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I am going to give a very basic exposition of Exapnder Graphs defining them spectrally and via the Cheeger constant. I will show how to construct expanders from groups with (T). Contrasting this construction with amenability I will show amenability’s connection to soficity. If time permits, I will give a short introduction to p-adic numbers in the end as reference for talks to come.

For centuries, consciousness was considered to be outside the reach of scientific investigation. Yet in recent decades, more and more studies have tried to probe the neural correlates of conscious experience, and several neuronally-inspired theories for consciousness have emerged. In this talk, I will focus on four leading theories of consciousness: Global Neuronal Workspace (GNW), integrated Information Theory (IIT), Recurrent Processing Theory (RPT) and Higher Order Theory (HOT). I will first shortly present the guiding principles of these theories. Then, I will provide a bird's-eye view of the field, using the results of a large-scale quantitative and analytic review we conducted, examining all studies that either empirically tested these theories or interpreted their findings with respect to at least one of them. Finally, I will describe the first results of the Cogitate consortium - an adversarial collaboration aimed at testing GNW and IIT.

You are all invited to register and attend.
Kindly note the poster* submission deadline – March 18th.

In this talk, I will discuss about two algorithmic problems in the setting of the continuous random energy model. The first part of the talk concerns finding a state in a given energy level, and the second part concerns sampling of the Gibbs measure of this model. I will explain the link between the two problems, and talk about some further directions if time permits.

I will discuss the dynamics of RNA and RNA-binding proteins in 3 subcellular contexts: interactions occurring in the nucleus within nuclear bodies and the connection to splicing; during the export of RNA from the nucleus to the cytoplasm through the nuclear pore complex; and finally in the cytoplasm - the formation of phase separated granules during stress, in particular chemotoxic stress, and the role of RNA in the assembly of these granules.

Uncertainty quantification and visualization is crucial for the deployment of image restoration models in safety-critical domains, like biological and medical imaging. To date, methods for visualizing uncertainty have mainly focused on per-pixel estimates, which provide limited information. Theoretically, more natural visualizations of uncertainty could be obtained from a principal component analysis (PCA) or from some clustering of the posterior distribution. However, such approaches would require generating numerous samples from the posterior distribution as a first step, which is computationally impractical with today’s SOTA (diffusion-based) posterior samplers. In this talk I will present methods that can output a hierarchical clustering (a tree) or the principal components (PCs) of the posterior in a single forward pass of a neural network. Our methods are both more accurate and orders of magnitude faster than the naïve approach of applying clustering or PCA to posterior samples generated by a conditional generative model. I will illustrate the effectiveness of our methods on multiple inverse problems in imaging, including denoising, inpainting, super-resolution, colorization, and biological image-to-image translation.
The talk will cover joint works with Elias Nehme, Omer Yair, Hila Manor and Rotem Mulayoff.

Transmissible RNA has emerged as a means of communication between organisms, both within and across different kingdoms of life. Donor organisms transmit long base-paired RNA, tRNA-fragments, and other small RNAs to elicit RNAi responses in recipient individuals, affecting their gene expression and phenotypes. Honeybees offer a unique opportunity to study RNA transmission since they possess a transmissible RNA pathway through which they share RNAs between individuals and across generations via the secretion and ingestion of worker- and royal jelly. We hypothesised that members of the gut microbiome exploit the same pathway and transmit RNA to their honeybee host.
We show that RNA originating from a gut-restricted bacterium, Snodgrassella alvi (S. alvi), can be detected in worker- and royal jellies. Endogenous S. alvi RNAs are present also in systemic larval tissues in the absence of bacterial genomic DNA, indicating jelly-mediated microbiome RNA uptake and systemic spread within recipient larvae. Characterisation of transmissible S. alvi RNA reveals enrichment of specific rRNA and tRNA fragments in systemic larval tissues. The transmitted RNA fragments could potentially be involved in RNAi and have the capacity to target honeybee pathogens, such as Nosema and viruses. An expanded transmissible RNA pathway and its potential cooperative roles in honeybee- microbiome interactions will be discussed.