: A Follow-Up Study to Evaluate the Performance of Various Automated Methods for Reference De-Duplication

Author: Sandra McKeown and Zuhaib M. Mir
Abstract: Searching multiple resources to locate eligible studies for research syntheses can result in hundreds to thousands of duplicate references that should be removed before the screening process for efficiency. Research investigating the performance of automated methods for deduplicating references via reference managers and systematic review software programs can become quickly outdated as new versions and programs become available. This follow-up study examined the performance of default de-duplication algorithms in EndNote 20, EndNote online classic, ProQuest RefWorks, Deduklick, and Systematic Review Accelerator's new Deduplicator tool. On most accounts, systematic review software programs outperformed reference managers when deduplicating references. While cost and the need for institutional access may restrict researchers from being able to utilize some automated methods for deduplicating references, Systematic Review Accelerator's Deduplicator tool is free to use and demonstrated the highest accuracy and sensitivity, while also offering user-mediation of detected duplicates to improve specificity. Researchers conducting syntheses should take automated de-duplication performance, and methods for improving and optimizing their use, into consideration to help prevent the unintentional removal of eligible studies and potential introduction of bias to syntheses. Researchers should also be transparent about their de-duplication process to help readers critically appraise their synthesis methods, and to comply with the PRISMA-S extension for reporting literature searches in systematic reviews.
Published: 2024
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24. Grounded Predictions of Teamwork as a One-Shot Game: A Multiagent Multi-Armed Bandits Approach

Author: Gómez, Alejandra López de Aberasturi, Sierra, Carles, and Sabater-Mir, Jordi
Subjects: Computer Science - Multiagent Systems, Computer Science - Computer Science and Game Theory
Abstract: Humans possess innate collaborative capacities. However, effective teamwork often remains challenging. This study delves into the feasibility of collaboration within teams of rational, self-interested agents who engage in teamwork without the obligation to contribute. Drawing from psychological and game theoretical frameworks, we formalise teamwork as a one-shot aggregative game, integrating insights from Steiner's theory of group productivity. We characterise this novel game's Nash equilibria and propose a multiagent multi-armed bandit system that learns to converge to approximations of such equilibria. Our research contributes value to the areas of game theory and multiagent systems, paving the way for a better understanding of voluntary collaborative dynamics. We examine how team heterogeneity, task typology, and assessment difficulty influence agents' strategies and resulting teamwork outcomes. Finally, we empirically study the behaviour of work teams under incentive systems that defy analytical treatment. Our agents demonstrate human-like behaviour patterns, corroborating findings from social psychology research.
Published: 2024

25. Learning Resilient Formation Control of Drones with Graph Attention Network

Author: Xiao, Jiaping, Fang, Xu, Jia, Qianlei, and Feroskhan, Mir
Subjects: Computer Science - Robotics
Abstract: The rapid advancement of drone technology has significantly impacted various sectors, including search and rescue, environmental surveillance, and industrial inspection. Multidrone systems offer notable advantages such as enhanced efficiency, scalability, and redundancy over single-drone operations. Despite these benefits, ensuring resilient formation control in dynamic and adversarial environments, such as under communication loss or cyberattacks, remains a significant challenge. Classical approaches to resilient formation control, while effective in certain scenarios, often struggle with complex modeling and the curse of dimensionality, particularly as the number of agents increases. This paper proposes a novel, learning-based formation control for enhancing the adaptability and resilience of multidrone formations using graph attention networks (GATs). By leveraging GAT's dynamic capabilities to extract internode relationships based on the attention mechanism, this GAT-based formation controller significantly improves the robustness of drone formations against various threats, such as Denial of Service (DoS) attacks. Our approach not only improves formation performance in normal conditions but also ensures the resilience of multidrone systems in variable and adversarial environments. Extensive simulation results demonstrate the superior performance of our method over baseline formation controllers. Furthermore, the physical experiments validate the effectiveness of the trained control policy in real-world flights., Comment: This work has been submitted to the IEEE for possible publication
Published: 2024

26. Democratizing MLLMs in Healthcare: TinyLLaVA-Med for Efficient Healthcare Diagnostics in Resource-Constrained Settings

