Papers

All entries are freely available in some form (usually, a preprint). Entries in brackets are not yet accepted for journal publication

(54.) C. Schuer et al., Daphnids Can Safeguard the Use of Alternative Bioassays to the Acute Fish Toxicity Test: A Focus on Neurotoxicity, Environmental Toxicology and Chemistry [submitted] (2024), bioRxiv, 2024.09. 12.612652

(53.) C. Chen et al., Producing Plankton Classifiers that are Robust to Dataset Shift, Limnol. Oceanogr.: Methods [submitted] (2024), arXiv:2401.14256

52. L. Gasser, C. Schuer, F. Perez-Cruz, K. Schirmer and M. Baity-Jesi, Machine learning-based prediction of fish acute mortality: Implementation, interpretation, and regulatory relevance, Environ. Sci.: Adv., 2024,3, 1124-1138, bioRxiv:2024.03.14.584983

(51.) S. Merkli et al., Automated plankton monitoring suggests a key role of microzooplankton and temperature for predicting dynamics of phytoplankton size classes, ISME Communications [submitted] (2024), bioRxiv:2024.02.23.581723

50. P. Reichert et al., Metamorphic Testing of Machine Learning and Conceptual Hydrological Models, Hydrol. Earth Syst. Sci., 28, 2505–2529 (2024)

49. I. Paga et al., Quantifying memory in spin glasses, Phys. Rev. Lett. [accepted] (2024), arXiv:2307.02224

48. E. Francazi, A. Lucchi, M. Baity-Jesi, Initial Guessing Bias: How Untrained Networks Favor Some Classes, Proceedings of the 41^st International Conference on Machine Learning [ICML 2024], PMLR 235:13783-13839, arXiv:2306.00809

47. A. Altieri and M. Baity-Jesi, An Introduction to the Theory of Spin Glasses, Encyclopedia of Condensed Matter Physics (Second Edition) Volume 2, 2024, arXiv:2302.04842

46. M. Baity-Jesi et al., Multifractality in the off-equilibrium dynamics of spin glasses, PNAS 121.2 (2024): e2312880120, arXiv:2306.04591

45. C. Schuer, L. Gasser, F. Perez-Cruz, K. Schirmer, M. Baity-Jesi, A Benchmark Dataset for Machine Learning in Ecotoxicology, Nature Scientific Data 10, 718 (2023), bioRxiv:2023.05.27.542160

44. C. Shen et al., Differentiable modeling to unify machine learning and physical models and advance Geosciences, Nature Reviews Earth & Environment 4, 552–567 (2023), arXiv:2301.04027

43. I. Paga et al., Superposition principle and nonlinear response in spin glasses, Phys. Rev. B 107 214436 (2023), arXiv:2207.10640

42. M. Baity-Jesi et al., Memory and rejuvenation in spin glasses: aging systems are ruled by more than one length-scale, Nature Physics 19, 978–985 (2023), arXiv:2207.06207

41. S.P. Kyathanahally et al., Author Correction: Ensembles of Data-efficient Vision Transformers: a New Paradigm for Automated Classification in Ecology, Scientific Reports 13, 6243 (2023)

40. E. Francazi, M. Baity-Jesi and A. Lucchi, A Theoretical Analysis of the Learning Dynamics under Class Imbalance, Proceedings of the 40^th International Conference on Machine Learning [ICML 2023], Honolulu, Hawaii, Proc. Mac. Lear. Res. 202, 2023, arXiv:2207.00391

39. S. Othman, J. Schulz, M. Baity-Jesi and C. De Bacco, Modeling Node Exposure for Community Detection in Networks, Studies in Computational Intelligence 1078, 233–244 (2023), arXiv:2207.02523

38. I.R. McFadden et al., Linking human impacts to community processes in terrestrial and freshwater ecosystems, Ecology Letters, 2023; 26: 203–218 [article featured in the cover of Vol 26, Issue 2]

37. S.P. Kyathanahally et al., Ensembles of Data-efficient Vision Transformers: a New Paradigm for Automated Classification in Ecology, Scientific Reports 12, 18590 (2022), arXiv:2203.01726

36. A. Sendek et al., Fewer non-native insects in freshwater than in terrestrial habitats across continents, Diversity and Distributions, 28, 2303–2315 (2022), biorxiv:10.1101/2022.03.22.485042v1

35. M. Hoege et al., Improving hydrologic models for predictions and process understanding using Neural ODEs, Hydrol. Earth Syst. Sci., 26, 5085–5102 (2022)

