In the lecture we present a three dimensional mdoel for the simulation of signal processing in neurons. To handle problems of this complexity, new mathematical methods and software tools are required. In recent years, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.

In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine.  To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.

This tutorial establishes and promotes the area of dual system functionality, allowing the radar to house voice and data transmission, leading to technological advances in radar and communications systems. The tutorial develops novel signaling schemes for embedding information into the radar pulsed emissions which, in most cases, is blind to the primary radar operation and radar ambiguity function. It considers different antenna configurations, including multiple-input multiple-output (MIMO) radars and shows how to achieve high data rate communications by combining amplitude-shift keying, phase-shift keying, and code shift keying modulations with waveform-diversity and spatial degrees of freedom.

A traditional goal of algorithmic optimality, squeezing out operations, has been superseded because of evolution in architecture. Arithmetic operations no longer serve as a reasonable proxy for all aspects of complexity. Instead, algorithms must now squeeze memory, data transfers, and synchronizations, while extra operations on locally cached data represent only small costs in time and energy. Hierarchically low-rank matrices realize a rarely achieved combination of optimal storage complexity and high-computational intensity in approximating a wide class of formally dense operators that arise in applications for which exascale computers are being constructed. We describe modules of a KAUST-built software toolkit, Hierarchical Computations on Manycore Architectures (HiCMA), that illustrate these features and are building blocks of KAUST mission applications, such as matrix-free higher-order methods in optimization and large-scale spatial statistics. Early modules of this open-source project have undergone industrial-rigor testing are distributed in the software libraries of major vendors.

The field of bioelectronics combines the worlds of electronics and biology with the aim of developing new tools for biomedical research and healthcare. The majority of implantable devices are mechanically stiff and the mechanical properties mismatch with soft tissue causes an immune response which results in their rejection from the body. Another limitation is associated with the fact that most devices utilize metal electrodes to record from/stimulate tissue. These electrodes offer limited coupling with ion fluxes used by cells to communicate with each other, resulting in low efficiency. Such challenges can be overcome with the integration of soft, conducting polymers displaying mixed (ionic and electronic) conduction. In this talk, I will present approaches that leverage the properties of organic conducting materials in order to develop bioelectronic devices interfacing with the body. These devices include organic electrochemical transistors for measuring metabolites, neural activity and integrity of cellular layers.

 In this tutorial, we review sparse arrays from the coarray perspective that strives for full augment ability, i.e., maximizing the number of spatial autocorrelation lags. In this respect, we discuss sparse array performance for direction finding and also address the passive and active arrays for stationary and moving platforms.  We then contrast these configurations with sparse arrays that achieve MaxSINR for both narrowband and wideband sources operating in an interference-active environment. The tutorial also considers both single point source and multiple point sources. We cover the two important cases where the array aperture size is constrained and unconstrained and demonstrate optimum performance in both cases. For the former, and with a limited aperture, we introduce a hybrid design that seeks a full augmentable array which at the same time optimizes beamformer performance. The problem is formulated as a quadratically constraint quadratic program, with the cost function penalized with weighted l1-norm squared of the beamformer weight vector. The wideband problem is tackled by two different approaches, one includes a delay line filter implementation and the other one is the DFT approach. 

As autonomy in individual robots becomes advanced, one of the next challenges is to coordinate multiple of such intelligent robots, which are then expected to innovatively transform legacy industries (e.g., warehouse automation, connected-vehicle management, etc.). Towards collaboration of multiple robots, this talk will particularly introduce a game-theoretical framework for clustering a large number of multiple robots and assigning the robot teams to given tasks, where the network of the robots is strongly connected and the individuals are asynchronous. The proposed decentralised algorithm guarantees convergence of selfish agents having social inhibition towards a Nash stable partition (i.e., social agreement) within polynomial time.

Approximating the solution to an evolutionary partial differential equation by a set of "moving particles" has several advantages. It validates the use of a continuity equation in an "individuals-based" modeling setting, it provides a link between Lagrangian and Eulerian description, and it defines a "natural" numerical approach to those equations. I will describe recent rigorous results in that context. The main one deals with one-dimensional scalar conservation laws with nonnegative initial data, for which we prove that the a suitably designed "follow-the-leader" particle scheme approximates entropy solutions in the sense of Kruzkov in the many particle limit. Said result represents a new way to solve scalar conservation laws with bounded and integrable initial data. The same method applies to second order traffic flow models, to nonlocal transport equations, and to the Hughes model for pedestrian movements.