Author: Mir, Aya El, Luoga, Lukelo Thadei, Chen, Boyuan, Hanif, Muhammad Abdullah, and Shafique, Muhammad
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Deploying Multi-Modal Large Language Models (MLLMs) in healthcare is hindered by their high computational demands and significant memory requirements, which are particularly challenging for resource-constrained devices like the Nvidia Jetson Xavier. This problem is particularly evident in remote medical settings where advanced diagnostics are needed but resources are limited. In this paper, we introduce an optimization method for the general-purpose MLLM, TinyLLaVA, which we have adapted and renamed TinyLLaVA-Med. This adaptation involves instruction-tuning and fine-tuning TinyLLaVA on a medical dataset by drawing inspiration from the LLaVA-Med training pipeline. Our approach successfully minimizes computational complexity and power consumption, with TinyLLaVA-Med operating at 18.9W and using 11.9GB of memory, while achieving accuracies of 64.54% on VQA-RAD and 70.70% on SLAKE for closed-ended questions. Therefore, TinyLLaVA-Med achieves deployment viability in hardware-constrained environments with low computational resources, maintaining essential functionalities and delivering accuracies close to state-of-the-art models.
Published: 2024

27. Safe Adaptive Control for Uncertain Systems with Complex Input Constraints

Author: Deng, Yaosheng, Bai, Yang, Wang, Yujie, Ogura, Masaki, and Feroskhan, Mir
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: In this paper, we propose a novel adaptive Control Barrier Function (CBF) based controller for nonlinear systems with complex, time-varying input constraints. Conventional CBF approaches often struggle with feasibility issues and stringent assumptions when addressing input constraints. Unlike these methods, our approach converts the input-constraint problem into an output-constraint CBF design. This transformation simplifies the Quadratic Programming (QP) formulation and enhances compatibility with the CBF framework. We design an adaptive CBF-based controller to manage the mismatched uncertainties introduced by this transformation. Our method systematically addresses the challenges of complex, time-varying, and state-dependent input constraints. The efficacy of the proposed approach is validated using numerical examples., Comment: 8 pages, 2 figures
Published: 2024

28. Collision Energy Dependence of Particle Ratios and Freeze-out Parameters in Ultra Relativistic Nucleus Nucleus Collisions

Author: Din, Iqbal Mohi Ud, Mir, Sameer Ahmad, Rather, Nasir Ahmad, Uddin, Saeed, and Parra, Rameez Ahmad
Subjects: High Energy Physics - Phenomenology, Nuclear Theory
Abstract: This work investigates how the theoretically calculated particle ratios and freeze-out parameters for a multi-component hot and dense non-ideal hadron resonance gas system, formed during the pre freeze-out stage in ultra-relativistic nucleus-nucleus collisions, are affected by van der Waals type interactions, incorporating both repulsive and attractive interactions among hadrons. The baryons (antibaryons) are treated as hard incompressible objects. Using this theoretical approach, we analyze experimental particle ratios involving like-mass as well as unlike-mass hadrons. We also try to understand the existence of horn-like structures seen in the ratios of strange particles to pion as a function of the collision energy. Using information about the dependence of experimental particle ratios on $\sqrt{s_{NN}}$ over a wide range of collision energies, we have extracted several model freeze-out parameters and examined their dependence on $\sqrt{s_{NN}}$ The correlation between the $\bar{p}/p$ and $K^-/K^+$ ratios is also examined., Comment: 15 pages, 19 Figures
Published: 2024

29. Exploring Non-Markovianity in Ergodic Channels: Introducing Ergotropy as a Measure

Author: Basu, Ritam, Chakraborty, Anish, Badhani, Himanshu, Alimuddin, Mir, and Bhattacharya, Samyadeb
Subjects: Quantum Physics
Abstract: In this work, we characterise a general set of quantum operations which possess a singular fixed point. These channels are dubbed as quantum ergodic channels. We have constructed Lindblad-type master equations for such channels for arbitrary dimensions. We also characterise the aspect of non-Markovianity for such a general class of channels. Interestingly, if the fixed point is considered a passive state, it turns out to be an ergotropy-decreasing channel at a given instant. The formalism of ergotropy is widely used in quantum batteries, where it quantifies how effectively a quantum battery can be used as an energy source under unitary evolution only. Our investigation explores how the usefulness of a quantum battery, or the ergotropy of a system, varies under ergodic dynamics. We observed that, while ergotropy always decreases in a Markovian process, it fluctuates under non-Markovian ergodic dynamics. This ergotropy backflow in the non-Markovian process makes it a potential candidate for measuring non-Markovianity. Consequently, this study enhances our understanding of quantum battery evolution in open dynamics and paves the way for more efficient quantum technology., Comment: 11 pages , 3 figures
Published: 2024

