The ultramassive white dwarfs of the Alpha Persei cluster
D.R. Miller, I. Caiazzo, J. Heyl, H.B. Richer, P.-E. Tremblay, The Astrophysical Journal Letters 926 (2022).

Reconstructing the Pleiades with Gaia ED
J. Heyl, I. Caiazzo, H.B. Richer, The Astrophysical Journal 926 (2022).

Evolutionary and structural constraints influencing apolipoprotein AI amyloid behavior
R.A. Gisonno, T. Masson, N.A. Ramella, E.E. Barrera, V. Romanowski, M.A. Tricerri, Proteins: Structure, Function, and Bioinformatics 90 (2022) 258–269.

Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
A. Valenti, G. Jin, J. Leonard, S.D. Huber, E. Greplova, Physical Review A 105 (2022).

Adversarial robustness via noise injection in smoothed models
Y. Nemcovsky, E. Zheltonozhskii, C. Baskin, B. Chmiel, A.M. Bronstein, A. Mendelson, Applied Intelligence 53 (2022) 9483–9498.

Machine learning approaches demonstrate that protein structures carry information about their genetic coding
L. Ackerman-Schraier, A.A. Rosenberg, A. Marx, A.M. Bronstein, Scientific Reports 12 (2022).

Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon
A.A. Rosenberg, A. Marx, A.M. Bronstein, Nature Communications 13 (2022).

Embryologist agreement when assessing blastocyst implantation probability: Is data-driven prediction the solution to embryo assessment subjectivity?
D.E. Fordham, D. Rosentraub, A.L. Polsky, T. Aviram, Y. Wolf, O. Perl, A. Devir, S. Rosentraub, D.H. Silver, Y. Gold Zamir, A.M. Bronstein, M. Lara Lara, J. Ben Nagi, A. Alvarez, S. Munné, Human Reproduction 37 (2022) 2275–2290.

Recording Silence – Accurate Annotation of the Genetic Sequence Is Required to Better Understand How Synonymous Coding Affects Protein Structure and Disease
A.A. Rosenberg, A.M. Bronstein, A. Marx, in:, Z.E. Sauna, C. Kimchi-Sarfaty (Eds.), Single Nucleotide Polymorphisms, Springer Nature, Cham, 2022, pp. 37–47.

Baby steps towards few-shot learning with multiple semantics
E. Schwartz, L. Karlinsky, R. Feris, R. Giryes, A.M. Bronstein, Pattern Recognition Letters 160 (2022) 142–147.

Deep isometric maps
G. Pai, A.M. Bronstein, R. Talmon, R. Kimmel, Image and Vision Computing 123 (2022).

Inverse design of spontaneous parametric downconversion for generation of high-dimensional qudits
E. Rozenberg, A. Karnieli, O. Yesharim, J. Foley-Comer, S. Trajtenberg-Mills, D. Freedman, A.M. Bronstein, A. Arie, Optica 9 (2022) 602–615.

Deep fused two-step cross-modal hashing with multiple semantic supervision
P. Kang, Z. Lin, Z. Yang, A.M. Bronstein, Q. Li, W. Liu, Multimedia Tools and Applications 81 (2022) 15653–15670.

Self-supervised classification network
E. Amrani, L. Karlinsky, A.M. Bronstein, in:, 17th European Conference on Computer Vision, Springer Nature, 2022, pp. 116–132.

Mint: An accelerator for mining temporal motifs
N. Talati, H. Ye, S. Vedula, K.-Y. Chen, Y. Chen, D. Liu, Y. Yuan, D. Blaauw, A.M. Bronstein, T. Mudge, R. Dreslinski, in:, 55th IEEE/ACM International Symposium on Microarchitecture, Institute of Electrical and Electronics Engineers, 2022.

Contrast to divide: Self-supervised pre-training for learning with noisy labels
E. Zheltonozhskii, C. Baskin, A. Mendelson, A.M. Bronstein, O. Litany, in:, IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2022, pp. 387–397.

Intra-class low-rank regularization for supervised and semi-supervised cross-modal retrieval
P. Kang, Z. Lin, Z. Yang, X. Fang, A.M. Bronstein, Q. Li, W. Liu, Applied Intelligence 52 (2022) 33–54.

<0-Conjugated redox-active two-dimensional polymers as organic cathode materials
Z. Jin, Q. Cheng, A.M. Evans, J. Gray, R. Zhang, S.T. Bao, F. Wei, L. Venkataraman, Y. Yang, C. Nuckolls, Chemical Science 13 (2022) 3533–3538.

Highly conducting single-molecule topological insulators based on mono- and di-radical cations
L. Li, J.Z. Low, J. Wilhelm, G. Liao, S. Gunasekaran, C.R. Prindle, R.L. Starr, D. Golze, C. Nuckolls, M.L. Steigerwald, F. Evers, L.M. Campos, X. Yin, L. Venkataraman, Nature Chemistry 14 (2022) 1061–1067.

Theory of chirality induced spin selectivity: Progress and challenges
F. Evers, A. Aharony, N. BarGill, O. EntinWohlman, P. Hedegård, O. Hod, P. Jelinek, G. Kamieniarz, M. Lemeshko, K. Michaeli, V. Mujica, R. Naaman, Y. Paltiel, S. RefaelyAbramson, O. Tal, J. Thijssen, M. Thoss, J.M. van Ruitenbeek, L. Venkataraman, D.H. Waldeck, B. Yan, L. Kronik, Advanced Materials 34 (2022).