This study uses the Palestinian Oral History Archive (POHA) to investigate how Palestinian refugee groups in Lebanon sustain a cohesive collective memory of the Nakba through shared narratives. Grounded in Halbwachs' theory of group memory, we employ statistical analysis of pairwise similarity of narratives, focusing on the influence of shared gender and location. We use textual representation and semantic embeddings of narratives to represent the interviews themselves. Our analysis demonstrates that shared origin is a powerful determinant of narrative similarity across thematic keywords, landmarks, and significant figures, as well as in semantic embeddings of the narratives. Meanwhile, shared residence fosters cohesion, with its impact significantly amplified when paired with shared origin. Additionally, women's narratives exhibit heightened thematic cohesion, particularly in recounting experiences of the British occupation, underscoring the gendered dimensions of memory formation. This research deepens the understanding of collective memory in diasporic settings, emphasizing the critical role of oral histories in safeguarding Palestinian identity and resisting erasure.

Ten video games have received nominations for the 36th Annual GLAAD Media Awards. This program celebrates media works that feature "fair, accurate and inclusive representations of the lesbian, gay, bisexual, transgender and queer (LGBTQ) community and the issues that affect their lives." There are nominees for television, film, music, theater, journalism and comics as well as video games. One of the 2024 nominees for outstanding video game is the re-release of Paper Mario: The Thousand Year Door for the Nintendo Switch. The original Japanese version of the GameCube title included a minor character named Vivian who was transgender. The game contained dialogue about her challenges being misgendered and her journey to understanding her own identity.

EDIT: After reading the author's rebuttal, I changed my assessment of the paper to an accept. The paper is well written and it does a good job at explaining the intuition behind the proposed algorithm. I appreciated the inclusion of the small dimensional toy example as it illustrates in a simple and clear manner the adaptability property of the algorithm. My main concern with the proposed algorithm is that, in my opinion, it is most suitable for small dimensional problems only. The provided examples further justify my impression given that posterior distribution to sample from is of reduced dimension. Consequently, I'm having a hard time justifying the interest of the ML community with respect to the proposed sampling algorithm considering its perceived limited scope.

However, even after reading the rebuttal, I feel that it is a bit premature to publish the research at this point in time. In the rebuttal, the authors acknowledge that their method is not the first universal monotonic approximator and clarify that their language regarding the "cap on expressiveness" of alternative monotonic approximators refers to the non-asymptotic case, i.e., a finite number of neurons/hidden units. They write "we believe that the constraints on the positiveness of the weights and on the class of possible activation functions are unnecessarily restraining the hypothesis space in the non-asymptotic case". However, this is an assertion for which they have not supplied any kind of proof, and I find it highly debatable. Any method, whether it is their UMNN or the Huang approach or lattices or max/min networks, has some cap on expressiveness in the non-asymptotic case.

Originality: The main originality of the paper is a data transformation process applied to tabular data so a GAN can learn from them. This is definitely higher novel and can be potentially useful in similar situations involving such distributions. Apart from this, however, I feel that the authors are overclaiming a bit regarding several challenge/contributions: -C2 (L86): The choice of activation function certainly depends on the data format, listing that as a "challenge" seems a bit too much to me, unless the authors can point out non-trivial adaptations they made to address the problem (and apologize if I missed that...) -C4 (L98): again, hardly something new -C5 (L105): mode collapse is certainly well studied in literature (speaking of which, the authors should add references on newer approaches such as BourGAN), using an off-the-shelf solution (PacGAN), again, does not seem to me as an important contribution. Rephrasing the section and focus on the important contributions (C3, and perhaps C1) will make the contributions of the paper more clear, in my opinion. Quality: The paper is of high quality and the description of techniques is sound.

This paper demonstrates that an analysis relied upon in a previous paper (Nasr et al., 2019) to identify number-sensitive units in a neural network trained for object recognition is flawed, and that indeed the same network with randomly initialized weights also has a large number of number sensitive units. Moreover, the number of units detected depends strongly on the sample size of the statistical test, with larger sample sizes detecting no number sensitive units. The paper additionally performs some analyses on a network trained specifically to predict number. The reviewers generally felt that the demonstration of Nasr et al.'s flawed analysis was important, with R2 arguing that the work is "imperative to publish" and R1 and R3 finding the experiments in the first part of the paper convincing. However, R1, R3, and R4 all had concerns with the second part of the paper, in which it is claimed that a network trained to classify number (Nu-Net) can learn to subitize. I feel that the results in the first part of the paper are sufficiently impactful that the paper should be accepted.

Additional Feedback: Post-rebuttal comments: I've read the rebuttal and other reviews. The authors have addressed most of my concerns and hence I increase my score. I hope the authors would make the suggested edits in the revised version and explain the role of their main assumption. Can you explain why things fail if this assumption does not hold? Can you make use of a prior (in the case it is informative)?

When I interviewed a nurse practitioner in California about what she cherished most about nursing, it was the "human element" of being present with others. "I think we all just want acknowledgment of our suffering, even if you can't cure it or do anything about it," she told me. She still remembered when a homeless man came into her clinic, his back hunched, feet gnarled and callused from being on the streets for years, and she "just sat and did wound care for his feet". The moment stood out for her, in part because the opportunity to take that kind of time is getting rarer in clinics and hospitals as drives for efficiency impose time constraints. Washing his feet captured what nursing was about for her: the humility, the service, the witnessing.

Weaknesses: My main concerns are the following: 1) Although well-motivated, the paper does not include a related work section to help place this work in the community. Have others worked on combining ML with climate models before? I am aware of many works in Astrophysics that have done so, but I am not too familiar with this domain and would benefit from a summary of others working on similar ML approaches. Is this the prior variance of the weights? I would be interested to hear more details about this and how it was applied here.

Overall I found the paper to be solid and rather enjoyable, and I would qualify it as a strong candidate for a poster. The authors' method of plotting velocity fields by decomposing the velocity into direction and speed, which they've apparently introduced, is especially effective. It made their arguments and conclusions much easier to follow, and will hopefully be picked up by others. In my opinion stating that this approach leads to "interpretable models" might be somewhat overselling the results – the interpretability of the results is still hampered by the fact that models are composed by 10-100 more or less arbitrary basis functions. That being said, their capacity to reproduce salient features of the phase diagram certainly makes them more interpretable than, say, recurrent neural networks.