30. Vision-Language Model Based Handwriting Verification

Author: Chauhan, Mihir, Satbhai, Abhishek, Hashemi, Mohammad Abuzar, Ali, Mir Basheer, Ramamurthy, Bina, Gao, Mingchen, Lyu, Siwei, and Srihari, Sargur
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Handwriting Verification is a critical in document forensics. Deep learning based approaches often face skepticism from forensic document examiners due to their lack of explainability and reliance on extensive training data and handcrafted features. This paper explores using Vision Language Models (VLMs), such as OpenAI's GPT-4o and Google's PaliGemma, to address these challenges. By leveraging their Visual Question Answering capabilities and 0-shot Chain-of-Thought (CoT) reasoning, our goal is to provide clear, human-understandable explanations for model decisions. Our experiments on the CEDAR handwriting dataset demonstrate that VLMs offer enhanced interpretability, reduce the need for large training datasets, and adapt better to diverse handwriting styles. However, results show that the CNN-based ResNet-18 architecture outperforms the 0-shot CoT prompt engineering approach with GPT-4o (Accuracy: 70%) and supervised fine-tuned PaliGemma (Accuracy: 71%), achieving an accuracy of 84% on the CEDAR AND dataset. These findings highlight the potential of VLMs in generating human-interpretable decisions while underscoring the need for further advancements to match the performance of specialized deep learning models., Comment: 4 Pages, 1 Figure, 1 Table, Accepted as Short paper at Irish Machine Vision and Image Processing (IMVIP) Conference
Published: 2024

31. Securing Proof of Stake Blockchains: Leveraging Multi-Agent Reinforcement Learning for Detecting and Mitigating Malicious Nodes

Author: Bappy, Faisal Haque, Hasan, Kamrul, Sajid, Md Sajidul Islam, Pritom, Mir Mehedi Ahsan, and Islam, Tariqul
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Cryptography and Security
Abstract: Proof of Stake (PoS) blockchains offer promising alternatives to traditional Proof of Work (PoW) systems, providing scalability and energy efficiency. However, blockchains operate in a decentralized manner and the network is composed of diverse users. This openness creates the potential for malicious nodes to disrupt the network in various ways. Therefore, it is crucial to embed a mechanism within the blockchain network to constantly monitor, identify, and eliminate these malicious nodes without involving any central authority. In this paper, we propose MRL-PoS+, a novel consensus algorithm to enhance the security of PoS blockchains by leveraging Multi-agent Reinforcement Learning (MRL) techniques. Our proposed consensus algorithm introduces a penalty-reward scheme for detecting and eliminating malicious nodes. This approach involves the detection of behaviors that can lead to potential attacks in a blockchain network and hence penalizes the malicious nodes, restricting them from performing certain actions. Our developed Proof of Concept demonstrates effectiveness in eliminating malicious nodes for six types of major attacks. Experimental results demonstrate that MRL-PoS+ significantly improves the attack resilience of PoS blockchains compared to the traditional schemes without incurring additional computation overhead.
Published: 2024

32. Local-Data-Hiding and Causal Inseparability: Probing Indefinite Causal Structures with Cryptographic Primitives

Author: Naik, Sahil Gopalkrishna, Sen, Samrat, Patra, Ram Krishna, Chakraborty, Ananya, Alimuddin, Mir, Banik, Manik, and Ghosal, Pratik
Subjects: Quantum Physics
Abstract: Formulation of physical theories typically assumes a definite causal structure -- either static or dynamic -- among the set of physical events. Recent studies, however, suggest the possibility of indefiniteness in causal structure, which emerges as a novel information primitive offering advantages in various protocols. In this work, we explore utilities of this new primitive in cryptographic applications. To this aim, we propose a task called local-data-hiding, where a referee distributes encrypted messages among distant parties in such a way that the parties individually remain completely ignorant about the messages, and thus try to decrypt their respective messages through mutual collaboration. As we demonstrate, agents embedded in an indefinite causal structure can outperform their counterparts operating in a definite causal background. Considering the bipartite local-bit-hiding (LBH) task, we establish a strict duality between its optimal success probability and the optimal violation of a causal inequality obtained from the guess-your-neighbour's-input game. This, in turn, provides a way forward to obtain Tsirelson-type bounds for causal inequalities. Furthermore, similar to Peres's separability criterion, we derive a necessary criterion for quantum processes to be useful in the LBH task. We then report an intriguing super-activation phenomenon, where two quantum processes, each individually not useful for the LBH task, become useful when used together. We also analyze the utility of causal indefiniteness arising in classical setups and show its advantages in multipartite variants of the local-data-hiding task. Along with establishing new cryptographic applications our study illuminates various unexplored aspects of causal indefiniteness, and welcomes further studies on this new information primitive., Comment: 15.25 pages + 4 figures; comments welcome
Published: 2024

