Integrated Calendar

Sensory input reaching the brain from bilateral and offset channels is nonetheless perceived as unified. This unity could be explained by simultaneous projections to both hemispheres, or inter-hemispheric information transfer between sensory cortical maps. Odor input, however, is not topographically organized, nor does it project bilaterally, making olfactory perceptual unity enigmatic. Here we report a circuit that interconnects mirror-symmetric isofunctional output cells between the mouse olfactory bulbs. Connected neurons respond to similar odors from ipsi- and contra-nostrils, whereas unconnected neurons do not respond to odors from the contralateral nostril. This circuit enables sharing of odor information across hemispheres in the absence of a cortical topographical organization, suggesting that olfactory glomerular maps are the equivalent of cortical sensory maps found in other senses.

Atomically-resolved imaging of materials has become the mainstay of modern materials science, as enabled by advent of aberration corrected scanning transmission electron microscopy (STEM). However, the wealth of quantitative information contained in the fine details of atomic structure or spectra remains largely unexplored. In this talk, I will present the new opportunities enabled by physics-informed big data and machine learning technologies to extract physical information from static and dynamic STEM images. The deep learning models trained on theoretically simulated images or labeled library data demonstrate extremely high efficiency in extracting atomic coordinates and trajectories, converting massive volumes of statistical and dynamic data into structural descriptors. I further present a method to take advantage of atomic-scale observations of chemical and structural fluctuations and use them to build a generative model (including near-neighbor interactions) that can be used to predict the phase diagram of the system in a finite temperature and composition space. Similar approach is applied to probe the kinetics of solid-state reactions on a single defect level and defect formation in solids via atomic-scale observations. Finally, synergy of deep learning image analytics and real-time feedback further allows harnessing beam-induced atomic and bond dynamics to enable direct atom-by-atom fabrication. Examples of direct atomic motion over mesoscopic distances, engineered doping at selected lattice site, and assembly of multiatomic structures will be demonstrated. These advances position STEM towards transition from purely imaging tool for atomic-scale laboratory of electronic, phonon, and quantum phenomena in atomically-engineered structures.

The talk is devoted to the study of the light-matter interaction in the nonlinear regime using three different cavity quantum electrodynamics (CQED) systems. The matter under study is a Josephson flux qubit in the first experiment [1], a spin ensemble of diphenylpicrylhydrazyl (DPPH) molecules in the second one, and different spin ensembles in a diamond lattice in the third one [3]. In all three experiments the matter under study interact with photons (light) confined in a superconducting microwave resonator (cavity). A variety of nonlinear effects are explored, including super-harmonic resonances, multi-photon resonances, effective cavity heating and cooling and motional narrowing induced by quantum-jumps. The effect of nonlinearity on spin detection sensitivity will be discussed.


1. Eyal Buks, Chunqing Deng, Jean-Luc F.X. Orgazzi, Martin Otto and Adrian Lupascu, Phys. Rev. A 94, 033807 (2016).
2. Hui Wang, Sergei Masis, Roei Levi, Oleg Shtempluk and Eyal Buks, Phys. Rev. A 95, 053853 (2017).
3. Nir Alfasi, Sergei Masis, Roni Winik, Demitry Farfurnik, Oleg Shtempluck, Nir Bar-Gill and Eyal Buks, Phys. Rev. A 97 (2018).