Below is the list of research publications for the UCD Institute for Discovery in the academic year 2022/23.
Assoc Professor Neil Hurley | |
D’Amico, E., Muhammad, K., Tragos, E., Smyth, B., Hurley, N., & Lawlor, A. (2023). Item Graph Convolution Collaborative Filtering for Inductive Recommendations. In Unknown Book (Vol. 13980 LNCS, pp. 249-263). doi:10.1007/978-3-031-28244-7_16 |
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D’Amico, E., Lawlor, A., & Hurley, N. (2023). Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation. In Unknown Book (Vol. 13937 LNCS, pp. 310-321). doi:10.1007/978-3-031-33380-4_24 |
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Professor Mohand Tahar Kechadi | |
Bansal, Y., Lillis, D., & Kechadi, M. -T. (2023). A Deep Learning Model for Heterogeneous Dataset Analysis - Application to Winter Wheat Crop Yield Prediction. In Communications in Computer and Information Science (pp. 182-194). Springer Nature Switzerland. doi:10.1007/978-3-031-43838-7_14 |
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Professor Brendan Murphy | |
Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Finite Mixture Models. In Model-Based Clustering, Classification, and Density Estimation Using mclust in R (pp. 7-26). Chapman and Hall/CRC. doi:10.1201/9781003277965-2 |
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Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Introduction. In Model-Based Clustering, Classification, and Density Estimation Using mclust in R (pp. 1-6). Chapman and Hall/CRC. doi:10.1201/9781003277965-1 |
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Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Model-Based Clustering. In Model-Based Clustering, Classification, and Density Estimation Using mclust in R (pp. 27-80). Chapman and Hall/CRC. doi:10.1201/9781003277965-3 |
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Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Mixture-Based Classification. In Model-Based Clustering, Classification, and Density Estimation Using mclust in R (pp. 81-128). Chapman and Hall/CRC. doi:10.1201/9781003277965-4 |
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Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Model-Based Density Estimation. In Model-Based Clustering, Classification, and Density Estimation Using mclust in R (pp. 129-152). Chapman and Hall/CRC. doi:10.1201/9781003277965-5 |
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Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Visualizing Gaussian Mixture Models. In Model-Based Clustering, Classification, and Density Estimation Using mclust in R (pp. 153-188). Chapman and Hall/CRC. doi:10.1201/9781003277965-6 |
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Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Miscellanea. In Model-Based Clustering, Classification, and Density Estimation Using mclust in R (pp. 189-226). Chapman and Hall/CRC. doi:10.1201/9781003277965-7 |
Assoc Professor Eimear Byrne | |
Byrne, E. (2023). q-Matroids, their Cyclic Flats and Relations to Codes. In Algebraic and Combinatorial Methods for Coding and Cryptography. CIRM, Marseilles. |
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Byrne, E. (2023). The Rank Generating Polynomial of a q-Polymatroid. In 29th Nordic Congress of Mathematicians with EMS. Aalborg, Denmark. |
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Byrne, E. (2023). Invariants of q-Polymatroids. In Society of Industrial and Applied Mathematics Conference on Applied Algebraic Geometry. Eindhoven. |
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Dr Kathleen Curran | |
Belton, N., Hagos, M. T., Lawlor, A., & Curran, K. M. (2023). FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2023-June (pp. 2978-2987). doi:10.1109/CVPRW59228.2023.00299 |
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Noonan, K., Hearne, R., Murphy, D., Gaffney, B., Sheehy, B., McVeigh, N., et al. (2023). AI Techniques to Differentiate Between Sub-Groups of Diffuse Cystic Lung Diseases Using Phenotypic Characteristics. In American Thoracic Society 2023 International Conference. doi:10.1164/ajrccm-conference.2023.207.1_MeetingAbstracts.A1117 |
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Leydon, P., & Curran, K. (2023). Synthetic Positron Emission Tomography Using Conditional-Generative Adversarial Networks for Healthy Baseline Image Generation. In IAPM. |
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Assoc Professor Neil Hurley | |
Tragos, E., O'Reilly-Morgan, D., Geraci, J., Shi, B., Smyth, B., Doherty, C., et al. (2023). Keeping People Active and Healthy at Home Using a Reinforcement Learning-based Fitness Recommendation Framework. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2023-August (pp. 6237-6245). |