33. DefTesPY: Cyber defense model with enhanced data modeling and analysis for Tesla company via Python Language

Author: Kshetri, Naresh, Sultana, Irin, Rahman, Mir Mehedi, and Shah, Darshana
Subjects: Computer Science - Cryptography and Security
Abstract: Several types of cyber-attacks on automobiles and business firms keep on rising as we are preparing to counter cybercrimes with several new technologies and defense models. Cyber defense (also, counter intelligence) is a computer network defense mechanism that involves response to activities, critical infrastructure protection, and information assurance for corporations, government bodies, and other conceivable networks. Cyber defense focuses on preventing, detecting, and responding to assaults or threats in a timely manner so that no infrastructure or information is compromised. With the increasing volume and complexity of cyber threats, most companies need cyber defense to protect sensitive information and assets. We can control attacker actions by utilizing firewalls at different levels, an intrusion detection system (IDS), with the intrusion prevention system (IPS) which can be installed independently or in combination with other protection approaches. Tesla is an American clean energy and automotive company in Austin, Texas, USA. The recent data breach at Tesla affected over 75,000 individuals as the company pinpoints two former employees as the offender revealing more than 23,000 internal files from 2015 to 2022. In this work, we will emphasize data modeling and data analysis using cyber defense model and python with a survey of the Tesla company. We have proposed a defense model, DefTesPY, with enhanced data modeling and data analysis based on the encountered cyber-attacks and cybercrimes for Tesla company till date., Comment: 11 pages, 4 figures
Published: 2024

34. Consequences of Godel Theorems on Third Quantized Theories Like String Field Theory and Group Field Theory

Author: Faizal, Mir, Shabir, Arshid, and Khan, Aatif Kaisar
Subjects: High Energy Physics - Theory
Abstract: The observation that spacetime and quantum fields on it have to be dynamically produced in any theory of quantum gravity implies that quantum gravity should be defined on configuration space of fields rather than spacetime. Such a theory that is defined on the configuration space of fields rather than spacetime is a third quantized theory. So, both string theory and group field theory are third-quantized theories. Thus, using axioms of string field theory, we motivate similar axioms for group field theory. Then using the structure of these axioms for string field theory and group field theory, we identify general features of axioms for any such third quantized theory of quantum gravity. Thus, we show that such third-quantized theories of quantum gravity can be formulated as formal axiomatic systems. We then analyze the consequences of Godel theorems on such third quantized theories. We thus address problems of consistency and completeness of any third quantized theories of quantum gravity., Comment: 14 PAGES
Published: 2024

35. Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run

Author: Raman, Gayathri, Ronchini, Samuele, Delaunay, James, Tohuvavohu, Aaron, Kennea, Jamie A., Parsotan, Tyler, Ambrosi, Elena, Bernardini, Maria Grazia, Campana, Sergio, Cusumano, Giancarlo, D'Ai, Antonino, D'Avanzo, Paolo, D'Elia, Valerio, De Pasquale, Massimiliano, Dichiara, Simone, Evans, Phil, Hartmann, Dieter, Kuin, Paul, Melandri, Andrea, O'Brien, Paul, Osborne, Julian P., Page, Kim, Palmer, David M., Sbarufatti, Boris, Tagliaferri, Gianpiero, Troja, Eleonora, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., 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., Anand, S., 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., 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., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., 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., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., 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., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., 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., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., 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., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., 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., Castaldi, G., 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, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, R. -J., Chanial, P., Chao, S., Chapman-Bird, C., 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, K. H., Chen, X., Chen, Yi-Ru, Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., 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., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Daw, E. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Dhani, A., Dhurandhar, S., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., 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., Emma, M., Engelby, E., Engl, A. J., 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., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Ferrante, I., Ferreira, T. A., Fidecaro, F., 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. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fukunaga, I., Fulda, P., Fyffe, M., Gabella, W. E., Gadre, B., Gair, J. R., Galaudage, S., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., George, J., George, R., Gerberding, O., Gergely, L., Ghadiri, N., Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., González, G., Goodarzi, P., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Argianas, L. Granda, Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Gruson, A. S., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurav, R., Gurs, J., Gutierrez, N., Guzman, F., Haba, D., Haberland, M., Haegel, L., Hain, G., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Healy, J., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A., Hertog, T., Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Hill, S., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, J., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Hollows, I. J., Holmes, Z. J., Holz, D. E., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, S. -C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huang, Y., Huang, Y. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Hur, R., Husa, S., Huxford, R., Huynh-Dinh, T., Iakovlev, A., Iandolo, G. A., Iess, A., Inayoshi, K., Inoue, Y., Iorio, G., Irwin, J., Isi, M., Ismail, M. A., Itoh, Y., Iwaya, M., Iyer, B. 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C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables
Published: 2024

