Integrated Calendar

The development of high energy density, long running rechargeable batteries like
Li ion batteries, that power so successfully all mobile electronic devices, can be
considered as the greatest success of modern electrochemistry.
However, the basis for this success was the capability of exploring most complex
electrodes, electrolyte solutions and reactive interfaces by most sophisticated
electroanalytical tools in conjunction with advanced spectroscopic and microscopic
was a first-rate leader in electroanalytical ז"ל techniques. Professor Israel Rubinstein
chemistry. I learned a lot from him.
The main theme of this presentation is to examine what is the true horizons for advanced
high energy density batteries that can promote the electro-mobility revolution. The
limiting factor in Li-ion batteries in terms of energy density, cost, potential, durability
and cycling efficiency are the cathode materials used. We will examine most energetic
cathode materials and novel approaches we developed for their stabilization. We
describe in this lecture which electrode materials can be relevant, methodologies
of their stabilization by doping, coating, and affecting electrodes surface chemistry
by the use of active additives. Most important cathode materials are comprising the
5 elements Li,Ni,Co,Mn,O at different stoichiometries that determine voltage and
specific capacities. We will explain how the stoichiometry dictates basic cathodes
properties.1,2 We will discuss the renaissance of Li metal-based rechargeable batteries.3
We have learned how the stabilize Li metal anodes in rechargeable batteries using
reactive electrolyte solutions that induce excellent passivation through controlled
surface reactions. The emphasis is on fluorinated co-solvents that open the door for a
very rich surface chemistry that forms passivating surface films that behave as ideal
solid electrolyte interphase on both anodes and cathodes in advanced secondary Li
batteries. This field provides fascinating examples how systematic basic scientific
work leads to development of most practical devices for energy storage & conversion.

We explore the framework of contract design through a computational perspective. Contract design is a fundamental pillar of microeconomics, addressing the essential question of how to incentivize people to work. The significance of contract design was acknowledged by the Nobel Prize awarded to Hart and Holmström, and it applies to various real-life scenarios, such as determining bonuses for employees, setting commission structures for sales representatives, and designing payment schemes for influencers promoting products.

While contract design has been extensively studied from an economic perspective, this talk will examine it from a computational viewpoint. Specifically, we introduce combinatorial extensions of classic contract design models, where a principal delegates tasks to one or multiple agents. The agents have sets of potential actions they can take to complete the task, and the chosen actions by the agents stochastically determine the success of the task. We analyze the structure and computational aspects of these models, and present algorithms that provide (approximately) optimal guarantees.

Naturalistic experimental paradigms in cognitive neuroscience arose from a pressure to test, in real-world contexts, the validity of models we derive from highly controlled laboratory experiments. In many cases, however, such efforts led to the realization that models (i.e., explanatory principles) developed under particular experimental manipulations fail to capture many aspects of reality (variance) in the real world. Recent advances in artificial neural networks provide an alternative computational framework for modeling cognition in natural contexts. In this talk, I will ask whether the human brain's underlying computations are similar or different from the underlying computations in deep neural networks, focusing on the underlying neural process that supports natural language processing in adults and language development in children. I will provide evidence for some shared computational principles between deep language models and the neural code for natural language processing in the human brain. This indicates that, to some extent, the brain relies on overparameterized optimization methods to comprehend and produce language. At the same time, I will present evidence that the brain differs from deep language models as speakers try to convey new ideas and thoughts. Finally, I will discuss our ongoing attempt to use deep acoustic-to-speech-to-language models to model language acquisition in children. 

Heatwaves have been extensively studied in the past, primarily from the standpoint of heatwave formation.Previous studies have identified air subsidence, diabatic heating, and warm air advection as the primary mechanisms for heat accumulation at the surface. However, less workhas focused on what leads to eatwave termination. A recent study suggests that surface temperature can onlyincrease until convection is triggered, and thus proposed a theoretical upper bound of maximum surface airtemperature, assuming a neutrally buoyant atmosphere and a dry surface. Given that most midlatitude heatwave events involve moist surface conditions, which also support theaccumulation of Convective Available Potential Energy (CAPE), we propose an alternative theory that quantifieschanges in surface moist temperature while correctly accounting for the buildup of CAPE. We show that the lower free tropospheric inversion predicts the maximum intensity ofboth moist heat and moist convection in midlatitudes. Implications for heatwave evolution and projected future changes in extreme moist heat events are discussed.