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Professor Mohand Tahar Kechadi | |
Ikhlaq, U., & Kechadi, M. T. (2023). MACHINE LEARNING BASED NUTRIENT APPLICATION S TIMELINE RECOMMENDATION FOR SMART AGRICULTURE A LARGE SCALE DATA MINING APPROACH. In INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND NEW APPLICATIONS (pp. 42-47). Liverpool, UK. |
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Professor Michael O'Neill | |
O'Neill, M., & Gaire Sharma, D. (2023). A Comparative Analysis of Deep Learning Mobile Networks on Marine Vessel Detection. In 25th Irish Machine Vision and Image Processing Conference (IMVIP 2023). Galway. |
Assoc Professor Nicolae Buchete | |
Li, Y., Zhang, F., Herron, C. E., Rosales, I., Heredia, A., Buchete, N. -V., & Rodriguez, B. J. (2023). Cannabidiol-Mediated Sequestration of Alzheimer’s Amyloid-<i>ß</i> Peptides in ADDL Oligomers. American Journal of Molecular Biology, 13(02), 113-126. doi:10.4236/ajmb.2023.132008 |
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Narayan, B., Kiel, C., & Buchete, N. V. (2023). Classification of GTP-dependent K-Ras4B active and inactive conformational states. Journal of Chemical Physics, 158(9). doi:10.1063/5.0139181 |
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Mancardi, G., Mikolajczyk, A., Annapoorani, V. K., Bahl, A., Blekos, K., Burk, J., et al. (2023). A computational view on nanomaterial intrinsic and extrinsic features for nanosafety and sustainability. Materials Today, 67, 344-370. doi:10.1016/j.mattod.2023.05.029 |
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Assoc Professor Miguel Bustamante | |
Hayat, U., Ullah, I., Murtaza, G., Azam, N. A., & Bustamante, M. D. (2022). Enumerating Discrete Resonant Rossby/Drift Wave Triads and Their Application in Information Security. Mathematics, 10(23). doi:10.3390/math10234395 |
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Sattar, K. A., Haider, T., Hayat, U., & Bustamante, M. D. (2023). An Efficient and Secure Cryptographic Algorithm Using Elliptic Curves and Max-Plus Algebra-Based Wavelet Transform. Applied Sciences (Switzerland), 13(14). doi:10.3390/app13148385 |
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Assoc Professor Eimear Byrne | |
Byrne, E., Horlemann, A. L., Khathuria, K., & Weger, V. (2022). Density of free modules over finite chain rings. Linear Algebra and Its Applications, 651, 1-25. doi:10.1016/j.laa.2022.06.013 |
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Bonini, M., Borello, M., & Byrne, E. (2023). Saturating systems and the rank-metric covering radius. Journal of Algebraic Combinatorics. doi:10.1007/s10801-023-01269-9 |
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Byrne, E., Gnilke, O. W., & Kliewer, J. (2023). Straggler- and Adversary-Tolerant Secure Distributed Matrix Multiplication Using Polynomial Codes. Entropy, 25(2). doi:10.3390/e25020266 |
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Byrne, E., Ceria, M., Ionica, S., Jurrius, R., & Saçikara, E. (2023). Constructions of new matroids and designs over F<inf>q</inf>. Designs, Codes, and Cryptography, 91(2), 451-473. doi:10.1007/s10623-022-01087-3 |
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Byrne, E., & Weger, V. (2023). Bounds in the Lee metric and optimal codes. Finite Fields and their Applications, 87. doi:10.1016/j.ffa.2022.102151 |
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Dr Kathleen Curran | |
Leydon, P., O'Connell, M., Greene, D., & Curran, K. M. (2022). Bone segmentation in contrast enhanced whole-body computed tomography. Biomedical Physics and Engineering Express, 8(5). doi:10.1088/2057-1976/ac37ab |
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Professor Frederic Dias | |
Calvino, C., Dabrowski, T., & Dias, F. (2022). A study of the sea level and current effects on the sea state in Galway Bay, using the numerical model COAWST. Ocean Dynamics, 72(11-12), 761-774. doi:10.1007/s10236-022-01532-w |
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Smith, R., Dias, F., Facciolo, G., & Murphy, T. B. (2023). Pre-computation of image features for the classification of dynamic properties in breaking waves. European Journal of Remote Sensing, 56(1). doi:10.1080/22797254.2022.2163707 |
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Nie, B. C., Guan, X., Vanden-Broeck, J. M., & Dias, F. (2023). Air/water interfacial waves with a droplet at the tip of their crest. Physics of Fluids, 35(1). doi:10.1063/5.0131944 |
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Calvino, C., Dabrowski, T., & Dias, F. (2023). A study of the wave effects on the current circulation in Galway Bay, using the numerical model COAWST. Coastal Engineering, 180. doi:10.1016/j.coastaleng.2022.104251 |