36. Curriculum Is More Influential Than Haptic Information During Reinforcement Learning of Object Manipulation Against Gravity

Author: Ojaghi, Pegah, Mir, Romina, Marjaninejad, Ali, Erwin, Andrew, Wehner, Michael, and Valero-Cueva, Francisco J
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Learning to lift and rotate objects with the fingertips is necessary for autonomous in-hand dexterous manipulation. In our study, we explore the impact of various factors on successful learning strategies for this task. Specifically, we investigate the role of curriculum learning and haptic feedback in enabling the learning of dexterous manipulation. Using model-free Reinforcement Learning, we compare different curricula and two haptic information modalities (No-tactile vs. 3D-force sensing) for lifting and rotating a ball against gravity with a three-fingered simulated robotic hand with no visual input. Note that our best results were obtained when we used a novel curriculum-based learning rate scheduler, which adjusts the linearly-decaying learning rate when the reward is changed as it accelerates convergence to higher rewards. Our findings demonstrate that the choice of curriculum greatly biases the acquisition of different features of dexterous manipulation. Surprisingly, successful learning can be achieved even in the absence of tactile feedback, challenging conventional assumptions about the necessity of haptic information for dexterous manipulation tasks. We demonstrate the generalizability of our results to balls of different weights and sizes, underscoring the robustness of our learning approach. This work, therefore, emphasizes the importance of the choice curriculum and challenges long-held notions about the need for tactile information to autonomously learn in-hand dexterous manipulation.
Published: 2024

37. Nonlocal Locking of Observable Quantities: A Faithful Signature of Nonclassical Correlations

Author: Alimuddin, Mir, Chowdhury, Snehasish Roy, Patra, Ram Krishna, Ghosh, Subhendu B., Tufarelli, Tommaso, Adesso, Gerardo, and Banik, Manik
Subjects: Quantum Physics
Abstract: Nonclassicality in composite quantum systems depicts several puzzling manifestations, with Einstein-Podolsky-Rosen entanglement, Schr\"odinger steering, and Bell nonlocality being the most celebrated ones. In addition to those, an unentangled quantum state can also exhibit nonclassicality, as evidenced from notions such as quantum discord and work deficit. Here, we propose a general framework to investigate nonclassical correlations in multipartite quantum states. The distinct signatures left on observable quantities, depending on whether the sub-parts of a composite system are probed separately or jointly, provide an operational avenue to construct different quantifiers that faithfully capture signatures of nonclassicality in quantum states. Along the line we unveil an intriguing phenomenon referred to as `nonlocal locking of observable quantities', where the value of an observable quantity gets locked in the correlation of a nonclassical state. Our approach reduces the experimental demand for verification of nonclassicality in composite systems and can find applications for enhanced energy storage in quantum thermodynamical devices., Comment: Comments are welcome
Published: 2024

38. Maximizing Blockchain Performance: Mitigating Conflicting Transactions through Parallelism and Dependency Management

Author: Bappy, Faisal Haque, Zaman, Tarannum Shaila, Sajid, Md Sajidul Islam, Pritom, Mir Mehedi Ahsan, and Islam, Tariqul
Subjects: Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: While blockchains initially gained popularity in the realm of cryptocurrencies, their widespread adoption is expanding beyond conventional applications, driven by the imperative need for enhanced data security. Despite providing a secure network, blockchains come with certain tradeoffs, including high latency, lower throughput, and an increased number of transaction failures. A pivotal issue contributing to these challenges is the improper management of "conflicting transactions", commonly referred to as "contention". When a number of pending transactions within a blockchain collide with each other, this results in a state of contention. This situation worsens network latency, leads to the wastage of system resources, and ultimately contributes to reduced throughput and higher transaction failures. In response to this issue, in this work, we present a novel blockchain scheme that integrates transaction parallelism and an intelligent dependency manager aiming to reduce the occurrence of conflicting transactions within blockchain networks. In terms of effectiveness and efficiency, experimental results show that our scheme not only mitigates the challenges posed by conflicting transactions, but also outperforms both existing parallel and non-parallel Hyperledger Fabric blockchain networks achieving higher transaction success rate, throughput, and latency. The integration of our scheme with Hyperledger Fabric appears to be a promising solution for improving the overall performance and stability of blockchain networks in real-world applications.
Published: 2024

