Reinforcement Learning Made Affordable for Hardware Verification Engineers.

Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today's hardware devices must be supported by powerful development and simulation environments, powerful computational mechanisms, and appropriate software to exploit them. Reinforcement learning, a powerful technique belonging to the field of artificial intelligence, provides the means to efficiently exploit computational resources to find even the least obvious correlations between configuration parameters, stimuli applied to digital design inputs, and their functional states. This paper, in which a novel software system is used to simplify the analysis of simulation outputs and the generation of input stimuli through reinforcement learning methods, provides important details about the setup of the proposed method to automate the verification process. By understanding how to properly configure a reinforcement algorithm to fit the specifics of a digital design, verification engineers can more quickly adopt this automated and efficient stimulus generation method (compared with classical verification) to bring the digital design to a desired functional state. The results obtained are most promising, with even 52 times fewer steps needed to reach a target state using reinforcement learning than when constrained random stimulus generation was used.

The Rescue of the Romanian Health System by the Emergency Departments during the Fourth Wave of COVID-19 Pandemic.

The COVID-19 pandemic has led to the confrontation of the health system with the need to identify solutions for providing medical care to a very large number of patients. The main objective of our study was to describe the measures taken to provide optimal medical care to patients who presented themselves in one of the large emergency hospitals of Romania in the fourth wave of the COVID-19 pandemic. Material and Methods: We conducted a retrospective, observational study on a group of 1417 patients. The statistical analysis was performed using R. Results: The average length of stay of patients in the emergency departments was approximately 2.6 h, increasing to up to 15 days in some more severe cases. For rapid antigen tests, the highest positivity rate for SARS-CoV-2 was identified in patients aged >75 years (53%). Among the identified risk factors associated with the need for mechanical ventilation were advanced age (α < 0.001) and lack of vaccination against SARS-CoV-2 (α < 0.001). Discussion and conclusions: A method of saving the Romanian health system in full hospital bed occupancy conditions in the wards proved to be the provision of medical care in emergency departments.

Cost-Efficient Approaches for Fulfillment of Functional Coverage during Verification of Digital Designs.

Digital integrated circuits play an important role in the development of new information technologies and support Industry 4.0 from a hardware point of view. There is great pressure on electronics companies to reduce the time-to-market for product development as much as possible. The most time-consuming stage in hardware development is functional verification. As a result, many industry and academic stakeholders are investing in automating this crucial step in electronics production. The present work aims to automate the functional verification process by means of genetic algorithms that are used for generating the relevant input stimuli for full simulation of digital design behavior. Two important aspects are pursued throughout the current work: the implementation of genetic algorithms must be time-worthy compared to the application of the classical constrained-driven generation and the verification process must be implemented using tools accessible to a wide range of practitioners. It is demonstrated that for complex designs, functional verification powered by the use of genetic algorithms can go beyond the classical method of performing verification, which is based on constrained-random stimulus generation. The currently proposed methods were able to generate several sets of highly performing stimuli compared to the constraint-random stimulus generation method, in a ratio ranging from 57:1 to 205:1. The performance of the proposed approaches is comparable to that of the well-known NSGA-II and SPEA2 algorithms.

Ancient DNA, pig domestication, and the spread of the Neolithic into Europe.

The Neolithic Revolution began 11,000 years ago in the Near East and preceded a westward migration into Europe of distinctive cultural groups and their agricultural economies, including domesticated animals and plants. Despite decades of research, no consensus has emerged about the extent of admixture between the indigenous and exotic populations or the degree to which the appearance of specific components of the "Neolithic cultural package" in Europe reflects truly independent development. Here, through the use of mitochondrial DNA from 323 modern and 221 ancient pig specimens sampled across western Eurasia, we demonstrate that domestic pigs of Near Eastern ancestry were definitely introduced into Europe during the Neolithic (potentially along two separate routes), reaching the Paris Basin by at least the early 4th millennium B.C. Local European wild boar were also domesticated by this time, possibly as a direct consequence of the introduction of Near Eastern domestic pigs. Once domesticated, European pigs rapidly replaced the introduced domestic pigs of Near Eastern origin throughout Europe. Domestic pigs formed a key component of the Neolithic Revolution, and this detailed genetic record of their origins reveals a complex set of interactions and processes during the spread of early farmers into Europe.