discovery

UCD Institute for Discovery

UCD Institute for Discovery - Research Publications 2022/23

Below is the list of research publications for the UCD Institute for Discovery in the academic year 2022/23.



Chapter

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

Available Online
 

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

Available Online
 
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

Available Online
 

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

Available Online
 

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

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Conference Paper

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.

 

Byrne, E. (2023). The Rank Generating Polynomial of a q-Polymatroid. In 29th Nordic Congress of Mathematicians with EMS. Aalborg, Denmark.

 

Byrne, E. (2023). Invariants of q-Polymatroids. In Society of Industrial and Applied Mathematics Conference on Applied Algebraic Geometry. Eindhoven.

 
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

Available Online
 

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

Available Online
 

Leydon, P., & Curran, K. (2023). Synthetic Positron Emission Tomography Using Conditional-Generative Adversarial Networks for Healthy Baseline Image Generation. In IAPM.

 
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).

 
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.

 
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.

 

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Journal article

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

Available Online
 
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

Available Online
 

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.

 
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

Available Online
 

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

Available Online
 

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

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Other

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

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Preprint

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

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Software / Code

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

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