39. Particle production in HRG with thermodynamically consistent EoS and partially deformable hadrons

Author: Mir, Sameer Ahmad, Din, Iqbal Mohi Ud, Rather, Nasir Ahmad, Uddin, Saeed, and Mir, M. Farooq
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Theory
Abstract: In the present work, we analyze several strange as well as non-strange relative hadronic yields obtained in the ultra-relativistic heavy-ion collisions (URHIC) experiments over a wide range of centre-of-mass collision energy ($\sqrt{s_{NN}}$). We invoke the formation of a hot and dense hadronic resonance gas (HRG) in the final stage following the URHIC. We use an earlier proposed thermodynamically consistent approach for obtaining the equation of state (EoS) of a HRG. It takes into account an important aspect of the hadronic interaction, viz., the hadronic hard-core repulsion, by assigning hard-core volumes to the hadrons, leading to an excluded volume (EV) type effect. We have invoked the bag model approach to assign hard-core volumes to baryons (antibaryons) while treating mesons to be point particles. We employ ansatz to obtain the dependence of the temperature and BCP of the HRG system on the centre-of-mass energy in the URHIC. We also find strong evidence of double freeze-out scenario, corresponding to baryons (antibaryons) and mesons, respectively. Strangeness (anti-strangeness) imbalance factor is also seen to play an important role in explaining the ratio of strange hadrons to the non-strange ones. The HRG model can explain the experimental data on various relative hadronic multiplicities quite satisfactorily over a wide range of $\sqrt{s_{NN}}$, ranging from the lowest RHIC energies to the highest LHC energies for the set of the model parameters obtaining the best theoretical fit to the experimental data by minimizing the $\chi^{2}$/dof value., Comment: 14 pages, 12 figures
Published: 2024

40. Comparative biochemical and physiological responses to the virus-induced mosaic disease in apple (Malus domestica)

Author: Manzoor, Subaya, Nabi, Sajad Un, Rather, Tariq Rasool, Verma, M. K., Alrobaish, Shouaa A., Raja, Wasim H., Mir, Javid I., Wani, Tanveer Ahmad, Mir, Riyazul Rouf, Jeelani, Faheem, Parveen, Shugufta, and Mansoor, Sheikh
Published: 2024
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41. Hypercalcemic Ovarian Carcinoma (Small Cell Carcinoma of the Ovary, Hypercalcemic Type (SCCOHT)): A Case Series and Review of Literature of a Rare Malignancy

Author: C S, Chandan, Mir, Abdul Waheed, Mir, Ab Wahid, Dar, Firdous Ahmad, and Yasin, Syed Besina
Published: 2024
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42. Identification of Novel Genomic Variants in COVID-19 Patients Using Whole-Exome Sequencing: Exploring the Plausible Targets of Functional Genomics

Author: Mir, Rashid, Altemani, Faisal A., Algehainy, Naseh A., Alanazi, Mohammad A., Elfaki, Imadeldin, Alsayed, Badr A., Mir, Mohammad Muzaffar, Mustafa, Syed Khalid, Moawadh, Mamdoh S., Tayeb, Faris J., Alfailfi, Jaber, Alatawi, Sael M., Alhiwety, Mohammed Saad, and Ullah, Mohammad Fahad
Published: 2024
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43. Union and displacement characteristics following percutaneous screw fixation of superior pubic rami fractures

Author: Flanagan, Christopher D., Fairchild, Ryan, McCaskey, Meghan, Sajid, Mir Ibrahim, Watson, David, and Mir, Hassan
Published: 2024
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44. An Integrated Conceptual Model for Enhancing Refugee Education