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Renzi, E., Bergin, C., Kokina, T., Pelaez-Zapata, D. S., Giles, D., & Dias, F. (2023). Meteotsunamis and other anomalous "tidal surge" events in Western Europe in Summer 2022. Physics of Fluids, 35(4). doi:10.1063/5.0139220 |
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Stellin, F., Filoche, M., & Dias, F. (2023). Localization landscape for interacting Bose gases in one-dimensional speckle potentials. Physical Review A, 107(4). doi:10.1103/PhysRevA.107.043306 |
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Professor Nial Friel | |
Meagher, J., & Friel, N. (2022). Assessing epidemic curves for evidence of superspreading. Journal of the Royal Statistical Society. Series A: Statistics in Society, 185(4), 2179-2202. doi:10.1111/rssa.12919 |
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Piancastelli, L. S. C., Friel, N., Barreto-Souza, W., & Ombao, H. (2023). Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly Intractable Bayesian Inference. Journal of Computational and Graphical Statistics, 32(2), 483-500. doi:10.1080/10618600.2022.2116443 |
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Professor Claire Gormley | |
O’Reilly, D., Murphy, C. A., Moore, C. M., Ní Áinle, F., Gormley, I. C., Morrell, C. N., et al. (2023). Markers of platelet activation foR identification of late onset sEpsis in infaNTs: PARENT study protocol. Pediatric Research. doi:10.1038/s41390-023-02812-x |
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Gupta, S., Gormley, I. C., & Brennan, L. (2023). MetaboVariation: Exploring Individual Variation in Metabolite Levels. Metabolites, 13(2). doi:10.3390/metabo13020164 |
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Gormley, I. C., Murphy, T. B., & Raftery, A. E. (2023). Model-Based Clustering. Annual Review of Statistics and Its Application, 10, 573-595. doi:10.1146/annurev-statistics-033121-115326 |
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Frizzarin, M., Gormley, I. C., Berry, D. P., & McParland, S. (2023). Estimation of body condition score change in dairy cows in a seasonal calving pasture-based system using routinely available milk mid-infrared spectra and machine learning techniques. Journal of Dairy Science, 106(6), 4232-4244. doi:10.3168/jds.2022-22394 |
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Tavernier, E., Gormley, I. C., Delaby, L., McParland, S., O'Donovan, M., & Berry, D. P. (2023). Cow-level factors associated with nitrogen utilization in grazing dairy cows using a cross-sectional analysis of a large database.. Journal of dairy science, S0022-0302(23)00548-9. doi:10.3168/jds.2023-23606 |
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Frizzarin, M., Miglior, F., Berry, D. P., Gormley, I. C., & Baes, C. F. (2023). Usefulness of mid-infrared spectroscopy as a tool to estimate body condition score change from milk samples in intensively-fed dairy cows.. Journal of dairy science, S0022-0302(23)00554-4. doi:10.3168/jds.2023-23290 |
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Xian Yao, G., Gormley, I. C., & Fop, M. (2023). A Latent Shrinkage Position Model for Binary and Count Network Data. Bayesian Analysis. |
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Assoc Professor Russell Higgs | |
Yu, F., Bi, W., Cao, N., Li, H., & Higgs, R. (2023). Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract. Computer Systems Science and Engineering, 47(1), 1-17. doi:10.32604/csse.2023.018349 |
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Higgs, R. J., Finnegan, C., & Healy, D. (2023). Groups with three projective characters. Communications in Algebra. doi:10.1080/00927872.2023.2255670 |
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Li, L., Wang, Y., Zhu, D., Li, X., Du, H., Lu, Y., et al. (2023). Dis-NDVW: Distributed Network Asset Detection and Vulnerability Warning Platform. Computers, Materials and Continua, 76(1), 771-791. doi:10.32604/cmc.2023.038268 |
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Yu, H. J., Qiu, J., Cao, N., & Higgs, R. (2023). Intelligent Color Reasoning of IOT Based on P-laws. Computer Systems Science and Engineering, 45(3), 3181-3193. doi:10.32604/csse.2023.030985 |
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Li, L., Fu, Y., Zhu, D., Li, X., Sun, Y., Ding, J., et al. (2023). DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning. Intelligent Automation and Soft Computing, 36(3), 3295-3307. doi:10.32604/iasc.2023.035257 |
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You, C., Lin, G., Qiu, J., Cao, N., Sun, Y., & Higgs, R. (2023). Sensor Network Structure Recognition Based on P-law. Computer Systems Science and Engineering, 46(2), 1277-1292. doi:10.32604/csse.2023.026150 |
