The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world process well. Intuitively, the better the quality of the event data, the better the quality of the model that is discovered. However, existing process discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We will also use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic process discovery.

With a lot of hype going on with the data science field, most of us jump directly into machine learning models and algorithms to make business decisions. All the online courses available fail to teach the very basics of decision making. Hypotheses testing is one of the basic building blocks of decision making and oldest. The earliest use of hypotheses testing was in the 1700s by John Arbuthnot to test whether male and female births are equally likely to occur. In this article, we will be discussing everything about hypotheses testing at the beginner level along with python code making use of the SciPy package.

There has been an upsurge of interest in applying machine-learning (ML) techniques to chemistry, and a number of these applications have achieved impressive predictive accuracies; however, they have done so without providing any insight into what has been learnt from the training data. The interpretation of ML systems (i.e., a statement of what an ML system has learnt from data) is still in its infancy, but interpretation can lead to scientific discovery, and examples of this are given in the areas of drug discovery and quantum chemistry. It is proposed that a research programme be designed that systematically compares the various model-agnostic and model-specific approaches to interpretable ML within a range of chemical scenarios.

Industry partners are often rivals, but not in the current coronavirus vaccine endeavour. Every member of the Consortium is united by a common goal: to accelerate our search for a new treatment or vaccine against COVID-19. The benefits of collaboration are greater speed and accuracy; a freer exchange of ideas and data; and full access to cutting-edge technology. In sum, it supercharges innovation and hopefully means the pandemic will be halted faster than otherwise.

Research across global academic and commercial labs to create a more efficient drug discovery process won recognition today with a special Gordon Bell Prize for work fighting COVID-19. A team of 27 researchers led by Rommie Amaro at the University of California at San Diego (UCSD) combined high performance computing (HPC) and AI to provide the clearest view to date of the coronavirus, winning the award. Their work began in late March when Amaro lit up Twitter with a picture of part of a simulated SARS-CoV-2 virus that looked like an upside-down Christmas tree. Seeing it, one remote researcher noticed how a protein seemed to reach like a crooked finger from behind a protective shield to touch a healthy human cell. "I said, 'holy crap, that's crazy'… only through sharing a simulation like this with the community could you see for the first time how the virus can only strike when it's in an open position," said Amaro, who leads a team of biochemists and computer experts at UCSD.

We propose a simple robust hypothesis test that has the same sample complexity as that of the optimal Neyman-Pearson test up to constants, but robust to distribution perturbations under Hellinger distance. We discuss the applicability of such a robust test for estimating distributions in Hellinger distance. We empirically demonstrate the power of the test on canonical distributions.