Author: Mir Abdullah Miri
Abstract: This paper introduces a unified conceptual framework for understanding refugee identity construction within educational contexts, drawing on theories of social justice (Fraser, 2008), affective relations (Lynch, 2012), the resumption of ordinary life (Kohli, 2011) and ecological systems (Bronfenbrenner, 1977). Addressing a gap in the existing scholarship, the model emphasises the importance of affective relations in shaping refugee identities in education alongside other factors. The study examines the macro constructs of redistribution, recognition, representation and relational affect, highlighting their impact on refugee children's educational experiences. Further, it considers the micro constructs of safety, sense of belonging and success in integration and attainment in educational environments. The constructs are examined across various levels of Bronfenbrenner's ecological system, demonstrating the complexity of refugees' needs. This model aims to facilitate a holistic, justice-oriented approach to refugee education, which is crucial given the rise in global displacement. The study has significant implications for refugee education research and policymaking, potentially informing targeted interventions and programmes that foster academic success, overall well-being, and practice in refugee education.
Published: 2024
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45. Repeater-Based Quantum Communication Protocol: Maximizing Teleportation Fidelity with Minimal Entanglement

Author: Ghosal, Arkaprabha, Ghai, Jatin, Saha, Tanmay, Ghosh, Sibasish, and Alimuddin, Mir
Subjects: Quantum Physics
Abstract: Transmitting unknown quantum states to distant locations is crucial for distributed quantum information protocols. The seminal quantum teleportation scheme achieves this feat while requiring prior maximal entanglement between the sender and receiver. In scenarios with noisy entangled states, optimal teleportation fidelity characterizes the efficacy of transmitting the state, demanding the proper selection of local operations at the sender's and receiver's ends. The complexity escalates further in long-range communication setups, prompting the consideration of a repeater-based approach, which incorporates arrays of nodes with multiple segments to facilitate the efficient transmission of quantum information. The fidelity of the communication line gets degraded even if a single segment is affected by noise. In such cases, the general wisdom employs the standard entanglement swapping protocol involving maximally entangled states across the noiseless segments and applying maximally entangled basis measurement at the corresponding nodes to achieve optimal fidelity. In this Letter, we propose a more efficient protocol for a certain class of noisy states in any intermediary segment, achieving the same fidelity as the standard protocol while consuming less amount of entanglement. Our approach ensures enhanced teleportation fidelity even when the end-to-end state gets noisier, and thus promises efficient utility of quantum resources in repeater-based distributed quantum protocols., Comment: (4.5+11.5, 4 figures)
Published: 2024

46. Electron-electron interactions in partially mixed helical states

Author: Bakhshipour, Zeinab and Hosseini, Mir Vahid
Subjects: Condensed Matter - Strongly Correlated Electrons
Abstract: We theoretically study the effect of electron-electron interactions in one-dimensional partially mixed helical states. These helical states can be realized at the edges of two-dimensional topological insulators with partially broken time-reversal symmetry, resulting in helical gapped states. Using the bosonization method and renormalization group analysis, we identify weak gap, crossover, and strong gap regimes in the phase diagram. We find that strong electron-electron interaction mixes the helicity of the states, leading to the relevant strong gap regime. We investigate the charge and spin density wave correlation functions in different relevancy regimes of the gap mediated by interactions, where in the case of strong repulsive interaction, the spin density wave dominates the charge density wave. Additionally, employing the Memory function technique, we calculate the effect of mixed helicity on the charge transport in a sufficiently long edge. We find a non-uniform temperature dependence for the charge conductivity in both the strong and weak gap regimes with distinct features., Comment: 19 pages, 5 figures
Published: 2024

47. Topological phases of commensurate or incommensurate non-Hermitian Su-Schrieffer-Heeger lattices

Author: Jangjan, Milad, Li, Linhu, Torres, Luis E. F. Foa, and Hosseini, Mir Vahid
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: We theoretically investigate topological features of a one-dimensional Su-Schrieffer-Heeger lattice with modulating non-Hermitian on-site potentials containing four sublattices per unit cell. The lattice can be either commensurate or incommensurate. In the former case, the entire lattice can be mapped by supercells completely. While in the latter case, there are two extra lattice points, thereby making the last cell incomplete. We find that an anti-PT transition occurs at exceptional points of edge states at certain parameters, which does not coincide with the conventional topological phase transition characterized by the Berry phase, provided the imaginary on-site potential is large enough. Interestingly, when the potential exceeds a critical value, edge states appear even in the regime with a trivial Berry phase. To characterize these novel edge states we present topological invariants associated with the system's parity. Finally, we analyze the dynamics for initial states with different spatial distributions, which exhibit distinct dynamics for the commensurate and incommensurate cases, depending on the imaginary part of edge state energy., Comment: 10 pages, 8 figures
Published: 2024
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48. Asymptotic Birkhoff-Violation in Operational Theories: Thermodynamic Implications and Information Processing

