Integrated Calendar

Cancer is a genetic disease that results from accumulation of unfavorable mutations. As soon as genetic and epigenetic modifications associated with these mutations become strong enough, the uncontrolled tumor cell growth is initiated, eventually spreading through healthy tissues. Clarifying the dynamics of initiation is critically important for understanding the mechanisms of cancer. Here we present a new theoretical approach, stimulated by analogy with chemical reactions and other stochastic processes in physics and biology, to evaluate the dynamic processes associated with cancer initiation. It is based on a discrete-state stochastic description of the formation of tumors as a fixation of unfavorable mutations. Thus, the main idea is to map complex processes of cancer initiation into a network of stochastic transitions between specific states of the tissue. Using a first-passage analysis, the probabilities for cancer to appear and the average times before this happens are explicitly calculated. The method is applied for estimating the initiation times from clinical data for 28 different types of cancer. It is found, surprisingly, that the higher probability of cancer to occur does not necessarily lead to the fast starting the cancer. This suggests that both lifetime risks and cancer initiation times must be used to evaluate the possibility of appearance of the cancer tumor.  The similarity of the mechanisms of cancer initiation processes with dynamics of chemical reactions are discussed.  Furthermore, it is shown that the order of mutations might lead to different cancer initiation dynamics, explaining surprising experimental observations that order of mutations can affect the cancer outcome. Our view of cancer initiation as a motion in the effective free-energy landscape provides new insights into the mechanisms of these complex processes. FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.biosoftweizmann.com/    

Probabilistically checkable proofs (PCPs) allow encoding a computation so that it can be quickly verified by only reading a few symbols. Inspired by tree codes (Schulman, STOC'93), we propose tree PCPs; these are PCPs that evolve as the computation progresses so that a proof for time t is obtained by appending a short string to the end of the tree PCP proof for time t-1. At any given time step t, a verifier can make a small number of queries to the entire tree PCP string (constructed thus far) to verify the correctness of the entire computation.

 

We construct tree PCPs for non-deterministic space-s computation, where at time step t, the proof only grows by an additional poly(s,log(t)) bits, and the number of queries made by the verifier to the overall proof is poly(s)*t^epsilon, for an arbitrary constant epsilon > 0.
 

Tree PCPs are well-suited to proving correctness of ongoing computation that unfolds over time.  They may be thought of as an information-theoretic analog of the cryptographic notion of incrementally verifiable computation (Valiant, TCC'08). We show that, in the random oracle model, tree PCPs can be compiled to realize a variant of incrementally verifiable computation where the prover is allowed a small number of queries to a large evolving state. This yields the first construction of (a natural variant of) IVC in the random oracle model.

This is a joint work with Alon Rosen and Ron D. Rothblum.

Infants gradually learn to recognize and categorize objects, a process that is influenced by language. This talk explores how caregivers' naming of objects, even if inconsistent and unclear, can enhance a child's visual understanding. Using a computer model and a synthetic set of images seen by a toddler-like agent during play, we study how matching images and words over time improves category recognition. Our findings show that small changes in how often objects are named can significantly affect learning, highlighting the importance of aligning visual and language inputs. We also discuss how humans learn relationships between objects. Using a bio-inspired neural network model, we simulate visual experiences to see how objects are grouped based on context, like kitchen or bedroom scenes. Our results reveal that higher network layers group objects by context, while lower layers focus on object identity. This dual approach of matching visuals with words and timing helps explain how we develop semantic knowledge. Overall, this talk suggests computational models to explore the role of language and context in shaping visual and semantic learning in early development.

Bio:
Gemma is a full professor (W3) at the Computer Science Department in Goethe University Frankfurt. She is also a hessian.AI member and affiliated at the Center for Brains Minds and Machines at MIT. Before, she was assistant prof. at Singapore University of Technology and Design. Previously, she was a postdoc fellow at MIT in the Center for Brains Minds and Machines with Prof. Tomaso Poggio. She was also affiliated at the Laboratory for Computational and Statistical Learning. She pursued her doctoral degree in Computer Vision at ETH Zurich.

Western boundary currents (WBCs)—such as the Gulf Stream and Kuroshio—are prominent features of the wind-driven surface ocean circulation. Their structure and dynamics have traditionally been explained by the seminal models of Stommel (1948) and Munk (1950), which emphasize the roles of wind-stress curl, friction, and the planetary vorticity gradient (β-effect). However, these classical theories largely overlook the influence of basin geometry. In this talk, we revisit the Stommel–Munk framework through a non-dimensional approach that isolates two key parameters: frictional damping and the domain aspect ratio, defined as the meridional-to-zonal extent of the ocean basin. Analytical solutions and numerical simulations show that WBC transport increases strongly with the aspect ratio—cubic in Stommel’s model and linear in Munk’s. This geometric dependence helps explain why the East Australian Current is weaker than other WBCs. Extending these insights to paleoclimate, we demonstrate that tectonic changes during the Cretaceous modified basin shapes, weakening gyre circulation and thereby reducing poleward oceanic heat transport. This reduction likely contributed to the larger meridional sea surface temperature gradients observed during that period. Our findings underscore the fundamental role of basin geometry in shaping both modern and ancient ocean circulation.

Event highlighting the innovative research supported by this year’s AI Institute Acceleration Grants. The colloquium will feature presentations from all 10 grant recipients- showing their projects focused on using AI to tackle scientific questions. This is a great opportunity to learn about cutting-edge work, hear about new ideas, and connect with fellow scientists across disciplines.