The coronavirus disease 2019 (COVID-19) pandemic has underscored the critical need to better understand the human immune system and how to unleash its power to develop vaccines and therapeutics. Much of our knowledge of the immune system has accrued from studies in mice, yet vaccines and drugs that work effectively in mice do not always translate into humans. Pulendran and Davis review recent technological advances that have facilitated the study of the immune system in humans. They discuss new insights and how these can affect the development of drugs and vaccines in the modern era. Science , this issue p. [eaay4014][1] ### BACKGROUND The mammalian immune system is a remarkable sensory system for the detection and neutralization of pathogens. History is replete with the devastating effects of plagues, and the coronavirus disease 2019 (COVID-19) pandemic is a defining global health crisis of our time. Although the development of effective vaccines has saved many lives, the basic workings of the immune system are complex and require the development of animal models, such as inbred mice. Indeed, research in mice has been enormously productive, and the tremendous insights gleaned have resulted in many Nobel prizes and other accolades. However, past results are not necessarily a reliable guide to the future, and a notable limitation of animal models has been their failure to accurately model some human diseases and their inability to predict human immune responses in many cases. With regard to inbred mice, which have been the principal model of choice for immunology, this is likely due to the compromises that were necessary to create a more tractable and reproducible system for experimentation, such as genetic uniformity and lack of pathogen exposure, as well as the fact that mice are evolutionarily quite distinct. These considerations suggest that direct studies of the human immune system are likely to be extremely rewarding, both from a scientific and a medical perspective. ### ADVANCES In the past decade there has been an explosion of new approaches and technologies to explore the human immune system with unprecedented precision. Insights into the human immune response to vaccination, cancers, and viral infections such as COVID-19 have come from high-throughput “omics” technologies that measure the behavior of genes, mRNA (single-cell transcriptomics), proteins (proteomics), metabolites (metabolomics), cells (mass cytometry), and epigenetic modifications (ATAC-seq), coupled with computational approaches. ### OUTLOOK Sydney Brenner remarked in 2008, “We don’t have to look for a model organism anymore. Because we are the model organisms.” We propose that studying the immune system in humans, who are genetically diverse and afflicted by a multitude of diseases, offers both a direct link to medicine (i.e., “translation”) and the very real prospect of discovering fundamentally new human biology. New approaches and technology are now making this area much more approachable, but profiling immunity in humans is but the first step. Computational mining of the data and biological validation in animal models or human organoids are essential next steps, in an iterative cycle that seeks to bridge fundamental and applied science, as well as mouse and human immunology, in a seamless continuum of scientific discovery and translational medicine. This will represent a new paradigm for accelerating the development of vaccines and therapeutics. ![Figure][2] Probing the human immune response to viral infections. Systems biology techniques can be used to probe the human immune response to viral infections and can define molecular signatures that predict disease severity and illuminate the underlying mechanisms of disease. ILLUSTRATION: KELLIE HOLOSKI/ SCIENCE Although the development of effective vaccines has saved countless lives from infectious diseases, the basic workings of the human immune system are complex and have required the development of animal models, such as inbred mice, to define mechanisms of immunity. More recently, new strategies and technologies have been developed to directly explore the human immune system with unprecedented precision. We discuss how these approaches are advancing our mechanistic understanding of human immunology and are facilitating the development of vaccines and therapeutics for infection, autoimmune diseases, and cancer. [1]: /lookup/doi/10.1126/science.aay4014 [2]: pending:yes

Quantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. However, these quantum algorithms, like their classical counterparts, have been shown to also be vulnerable to input perturbations, in particular for classification problems. These can arise either from noisy implementations or, as a worst-case type of noise, adversarial attacks. These attacks can undermine both the reliability and security of quantum classification algorithms. In order to develop defence mechanisms and to better understand the reliability of these algorithms, it is crucial to understand their robustness properties in presence of both natural noise sources and adversarial manipulation. From the observation that, unlike in the classical setting, measurements involved in quantum classification algorithms are naturally probabilistic, we uncover and formalize a fundamental link between binary quantum hypothesis testing (QHT) and provably robust quantum classification. Then from the optimality of QHT, we prove a robustness condition, which is tight under modest assumptions, and enables us to develop a protocol to certify robustness. Since this robustness condition is a guarantee against the worst-case noise scenarios, our result naturally extends to scenarios in which the noise source is known. Thus we also provide a framework to study the reliability of quantum classification protocols under more general settings.

"We've observed that nurses who are supported by artificial-intelligence tools … Machine–learning algorithms and AI can be trained to read "cues" …

"I did not have lines in the resume that showed my ML expertise. I did not have a Data Science industry experience or relevant papers. For this week's ML practitioner's series, Analytics India Magazine got in touch with Vladimir Iglovikov, an ex-Spetsnaz, theoretical physicist and also a Kaggle GrandMaster. In this exclusive interview, he shares valuable information from his journey in the world of data science. After a brief stint in Russian special forces, Iglovikov enrolled for the Master's programme in theoretical Physics at the St.Petersburg State University whose distinguished alumni include President Vladimir Putin. In September 2010, Iglovikov moved to California to pursue a PhD in Physics from UC Davis and on completion of the degree, he moved to Silicon Valley in the summer of 2015. Currently, Iglovikov works as Sr. Software Engineer at Lyft, a ride-sharing company that operates in the United States and Canada. His work is centered around building robust machine learning models for autonomous vehicles at Lyft, Level5. Post PhD, Iglovikov had two options in hand. One was to pursue postdoc, and the other was to get into the industry as a software engineer. His career took a new turn when one of his friends introduced him to the world of data science. "I attended a lecture where the presenter talked about Data Science as the 4th paradigm of scientific discovery.