THE HISTORY OF THE POST MORTEM EXAMINATIONS IN HUNGARY

Any death must be determined by a post mortem. One element of this is to determine that death has occurred, a decision which can be made by a doctor and by a paramedic. A further element of the post mortem is to determine the mode and cause of death. In terms of the mode of death, we distinguish between natural and non-natural deaths. The cause of death can either be declared immediately during the post mortem (run over by a train, stabbing injury, firearms injury, body severely damaged) or only after an autopsy has been carried out. It follows from the foregoing that in most cases the post mortem can only reveal the mode of death, i.e. we can distinguish between deaths caused naturally and unnaturally, in which case an official procedure is required to close the case. However, in the case of deaths caused by natural diseases, the necessary steps can be taken without the involvement of the authorities. A post mortem is also important in the sense that we can deduce a possible crime from external injuries, wounds, and damage to the clothing worn by the deceased, so that the authority can be provided with a fresh trail in their attempts to solve the case. At the end of the 19th century in Hungary, a law incorporating a completely new approach to a public health was introduced, which created regulations of a European standard, and at the same time raised the post mortem to a completely new, European level

Design of Oscillatory Neural Networks by Machine Learning

We demonstrate the utility of machine learning algorithms for the design of Oscillatory Neural Networks (ONNs). After constructing a circuit model of the oscillators in a machine-learning-enabled simulator and performing Backpropagation through time (BPTT) for determining the coupling resistances between the ring oscillators, we show the design of associative memories and multi-layered ONN classifiers. The machine-learning-designed ONNs show superior performance compared to other design methods (such as Hebbian learning) and they also enable significant simplifications in the circuit topology. We demonstrate the design of multi-layered ONNs that show superior performance compared to single-layer ones. We argue Machine learning can unlock the true computing potential of ONNs hardware

Design and Performance of the Data Acquisition System for the NA61/SHINE Experiment at CERN

This paper describes the hardware, firmware and software systems used in data acquisition for the NA61/SHINE experiment at the CERN SPS accelerator. Special emphasis is given to the design parameters of the readout electronics for the 40m^3 volume Time Projection Chamber detectors, as these give the largest contribution to event data among all the subdetectors: events consisting of 8bit ADC values from 256 timeslices of 200k electronic channels are to be read out with ~100Hz rate. The data acquisition system is organized in "push-data mode", i.e. local systems transmit data asynchronously. Techniques of solving subevent synchronization are also discussed.Comment: 14 pages, 13 figure

Perspective on Nanoscaled Magnonic Networks

With the rapid development of artificial intelligence in recent years, mankind is facing an unprecedented demand for data processing. Today, almost all data processing is performed using electrons in conventional complementary metal-oxide-semiconductor (CMOS) circuits. Over the past few decades, scientists have been searching for faster and more efficient ways to process data. Now, magnons, the quanta of spin waves, show the potential for higher efficiency and lower energy consumption in solving some specific problems. While magnonics remains predominantly in the realm of academia, significant efforts are being made to explore the scientific and technological challenges of the field. Numerous proof-of-concept prototypes have already been successfully developed and tested in laboratories. In this article, we review the developed magnonic devices and discuss the current challenges in realizing magnonic circuits based on these building blocks. We look at the application of spin waves in neuromorphic networks, stochastic and reservoir computing and discuss the advantages over conventional electronics in these areas. We then introduce a new powerful tool, inverse design magnonics, which has the potential to revolutionize the field by enabling the precise design and optimization of magnonic devices in a short time. Finally, we provide a theoretical prediction of energy consumption and propose benchmarks for universal magnonic circuits.Comment: 9 pages, 1 figur

Experimental Demonstration of a Rowland Spectrometer for Spin Waves

We experimentally demonstrate the operation of a spin-wave Rowland spectrometer. In the proposed device geometry, spin waves are coherently excited on a diffraction grating and form an interference pattern that spatially separates spectral components of the incoming signal. The diffraction grating was created by focused-ion-beam irradiation, which was found to locally eliminate the ferrimagnetic properties of YIG, without removing the material. We found that in our experiments spin waves were created by an indirect mechanism, by exploiting nonlinear resonance between the grating and the coplanar waveguide. Our work paves the way for complex spin-wave optic devices -- chips that replicate the functionality of integrated optical devices on a chip-scale.Comment: 7 pages, 5 figures, presented at Joint European Magnetic Symposia (JEMS) 202

Coupled-Oscillator Associative Memory Array Operation for Pattern Recognition

Operation of the array of coupled oscillators underlying the associative memory function is demonstrated for various interconnection schemes (cross-connect, star phase keying and star frequency keying) and various physical implementation of oscillators (van der Pol, phase-locked loop, spin torque). The speed of synchronization of oscillators and the evolution of the degree of matching is studied as a function of device parameters. The dependence of errors in association on the number of the memorized patterns and the distance between the test and the memorized pattern is determined for Palm, Furber and Hopfield association algorithms