34. M.R. Carbone and M. Baity-Jesi, Competition between Barrier- and Entropy-Driven Activation in Glasses, Phys. Rev. E 106, 024603 (2022), arXiv:2201.01208

33. J. Wu et al., Predicting Chemical Hazard across Taxa through Machine Learning, Environment International 163, 107184 (2022), arXiv:2110.03688

32. S.P. Kyathanahally et al., Deep Learning Classification of Lake Zooplankton, Front. Microbiol. 12:746297 (2021), arXiv:2108.05258

31. E. Merz et al., Underwater dual-magnification imaging for automated lake plankton monitoring, Water Research 203, 2021, 117524, bioRxiv:2021.04.14.439767

30. M. Baity-Jesi, G. Biroli and D.R. Reichman, Revisiting the Concept of Activation in Supercooled Liquids, Eur. Phys. J. E 44, 77 (2021), arXiv:2103.07211

29. I. Paga et al., Spin-glass dynamics in the presence of a magnetic field: exploration of microscopic properties, J. Stat. Mech. (2021) 033301, arXiv:2101.00821

28. M. Baity-Jesi et al., Temperature chaos is present in off-equilibrium spin-glass dynamics, Communications Physics 4, 74 (2021), arXiv:2011.09419

27. Q. Zhai et al., A scaling law describes the spin-glass response in theory, experiments and simulations, Phys. Rev. Lett. 125, 237202 (2020), arXiv:2007.03871

26. M.R. Carbone, V. Astuti and M. Baity-Jesi, Effective Trap-like Activated Dynamics in a Continuous Landscape, Phys. Rev. E 101, 052304 (2020), arXiv:2001.02567

25. M. Baity-Jesi and D.R. Reichman, On mean-field theories of dynamics in supercooled liquids, J. Chem. Phys. 151 (2019), 084503, arXiv:1906.05818

24. I. Hartarsky, M. Baity-Jesi, R. Ravasio, A. Billoire and G. Biroli, Maximum-energy records in glassy landscapes, J. Stat. Mech. (2019) 093302, arXiv:1904.08024

23. M. Baity-Jesi and V. Martin-Mayor, Precursors of the Spin Glass Transition in Three Dimensions, J. Stat. Mech. (2019) 084016, arXiv:1901.05581

22. M. Geiger et al., Jamming transition as a paradigm to understand the loss landscape of deep neural networks, Phys. Rev. E 100, 012115 (2019), arXiv:1809.09349

21. M. Baity-Jesi et al., The Mpemba effect in spin glasses is a persistent memory effect, Proc. Natl. Ac. Sci. Jul 2019, 201819803, arXiv:1804.07569

20. M. Baity-Jesi et al., Comparing Dynamics: Deep Neural Networks versus Glassy Systems, Proceedings of the 35^th International Conference on Machine Learning [ICML 2018], Stockholm, Sweden, Proc. Mac. Lear. Res. 80 324--333, 2018, arXiv:1803.06969

19. M. Baity-Jesi, A. Achard-de Lustrac and G. Biroli, Activated dynamics: an intermediate model between random energy model and p-spin model, Phys. Rev. E 98 1, 012133 (2018), arXiv:1805.04581

18. M. Baity-Jesi, C. Cammarota and G. Biroli, Activated Aging Dynamics and Effective Trap Model Description in the Random Energy Model, J. Stat. Mech. (2018) 013301, arXiv:1708.03268

17. M. Baity-Jesi et al., Aging rate of spin glasses from simulations matches experiments, Phys. Rev. Lett. 120 267203 (2018), arXiv:1803.02264

16. M. Baity-Jesi et al., Matching microscopic and macroscopic responses in glasses, Phys. Rev. Lett. 118, 157202 (2017), arXiv:1704.07777

15. M. Baity-Jesi et al., A statics-dynamics equivalence through the fluctuation-dissipation ratio provides a window into the spin-glass phase from nonequilibrium measurements, Proc. Natl. Acad. Sci. USA 114 (2017), 1838-1843, arXiv:1610.01418

14. M. Baity-Jesi, C.P. Goodrich, A.J. Liu, S.R. Nagel and J.P. Sethna, Emergent SO(3) Symmetry of the Frictionless Shear Jamming Transition, J. Stat. Phys., (2017) 167:735, arXiv:1609.00280

