לוח שנה משולב

A large portion of research in 2D materials is limited to mechanical exfoliation of van der Waals (vdW) materials. Chemical exfoliation is a relatively under-utilized route for preparing ultra-thin quantum materials, but it accesses 2D materials that cannot be obtained by mechanical “Scotch -taping.'” It is also a way to mass produce 2D materials, as mechanical taping only accesses small amounts, insufficient for industrial applications. However, chemical exfoliation comes with the drawback that it commonly introduces many defects into the 2D sheets. In this talk I will show the challenges of using chemical exfoliation for 2D quantum materials synthesis, to then introduce two systems in which the approach was successful.
I will show that we can use chemical exfoliation to synthesize large qualities of stable and magnetic monolayers of VOCl. Films of these high-quality sheets are shown to possess similar magnetic properties as the bulk crystals. I will also discuss the synthesis of a stable, aqueous ink of superconducting 1T'-WS2 monolayers. Films printed with the ink are superconducting below 7.3 K and show typical behavior of 2D superconductivity. This ink and its dried, printed version, is stable in ambient conditions. It is ideally suited for applications in flexible and printed electronics. Thus, we were able to establish that chemical exfoliation is of use for quantum materials synthesis.

The rapid advancement of Large-scale Text-to-Image models has revolutionized generative tasks by producing high-quality images from textual prompts. However, amid the progress, significant challenges persist, three of which will be the focus of my talk. First, the issue of alignment between the text prompt and the resulting image, particularly evident when multiple subjects are present in the prompt. Second, the challenge of effectively utilizing recent image editing techniques to consistently edit images captured from varying views. Third, the task of distilling the 2D prior knowledge to enhance generation capabilities across diverse domains.

In the first portion I'll give an introduction and motivation for geometric structures on manifolds, and discuss affine structures.  Then I will explain the classification of affine structures on the 2 torus (Nagano and Yagi, Arrowsmith and Furness, Baues, Goldman) and deformations (Baues and Goldman).  Then I will discuss the classification of affine structures on the 3 torus, and progress on the deformation space. Everything is joint work in progress with Max Reistenberg. 

A few years after the deep learning revolution in computer vision it became apparent that despite their amazing performance, DL classifiers are remarkably brittle and can often be fooled by tiny perturbations of the input (e.g. a 1 pixel translation of the image). While these results received much attention, for the most part modern classifiers continue to be trained to optimize accuracy with the hope that robustness will emerge "for free" when training is done on large scale datasets with appropriate data augmentation.

In the first part of my talk, I will present empirical evaluations that show that SOTA image classifiers (e.g. DINOV2) trained on billions of images with data augmentation are still highly brittle. In the second part of the talk, I will present classifiers that we developed using a  more standard, engineering, approach. Rather than hoping that robustness will emerge for free, we try to understand the source of the failure and to create systems that build on the success of DL but are explicitly designed to be  robust. 

Joint work with Hadar Yosef. Amir Hagai and Ofir Shifman.