לוח שנה משולב

One of Alan Turing's most influential papers is his 1950 Computing machinery and intelligence, in which he introduces the famous "Turing test" for probing the nature of intelligence by evaluating the abilities of machines to behave as humans. In this test, which he calls the "Imitation Game," a (human) referee has to distinguish between two (remote and separate) entities, a human and a computer, only by observing answers to a sequence of arbitrary questions to each entity.
This lecture will exposit, through examples from a surprisingly diverse array of settings, the remarkable power of this basic idea to understand many other concepts. I will discuss variations of the Imitation Game in which we change the nature of the referee, and of the objects to be distinguished, to yield different analogs of the Turing test. These new Imitation Games lead to novel, precise, and operative definitions of classical notions, including secret, knowledge, privacy, randomness, proof, fairness, and others. These definitions have in turn led to numerous results, applications, and understanding.
Some, among many consequences of this fundamental paradigm, are the foundations of cryptography, the surprising discoveries on the power and limits of randomness, the recent influential notion of differential privacy, and breakthrough results on patterns in the prime numbers and navigation in networks. Central to each of these settings are computational and information theoretic limitations placed on the referee in the relevant Imitation Game.
This lecture will survey some of these developments. It assumes no specific background knowledge.

Processes in nature are often long-lasting, but they have a common goal, i.e., to advance structures or constructions. Especially for the composition of materials, it is worth having a closer look and mimic the natural concept for improving the properties of the known materials and in this way opening doors for new application fields.
Among the concepts in nature there is the hybridization of materials, i.e., the blend of organic and inorganic materials with the goal of outperforming both constituting components. The engineering of such hybrid materials can be done in synthetic wet-chemical or in physical ways and often the results, i.e., the properties of the materials, will differ, even if their composition is identical. This may result from different qualities of interactions between the constituting materials.
The quality of interactions can be controlled by the choice of the chemicals and/or the choice of hybridization process. Two recently developed approaches for hybridization base on vapor phase chemistry and are derived from atomic layer deposition (ALD) and result in hybrid thin film growth (molecular layer deposition, MLD) or subsurface hybridization of polymers (vapor phase infiltration, VPI). Both approaches open a plethora of new options for materials design for future applications.
In this talk, some approaches of our group will be discussed that show great promise of vapor phase-grown hybrid films for innovation in technological fields beyond the microelectronics industry. Examples, where mechanical and electronic properties of polymeric materials have been significantly improved through nanoscale coatings and infiltration, will be shown. Furthermore, new concepts towards self-healing of semiconducting thin films, enabled by hybrid materials, will be shown. In most cases, the chemical or physical properties of the initial substrate are altered, typically improved, and new functionalities are added.

A new quantum theory, MRSF-TDDFT (Mixed-Reference Spin-Flip Time-Dependent Density Functional Theory) has been developed*, which introduces the multi-reference advantages within the linear response formalism. The density functional theory (DFT) and linear response (LR) time dependent (TD)-DFT are of utmost importance for routine computations. However, the single reference formulation of DFT is suffering from the description of open-shell singlet systems such as diradicals and bond-breaking. LR-TDDFT, on the other hand, finds difficulties in the modeling of conical intersections, doubly excited states, and core-level excitations. Many of these limitations can be overcome by MRSF-TDDFT, providing an alternative yet accurate route for such challenging situations. Now the theory is combined with NAMD, QM/MM, Spin-Orbit Couplings, and Extended Koopman Theorem. Here, we highlight its performances by presenting our recent results by MRSF-TDDFT especially focusing on nonadiabatic molecular dynamics.

In this talk I will present a framework for designing worst-case to average-case reductions. Focusing on the problem of Matrix Multiplication, I will describe a transformation that takes any weak algorithm that is only correct on a small fraction of the inputs, and converts it into an algorithm that is correct on all inputs, while paying only a small overhead in running time.

The talk is based on joint work with Vahid Asadi, Sasha Golovnev, Tom Gur, and Sathyawageeswar Subramanian.