13. M. Baity-Jesi, V. Martin-Mayor, G. Parisi and S. Perez-Gaviro, Soft Modes, Localization and Two-Level Systems in Spin Glasses, Phys. Rev. Lett. 115, 267205 (2015), arXiv:1506.04927

12. L. Yan, M. Baity-Jesi, M. Mueller and M. Wyart, Dynamics and Correlations among Soft Excitations in Marginally Stable Glasses, Phys. Rev. Lett. 114, 247208 (2015), arXiv:1501.03017

11. M. Baity-Jesi and G. Parisi, Inherent Structures in m-component Spin Glasses, Phys. Rev. B 91, 134203 (2015), arXiv:1410.2163

10. M. Baity-Jesi et al., The three dimensional Ising spin glass in an external magnetic field: the role of the silent majority, J. Stat. Mech. (2014) P05014, arXiv:1403.2622 [article selected as journal highlight]

9. M. Baity-Jesi et al., Dynamical Transition in the D=3 Edwards-Anderson spin glass in an external magnetic field, Phys. Rev. E 89, 032140 (2014), arXiv:1307.4998

8. M. Baity-Jesi, L.A. Fernandez, V. Martin-Mayor and J.M. Sanz, Phase Transition in 3d Heisenberg Spin Glasses with Strong Anisotropies, through a Multi-GPU Parallelization, Phys. Rev. B, 89, 014202 (2014), arXiv:1309.1599

7. M. Baity-Jesi et al., Janus II: a new generation application-driven computer for spin-system simulations, Computer Physics Communications 185, 2, 550-559 (2014), arXiv:1310.1032

6. M. Baity-Jesi et al., An FPGA-Based Supercomputer for Statistical Physics: The Weird Case of Janus from High-Performance Computing Using FPGAs, Vanderbauwhede, Wim; Benkrid, Khaled (Eds.) 2013, Springer

5. M. Baity-Jesi et al., Critical parameters of the three-dimensional Ising spin glass, Phys. Rev. B 88, 224416 (2013), arXiv:1310.2910

4. M. Baity-Jesi et al., The Janus project: boosting spin-glass simulations using FPGAs, IFAC Proceedings 46, 28 227-232 (2013)

3. M. Baity-Jesi et al., Reconfigurable computing for Monte Carlo simulations: results and prospects of the Janus project, The European Physical Journal - Special Topics, 210, 1, 33-51 (2012), arXiv:1204.4134

2. M. Baity-Jesi et al., Spin Glass Simulations on the Janus Architecture: A Desperate Quest for Strong Scaling, Euro-Par 2012. Lecture Notes in Computer Science 7640, 528--537

1. M. Baity-Jesi et al., Janus 2: an FPGA-based supercomputer for spin glass simulations, Proceedings of the Future HPC Systems: the Challenges of Power Constrained Performance (FutureHPC '12), p. 2:1-2:11, New York, NY, USA:ACM, 2012

Deliverables

1. G. Losilla-Anadon, V. Martin-Mayor, M. Baity-Jesi, A. Castro-Barrigon, X. Meng, C. Yu, Deliverable 3.1: Assessment results and forum discussion report, 2013. Project: SCC-Computing (FP7, European Union)

Books

1. M. Baity-Jesi, Spin Glasses: Criticality and Energy Landscapes, Springer International Publishing Switzerland, 2016, arXiv:1602.08239

Data Sets

3. C. Schuer, L. Gasser, F. Perez-Cruz, K. Schirmer, and M. Baity-Jesi (2023). Data for: A Benchmark Dataset for Machine Learning in Ecotoxicology (Version 1.0) [Data set]. Eawag: Swiss Federal Institute of Aquatic Science and Technology

2. S.P. Kyathanahally et al., Data for: Deep Learning Classification of Lake Zooplankton, Eawag: Swiss Federal Institute of Aquatic Science and Technology, 2021, Creative Commons Zero v1.0 Universal

1. {E. Merz}, {T. Kozakiewicz}, {M. Reyes}, C. Ebi, {P. Isles}, M. Baity-Jesi, P. Roberts, J. S. Jaffe, {S. Dennis}, {T. Hardeman}, N. Stevens, {T. Lorimer}, {F. Pomati} (2021). Data for: Underwater dual-magnification imaging for automated lake plankton monitoring (Version 1.0). Eawag: Swiss Federal Institute of Aquatic Science and Technology. \url{https://doi.org/10.25678/0004BW}