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Non-genetic variability in physical and functional properties of living cells is generated by cell proliferation, which suggests that properties are unreliably inherited between consecutive generations. But what is the extent of unreliability of inheritance? Do cellular properties have short, or long-term memory? Answering these questions enables us to uncover the sources that contribute to generating variability among genetically identical cells. It also allows us to develop an accurate description of how the properties of cells change over time. In this talk I will present our newly developed method that allows measuring and characterizing non-genetic inheritance (or cellular memory) in the model organism E. coli. The method utilizes a novel microfluidic device, coined “sisters machine”, that enables us to track and measure how two sister cells become different from each other over time. Our measurements quantify the contribution of cellular and environmental factors to cell variability, and reveal how non-genetic inheritance contributes to regulating the various cellular properties (e.g. size, growth rate, etc.) in future generations. We find that non-genetic cellular memory is property specific, and can last up to ∼10 generations, but decreases under stress. FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.bio

The input to the Multiway Cut problem is a weighted undirected graph, with nonnegative edge weights, and $k$ designated terminals. The goal is to partition the vertices of the graph into~$k$ parts, each containing exactly one of the terminals, such that the sum of weights of the edges connecting vertices in different parts of the partition is minimized. The problem is APX-hard for $k\ge3$. The currently best-known approximation algorithm for the problem for arbitrary~$k$, obtained by Sharma and Vondr\'ak [STOC 2014] more than a decade ago, has an approximation ratio of 1.2965. We present an algorithm with an improved approximation ratio of 1.2787. Also, for small values of $k \ge 4$ we obtain the first improvements in 25 years over the currently best approximation ratios obtained by Karger, Klein, Stein, Thorup, and Young [STOC 1999]. (For $k=3$ an optimal approximation algorithm is known.)

Our main technical contributions are new insights on rounding the LP relaxation of C{\u{a}}linescu, Karloff, and Rabani [STOC 1998], whose integrality ratio matches Multiway Cut's approximability ratio, assuming the Unique Games Conjecture [Manokaran, Naor, Raghavendra, and Schwartz, STOC 2008]. First, we introduce a generalized form of a rounding scheme suggested by Kleinberg and Tardos [FOCS 1999] and use it to replace the Exponential Clocks rounding scheme used by Buchbinder, Naor, and Schwartz [STOC 2013] and by Sharma and Vondr\'ak. Second, while previous algorithms use a mixture of two, three, or four basic rounding schemes, each from a different family of rounding schemes, our algorithm uses a computationally-discovered mixture of hundreds of basic rounding schemes, each parametrized by a random variable with a distinct probability distribution, including in particular many different rounding schemes from the same family. We give a completely rigorous analysis of our improved algorithms using a combination of analytical techniques and interval arithmetic.

Joint work with Joshua Brakensiek, Neng Huang and Aaron Potechin.

The plant microbiome plays a vital role in host fitness. However, the complexity of plant systems makes it difficult to disentangle the roles of individual bacterial species and their interactions with the host. Here, we developed two screening approaches using Chlamydomonas reinhardtii as a model for investigating bacterial pathogenicity and host immunity. First, we measured the effect of ~120 bacterial strains previously isolated from healthy Arabidopsis thaliana roots in a halo assay. We found nine bacterial strains with inhibitory effect on C. reinhardtii growth, all of which previously demonstrated pathogenicity towards A. thaliana, which suggests conserved mechanisms. Focusing on a green lineage specific pathogenic Burkholderia strain (MF6), we revealed it exerts its antagonistic effect through a contact-dependent secretion system. We, next, employed forward genetics in both Chlamydomonas and MF6 to address the genetic basis of pathogenicity and immunity, further characterized by RNA-seq, proteomics and functional assays.In another strategy, we characterized the genetic basis of immunity in a natural habitat. We inoculated the pooled deletion mutant library in Chlamydomonas in soil samples containing various microbial communities and quantified mutant unique barcodes abundance as a proxy for mutant fitness. A promising subset of genes was identified and provides new insights into the defense strategies and potential symbiotic mechanisms employed by green algae with conservation throughout the green lineage.Altogether, these findings highlight conserved plant/alga–bacteria interactions and establish Chlamydomonas as a fascinating system for unraveling the molecular mechanisms of host–pathogen interactions across the plant superkingdom.

As networks become more programmable, they are increasingly built around flexible software components. While this programmability enables new functionality and faster innovation, it also makes network behavior harder to reason about. In this talk, I will present a research agenda that brings ideas from formal methods to programmable networks. In particular, I will present techniques that leverage programmable-network semantics for concurrency safety, traffic monitoring, and failure recovery. More broadly, this work illustrates how semantic foundations can help bring stronger correctness guarantees to modern networked systems.

Bio
Guy Amir is a Postdoctoral Researcher at Cornell University, conducting research at the intersection of formal methods, networking, and systems. He earned his Ph.D. in 2024 from the Hebrew University of Jerusalem, where he studied AI safety, focusing on formally verifying reactive AI systems and interpreting neural networks. He holds an M.Sc. in Computer Science and a B.Sc. in Computational Biology and Computer Science, both from the Hebrew University. He has received Rothschild, Fulbright, AI-Net, and Charles Clore fellowships, as well as an ICML Spotlight and KLA Award.

Despite the dominance of diffusion and flow-based models in visual and multi-modal generation, they remain paradoxically dependent on external encoders for strong semantic representations, leading to limited generalization and unexpected scaling behavior. This reliance suggests a fundamental limitation: standard local denoising provides little incentive for models to develop global semantic structure. To address this, we introduce Self-Flow, a self-supervised framework that unifies representation learning and generative modeling within a single pipeline. By utilizing Dual-Timestep Scheduling to create an information asymmetry across tokens, we force the model to infer corrupted inputs from cleaner context, naturally driving the emergence of strong representations alongside generative capabilities. Because Self-Flow operates without external models, it generalizes seamlessly across modalities and follows expected scaling laws, offering a path toward world models that are both perceptually grounded and semantically rich. Teaser video in this Link.

Bio:

Dr. Hila Chefer is a Research Scientist at Black Forest Labs and an incoming Assistant Professor (Senior Lecturer) at Tel Aviv University. Her research focuses on architecture development and interpretability for visual foundation models, aiming to both understand and advance their generative capabilities. Hila earned her PhD from Tel Aviv University under the supervision of Prof. Lior Wolf, where she developed the de facto leading methods for Transformer interpretability and the Attend-and-Excite framework for text-to-image control. Her work in video generation includes the development of Lumiere during her time at Google and VideoJAM at Meta AI.