Election hacking, power grid cyber-attacks, troll farms, fake news, ransomware, and other terms have entered our daily vocabularies and are here to stay. Cybersecurity touches nearly every part of our daily lives. Most importantly, national security and economic vitality rely on a safe, resilient, and stable cyber-space. We rely on cyber-physical systems with hardware devices, software platforms, and network systems to connect, travel, communicate, power our homes, provide health care, run our economy, etc. However, cyber-threats and attacks have grown exponentially over the past years, exposing both corporate and personal data, disrupting critical operations, causing a public health and safety impact, and imposing high costs on the economy. In this talk, we will focus on cyber-physical energy systems (CPES) as the backbone of critical infrastructure, and provide a research perspective and present red team security threats, challenges, and blue team countermeasures. We will discuss recent approaches on developing low-budget targeted cyberattacks against CPES, designing resilient methods against false data, and the need for an accurate assessment environment achieved through the inclusion of hardware-in-the-loop testbeds.

In this seminar, I will present our work on manipulating magnetization with acoustic waves. The temporally and spatially varying strain in acoustic waves produces a corresponding change in the local anisotropy of magnetostrictive materials through the Villari effect. This magneto-acoustic coupling may be used for patterning magnetic films and for nonlinear signal processing such as amplification and correlation of spin waves. I will discuss our experiments and results towards these application possibilities, and also present the techniques we have developed to characterize magnetostriction. 

In this talk I will discuss statistical models which incorporate temperature response to the radiative forcing components. The models can be used to estimate important climate sensitivity measures and give temperature forecasts. Bayesian inference is obtained using the methodology of integrated nested Laplace approximation and Monte Carlo simulations. The resulting approach will be demonstrated in analyzing instrumental data and Earth system model ensembles.

In the history of humankind, domestication was invented as anthropogenic evolution that fulfills mankind’s critical food demand. The domestication is simply based on mating processes and subsequent selection processes to pick up better hybrid offspring that have advantageous combinations of genomes. Taking advantage of machine learning classifier, we discovered a number of sub-genomic regions that have been incorporated in the rice genomes through production of hybrid offspring during domestication. This so-called “introgression” event is disclosed as an essential key of domestication process. This eventually leads to construction of the AI-aided Smart Breeding Platform to accumulate all the breeding histories of crop species into an Integrated Breeding Knowledgebase.

Abstract

Current genetic diagnosis by next-g

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.

Graduate Seminar Part 2. Fiber-optic distributed acoustic sensor (DAS) and distributed temperature sensor (DTS) are considered important for many applications. It is challenging to design a hybrid DAS-DTS system using the same optical fiber because the operation principles of the two sensors are different. In this talk, we summarize the concept of using the widespread standard multimode fiber (MMF) for simultaneous distributed acoustic and temperature sensing. In particular, we operate the MMF in a quasi-single-mode (QSM) state to simultaneously fulfill the functional requirements of the DAS and DTS. This technique is significant for many industrial applications because it efficiently tackles a long-standing issue in practical implementation. 

Graduate Seminar Part 1. In this talk, catalyst- and mask-free GaN nanowires ensemble were grown by plasma-assisted molecular beam epitaxy (PA-MBE) under nitrogen-rich condition. Prior to optoelectronic device realization, we conducted fundamental studies including diffusion-induced growth mechanisms on various substrates. On bare fused silica substrate, without any buffer layer, hundreds-of-nanometer scale grains of GaN nanowires were examined by SEM and interfaces were investigated by TEM. Despite the poly-crystalline properties of the coalescent columnar GaN layer, each grain showed the preferential orientation along the c-axis growth direction. To provide conductivity and transparency on amorphous fused silica as a thermally durable substrate, transparent conductive oxide (TCO) layers were deposited by RF magnetron sputtering method on a fused silica glass substrate. Next, for the heterogeneous integration toward solar cell application, we introduced n-GaN nanowires as an electron transport layer (ETL) for methylammonium lead iodide (MAPbI3) perovskite solar cells (PSCs). n-GaN nanowires showed high electron mobility and UV blocking characteristics with MAPbI3. Moreover, finite-difference time-domain (FDTD) simulation confirmed that the roughened interfaces of GaN nanowire arrays are helpful for photon recycling. These achievements can open a new pathway for the heterogeneous integration of group-III nitride and perovskite semiconductors and substrate-independent epitaxy.