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Finnegan, C., & Higgs, R. J. (2023). Projective characters of metacyclic p-groups. Communications in Algebra, 51(7), 2736-2747. doi:10.1080/00927872.2023.2172176 |
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Higgs, R. J. (2023). HALL SUBGROUPS AND -COCYCLE REGULARITY. Bulletin of the Australian Mathematical Society, 24(3-4), 1-6. doi:10.1017/S0004972723000242 |
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Higgs, R. J. (2023). STRICT REGULARITY FOR -COCYCLES OF FINITE GROUPS. Bulletin of the Australian Mathematical Society, 46(2). doi:10.1017/S0004972723000230 |
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Assoc Professor Neil Hurley | |
Roy, A. S., D’amico, E., Lawlor, A., & Hurley, N. (2023). Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems. Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS, 36. doi:10.32473/flairs.36.133307 |
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Professor Mohand Tahar Kechadi | |
Tufford, A. R., Diou, C., Lucassen, D. A., Ioakimidis, I., O'Malley, G., Alagialoglou, L., et al. (2022). Toward Systems Models for Obesity Prevention: A Big Role for Big Data. Current Developments in Nutrition, 6(9). doi:10.1093/cdn/nzac123 |
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Chergui, N., & Kechadi, M. T. (2022). Data analytics for crop management: a big data view. Journal of Big Data, 9(1). doi:10.1186/s40537-022-00668-2 |
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Thantilage, R. D., Le-Khac, N. A., & Kechadi, M. T. (2023). Healthcare data security and privacy in Data Warehouse architectures. Informatics in Medicine Unlocked, 39. doi:10.1016/j.imu.2023.101270 |
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Dhelim, S., Aung, N., Kechadi, M. T., Ning, H., Chen, L., & Lakas, A. (2023). Trust2Vec: Large-Scale IoT Trust Management System Based on Signed Network Embeddings. IEEE Internet of Things Journal, 10(1), 553-562. doi:10.1109/JIOT.2022.3201772 |
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Aung, N., Dhelim, S., Chen, L., Lakas, A., Zhang, W., Ning, H., et al. (2023). VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning. IEEE Transactions on Intelligent Transportation Systems, 24(8), 8638-8649. doi:10.1109/TITS.2023.3250320 |
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Assoc Professor Vladimir Lobaskin | |
Wyrzykowska, E., Mikolajczyk, A., Lynch, I., Jeliazkova, N., Kochev, N., Sarimveis, H., et al. (2022). Representing and describing nanomaterials in predictive nanoinformatics. Nature Nanotechnology, 17(9), 924-932. doi:10.1038/s41565-022-01173-6 |
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Hasenkopf, I., Mills-Goodlet, R., Johnson, L., Rouse, I., Geppert, M., Duschl, A., et al. (2022). Computational prediction and experimental analysis of the nanoparticle-protein corona: Showcasing an in vitro-in silico workflow providing FAIR data. Nano Today, 46. doi:10.1016/j.nantod.2022.101561 |
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Rouse, I., & Lobaskin, V. (2022). Machine-learning based prediction of small molecule-surface interaction potentials. Faraday Discussions, 244, 306-335. doi:10.1039/d2fd00155a |
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Exner, T. E., Papadiamantis, A. G., Melagraki, G., Amos, J. D., Bossa, N., Gakis, G. P., et al. (2023). Metadata stewardship in nanosafety research: learning from the past, preparing for an “on-the-fly” FAIR future. Frontiers in Physics, 11. doi:10.3389/fphy.2023.1233879 |
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Mosaddeghi Amini, P., Subbotina, J., & Lobaskin, V. (2023). Milk Protein Adsorption on Metallic Iron Surfaces. Nanomaterials, 13(12). doi:10.3390/nano13121857 |
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Subbotina, J., Rouse, I., & Lobaskin, V. (2023). In silico prediction of protein binding affinities onto core-shell PEGylated noble metal nanoparticles for rational design of drug nanocarriers. Nanoscale, 15(32), 13371-13383. doi:10.1039/d3nr03264g |
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del Giudice, G., Serra, A., Saarimäki, L. A., Kotsis, K., Rouse, I., Colibaba, S. A., et al. (2023). An ancestral molecular response to nanomaterial particulates. Nature Nanotechnology, 18(8), 957-966. doi:10.1038/s41565-023-01393-4 |
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Assoc Professor Sean McGarraghy | |
Juan, A. A., Keenan, P., Martí, R., McGarraghy, S., Panadero, J., Carroll, P., & Oliva, D. (2023). A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics. Annals of Operations Research, 320(2), 831-861. doi:10.1007/s10479-021-04142-9 |
