Integrated Calendar

Generative AI models, including those behind image creation tools, have shown remarkable capabilities in transforming random inputs into coherent outputs. Inspired by these advancements, we've developed Parnassus, a deep-learning model designed for particle physics. Parnassus processes point clouds representing particles interacting with a detector and outputs reconstructed particle data.  Parnassus accurately replicates the particle flow algorithm and generalizes well beyond its training set. This approach exemplifies how techniques from image generation can be adapted to accelerate simulations in high-energy physics.

 Living systems from individual cells to entire tissues adopt diverse curved shapes, appearing on many length scales and commonly driven by active contractile stresses generated in the cell cytoskeleton. Yet, how these forces generate specific 3D forms remains unclear. By recreating the cell cytoskeleton from basic components, with precisely controlled composition and initial geometry, we demonstrate that the spontaneous buildup of stress gradients generated by these molecular motors drive shape deformation. We identify the shape selection rules that determine the final adopted configurations. These are encoded in the initial radius to thickness aspect ratio, likely indicating shaping scalability. These results provide insights on the mechanically induced spontaneous shape transitions in contractile active matter, revealing potential shared mechanisms with living systems across scales.  FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.biosoftweizmann.com/

I will explain the construction of Ramanujan graphs based on the “method of interlacing families” introduced by Adam Marcus, Daniel Spielman and Nikhil Srivastava. This method was also used to prove two theorems known to imply a positive solution to the celebrated Kadison–Singer problem.

In this talk, I will present recent studies in our lab, where new tools are pushing the boundaries of sleep research.In a first line of studies, we are investigating how sleep promotes memory consolidation in humans. To this end, we developed a novel ecological paradigm to study episodic memory without report; upon repeated viewing of special movies ,eye gaze patterns can quantify memory for specific events. Next, we show that deep brain closed-loop intracranial electrical stimulation during human sleep enhances hippocampus-cortex synchrony and memory performance. Finally, we examine how sleep and memory are disrupted in amnestic mild cognitive impairment (aMCI) representing early Alzheimer’s disease (AD). To this end, we developed a novelmachine learning-based method to non-invasively detect interictal spikes occurring in the medial temporal lobe during sleep. This approach can help identify disruptions in hippocampus-cortex synchrony and memory consolidation during sleep.In the second line of studies, we investigate how reduced locus coeruleus-noradrenaline (LC-NE) activity during sleep mediates sensory disconnection. In rodents, the level of ongoing tonic LC activity during sleep anticipates sound-evoked awakenings, while minimal optogenetic LC activation or silencing increases and decreases such awakenings, respectively. Projection-specific investigation in mice indicates that an early surge of brainstem NE is particularly important for mediating sensory-evoked awakenings. These findings may shift how we view arousal- promoting neuromodulation; rather than acting in a diffuse hormonal-like manner, the LC-NE system may in fact operate through specific projection pathways, in a circuit-like manner. Finally, I will describe a novel method for monitoring gaze direction and pupil size through closed eyes in humans via short-wave infrared (SWIR) imaging in combination with video analysis algorithms. This technology has potential to enable touchless and continuous monitoring of depth of anesthesia, pain, and detection of intraoperative awareness.

In recent years, the landscape of artificial intelligence (AI) has been reshaped by the rapid emergence of Foundation Models (FMs). These versatile models have garnered widespread attention for their remarkable ability to transcend the boundaries of traditional, bespoke AI solutions and to generalize to a large set of downstream tasks.   In this presentation we will describe the development of geospatial FMs with earth observation and weather data and discuss the initial results of such models when fine-tuned to various applications including flood detection, CO2 monitoring, nature-based carbon sequestration. We will also show how such foundation models can be a new and exciting tool for assisting and accelerating scientific discovery.

Since the groundbreaking discovery of 51 Pegasi b in 1995, a few thousands extra-solar planets have been identified, marking the beginning of a rapidly evolving new field in Astronomy. This talk will trace the history of exoplanet research and explore how these discoveries have reshaped our understanding of planetary formation and opening new frontiers in the study of planetary systems.