Secondary quantities such as energy and entropy can be very important for numerical methods. Firstly, preserving these quantities can ensure that non-physical behavior is excluded. Secondly, preserving such quantities can result in stability estimates. Finally, preserving the correct energy/entropy evolution in time can result in additional desirable properties such as lower numerical errors. In this talk, a brief overview of some recent advances concerning energy and entropy preserving numerical methods for ordinary and partial differential equations will be given, together with an outlook on future research directions and applications.

Abstract

The demand for increasingly multidisciplinary reliable simulations, for both analysis and

The amount of collected medical data and computational power are growing rapidly, which creates new opportunities to impact and improve healthcare by advanced analytics and data science. We aim to bring together practitioners and researchers from across the Kingdom to exchange knowledge and explore possible collaborations. To register, please, fill form here.

Robotics is set to play an ever increasingly important role in society, due to its influence in every aspect of life, including medicine and healthcare, manufacturing, services..etc

In this talk we consider the Cauchy problem for the 2D Euler equations for incompressible inviscid fluids. It is well-known that given a smooth initial datum, the Cauchy problem is  well-posed and in particular the energy is conserved and the vorticity is transported by the flow of the velocity. When we consider weak solutions this might not be the case anymore. We will review some recent results obtained in collaboration with Gianluca Crippa and Gennaro Ciampa where we extend those properties to the case of irregular vorticities. In particular, under low integrability assumptions on the vorticity we show that certain approximations important from a physical and a numerical point of view converge to solutions satisfying those properties.

In Visualization, the success or failure of an analysis often depends on the choice of some subtle parameters or design choices. While simple heuristics are often sufficient, in some cases they make the analysis miserably fail. We present three approaches in visualization where a careful choice of optimal parameters results in completely new algorithms: 1) the choice of a reference frame for finding objective vortices in flow visualization, 2) the choice of a scaling of high-dimensional data sets for finding linear projections to 2D in information visualization, and 3) the choice of a feature definition along with numerical extraction methods for visualizing recirculation phenomena in flows.

With the advent of Internet-of-Things (IoT), the conventional radio frequency (RF) communication technology with a congested spectrum of 300 GHz cannot meet the ever-increasing demand for broadband transmission. By exploiting an unlicensed spectrum of ~ 30 PHz, optical wireless communication (OWC) technology is supposed to significantly relieve the load of RF spectrum to support the massive connectivity of IoT devices in the era of fifth-generation networks and beyond. Up to now, continuous breakthroughs in the field of free-space optical communication, visible light communication, and underwater wireless optical communication (UWOC) are laying a solid foundation for the realization of OWC across satellite-air-ground-ocean (SAGO) boundaries, which is expected to considerably accelerate the pace of realizing globally-connected IoT. In this talk, we will briefly introduce the current progress of UWOC research and development toward applications in SAGO OWC.

In this speech, In this speech, Okumura-Hata expressions will be presented for calculating the attenuation of the field strength of mobile communication stations in the megapolis on the example of the capital of Uzbekistan - Tashkent in the frequency bands 900 and 1800 MHz. Expressions allow to take into account factors that affect the signal attenuation in detail. Knowing the levels of the distribution patterns of the field strength in urban environments can correctly determine the number of base stations required to provide high-quality mobile communication. High quality of mobile communications, in turn, creates the best conditions for a quick payback on the development of a mobile network.

​Author of more than 290 journal and conference publications, Professor Stenchikov's research interests are in multi-scale modeling of environmental processes and numerical methods; global climate change, climate downscaling, atmospheric convection; assessment of anthropogenic impacts and geoengineering; air-sea interaction, evaluating environmental consequences of catastrophic events like volcanic eruptions, nuclear explosions, forest and urban fires; and air pollution, transport of aerosols, chemically and optically active atmospheric tracers, their radiative forcing and effect on climate.