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Professor Gary McGuire | |
Gow, R., & McGuire, G. (2023). On Galois groups of linearized polynomials related to the special linear group of prime degree. Linear Algebra and Its Applications. doi:10.1016/j.laa.2023.03.004 |
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Professor Brendan Murphy | |
Ng, T. L. J., & Murphy, T. B. (2022). Model-based clustering for random hypergraphs. Advances in Data Analysis and Classification, 16(3), 691-723. doi:10.1007/s11634-021-00454-7 |
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Casa, A., O’callaghan, T. F., & Murphy, T. B. (2022). PARSIMONIOUS BAYESIAN FACTOR ANALYSIS FOR MODELLING LATENT STRUCTURES IN SPECTROSCOPY DATA. Annals of Applied Statistics, 16(4), 2417-2436. doi:10.1214/21-AOAS1597 |
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Smith, R., Dias, F., Facciolo, G., & Murphy, T. B. (2023). Pre-computation of image features for the classification of dynamic properties in breaking waves. European Journal of Remote Sensing, 56(1). doi:10.1080/22797254.2022.2163707 |
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Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Model-Based Clustering, Classification, and Density Estimation Using mclust in R, 1-242. doi:10.1201/9781003277965 |
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Gormley, I. C., Murphy, T. B., & Raftery, A. E. (2023). Model-Based Clustering. Annual Review of Statistics and Its Application, 10, 573-595. doi:10.1146/annurev-statistics-033121-115326 |
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Bocci, C., Gottard, A., Murphy, T. B., & Porzio, G. C. (2023). CLADAG 2021 special issue: Selected papers on classification and data analysis. Statistical Analysis and Data Mining, 16(4), 317-318. doi:10.1002/sam.11633 |
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Dr Miguel Nicolau | |
Mauceri, S., Sweeney, J., Nicolau, M., & McDermott, J. (2023). Dissimilarity-Preserving Representation Learning for One-Class Time Series Classification. IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2023.3273503 |
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Professor Adrian Ottewill | |
Taylor, P., Breen, C., & Ottewill, A. (n.d.). Mode-sum prescription for the renormalized stress energy tensor on black hole spacetimes. Physical Review D, 106(6). doi:10.1103/physrevd.106.065023 |
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Zilberman, N., Casals, M., Ori, A., & Ottewill, A. C. (2022). Two-point function of a quantum scalar field in the interior region of a Kerr black hole. Physical Review D, 106(12). doi:10.1103/PhysRevD.106.125011 |
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Zilberman, N., Casals, M., Ori, A., & Ottewill, A. C. (2022). Quantum Fluxes at the Inner Horizon of a Spinning Black Hole. Physical review letters, 129(26), 261102. doi:10.1103/PhysRevLett.129.261102 |
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Assoc Professor Gianluca Pollastri | |
Mahmoud, S. S. M., Portelli, B., D’agostino, G., Pollastri, G., Serra, G., & Fogolari, F. (2023). A Comparison of Mutual Information, Linear Models and Deep Learning Networks for Protein Secondary Structure Prediction. Current Bioinformatics, 18(8), 631-646. doi:10.2174/1574893618666230417103346 |
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Villaman, C., Pollastri, G., Saez, M., & Martin, A. J. M. (2023). Benefiting from the intrinsic role of epigenetics to predict patterns of CTCF binding. Computational and Structural Biotechnology Journal, 21, 3024-3031. doi:10.1016/j.csbj.2023.05.012 |
Professor Frederic Dias | |
Peláez Zapata, D. S., Pakrashi, V., & Dias, F. (2023). Observations of the ocean waves directional spreading during the HIGHWAVE project and SUMOS campaign.. doi:10.5194/egusphere-egu23-6067 |
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Andraud, P., Gailler, A., Dias, F., & Vayatis, N. (2023). Deep learning approach for real-time tsunami impact forecasting in near field context – application to the French Mediterranean coastline. doi:10.5194/egusphere-egu23-7763 |
Assoc Professor Vladimir Lobaskin | |
Mosaddeghi Amini, P., Subbotina, J., & Lobaskin, V. (2023). Multiscale Modelling of Milk Proteins Adsorption on Metallic Iron Surfaces. doi:10.20944/preprints202305.1711.v1 |
Professor Adrian Ottewill | |
Wardell, B., Warburton, N., Fransen, K., Upton, S., Cunningham, K., Ottewill, A., & Casals, M. (2023). SpinWeightedSpheroidalHarmonics [Computer Software]. Zenodo. doi:10.5281/zenodo.8112931 |
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Wardell, B., Warburton, N., Cunningham, K., Durkan, L., Leather, B., Nasipak, Z., et al. (2023). Teukolsky [Computer Software]. Zenodo. doi:10.5281/zenodo.8272374 |