Author: Chakraborty, Ananya, Naik, Sahil Gopalkrishna, Sen, Samrat, Patra, Ram Krishna, Ghosal, Pratik, Alimuddin, Mir, and Banik, Manik
Subjects: Quantum Physics, Mathematical Physics
Abstract: In accordance with the entropy principle of thermodynamics, under spontaneous evolutions, physical systems always evolve towards states with equal or greater randomness. But, where does this randomness originate? Renowned Birkhoff-von Neumann theorem, often referred to as Birkhoff theorem, identifies source of this randomness to be the stochastic application of reversible operations on the system under study, thereby ensuring its epistemic origin. Analogue of this theorem is known to fail in the quantum case. Here, we extend this investigation beyond quantum mechanics to a broader class of operational theories described within the framework of general probabilistic theories (GPTs). In this generalized framework, we establish Birkhoff-violation as the prevalent trait; in fact the asymptotic variant of the theorem gets violated. We then demonstrate that Birkhoff-violation in GPTs can lead to consequences that are atypical to quantum theory. For instance, we report manifestation of Birkhoff-violation in a communication task, which otherwise is not observed in quantum world. We also show that, unlike the quantum case, in other operational theories the state transformation criteria can be distinct under mixtures of reversible transformations and doubly stochastic evolutions, leading to different resource theories of purity. Despite these exotic implications, we analyze how to define a coherent notion of entropy in this generalized framework, while upholding alignment with von Neumann's thought experiment., Comment: (4.25 + 7) Pages, 6 Figures, Comments are welcome
Published: 2024

49. XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags

Author: Shohan, Faisal Tareque, Nayeem, Mir Tafseer, Islam, Samsul, Akash, Abu Ubaida, and Joty, Shafiq
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval
Abstract: Millions of news articles published online daily can overwhelm readers. Headlines and entity (topic) tags are essential for guiding readers to decide if the content is worth their time. While headline generation has been extensively studied, tag generation remains largely unexplored, yet it offers readers better access to topics of interest. The need for conciseness in capturing readers' attention necessitates improved content selection strategies for identifying salient and relevant segments within lengthy articles, thereby guiding language models effectively. To address this, we propose to leverage auxiliary information such as images and captions embedded in the articles to retrieve relevant sentences and utilize instruction tuning with variations to generate both headlines and tags for news articles in a multilingual context. To make use of the auxiliary information, we have compiled a dataset named XL-HeadTags, which includes 20 languages across 6 diverse language families. Through extensive evaluation, we demonstrate the effectiveness of our plug-and-play multimodal-multilingual retrievers for both tasks. Additionally, we have developed a suite of tools for processing and evaluating multilingual texts, significantly contributing to the research community by enabling more accurate and efficient analysis across languages., Comment: ACL 2024 camera ready. The first two authors contributed equally
Published: 2024

50. Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs

Author: Islam, Mohammed Saidul, Rahman, Raian, Masry, Ahmed, Laskar, Md Tahmid Rahman, Nayeem, Mir Tafseer, and Hoque, Enamul
Subjects: Computer Science - Computation and Language
Abstract: Natural language is a powerful complementary modality of communication for data visualizations, such as bar and line charts. To facilitate chart-based reasoning using natural language, various downstream tasks have been introduced recently such as chart question answering, chart summarization, and fact-checking with charts. These tasks pose a unique challenge, demanding both vision-language reasoning and a nuanced understanding of chart data tables, visual encodings, and natural language prompts. Despite the recent success of Large Language Models (LLMs) across diverse NLP tasks, their abilities and limitations in the realm of data visualization remain under-explored, possibly due to their lack of multi-modal capabilities. To bridge the gap, this paper presents the first comprehensive evaluation of the recently developed large vision language models (LVLMs) for chart understanding and reasoning tasks. Our evaluation includes a comprehensive assessment of LVLMs, including GPT-4V and Gemini, across four major chart reasoning tasks. Furthermore, we perform a qualitative evaluation of LVLMs' performance on a diverse range of charts, aiming to provide a thorough analysis of their strengths and weaknesses. Our findings reveal that LVLMs demonstrate impressive abilities in generating fluent texts covering high-level data insights while also encountering common problems like hallucinations, factual errors, and data bias. We highlight the key strengths and limitations of chart comprehension tasks, offering insights for future research.
Published: 2024

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