CAS drives Increase in R&D productivity with launch of breakthrough Retrosynthesis Planner in SciFinder-n. This unites advanced technology with CAS's unmatched collection of chemical reaction data to increase efficiency of chemical synthesis planning.

As a child of refugees, my parents' narrative is missing huge gaps of information. In our data rich world, archivists are finally piecing together new clues of history using unmanned systems to reopen cold cases. The Nazis were masters in using technology to mechanize killing and erasing all evidence of their crime. Nowhere is this more apparent than in Treblinka, Poland. The death camp exterminated close to 900,000 Jews over a 15-month period before a revolt led to its dismantlement in 1943.

These tasks are modelled on a system of very famous mathematical equations -- partial differential equations (PDE). PDEs are the class of equations which describe everything smooth and continuous in the physical world, and the most common class of simulation problems in science and engineering. Solving computation hungry PDEs takes a toll even on supercomputers. And, we just can't tweak in the hardware (shrink transistors) for reducing the time consumed, a theory complemented by Moore's law. However, there is still a glimmer of hope.

These notes survey and explore an emerging method, which we call the low-degree method, for predicting and understanding statistical-versus-computational tradeoffs in high-dimensional inference problems. In short, the method posits that a certain quantity -- the second moment of the low-degree likelihood ratio -- gives insight into how much computational time is required to solve a given hypothesis testing problem, which can in turn be used to predict the computational hardness of a variety of statistical inference tasks. While this method originated in the study of the sum-of-squares (SoS) hierarchy of convex programs, we present a self-contained introduction that does not require knowledge of SoS. In addition to showing how to carry out predictions using the method, we include a discussion investigating both rigorous and conjectural consequences of these predictions. These notes include some new results, simplified proofs, and refined conjectures. For instance, we point out a formal connection between spectral methods and the low-degree likelihood ratio, and we give a sharp low-degree lower bound against subexponential-time algorithms for tensor PCA.

Sure, world is crying out loud that big-data's biggest problem will be resources. Demand has skyrocketed and everyone in the world is going into tailspin in meeting that demands. Companies are going frantic and overspending to hire data scientists to secure themselves from any upcoming shortfall. This is nothing but a sign that world needs our robot algorithm friends to pacify some demand and increase credibility to new paradigms. Who could forget Steve Balmer's famous quote comparing Big Data as a Machine Learning problem.

The newly organized research project "MELLODDY" (Machine Learning Ledger Orchestration for Drug Discovery), involving ten large pharma companies and seven technology providers, is that kind of deals which can catalyze a transition of the pharmaceutical industry to a new level -- a "paradigm shift", as one might refer to it in terms of Thomas Kuhn's "The Structure of Scientific Revolutions". The project aims at developing a state-of-the-art platform for collaboration, based on Owkin's blockchain architecture technology, which would allow collective training of artificial intelligence (AI) algorithms using data from multiple direct pharmaceutical competitors, without exposing their internal know-hows and compromising their intellectual property -- for the collective benefit of everyone involved. While artificial intelligence (AI) already proved to be a groundbreaking thing in many industries (robotics, finance, surveillance, cyber security, self-driving cars to name just a few), drug discovery still seems like a hard case for machine learning practitioners. A major reason for that is the lack of quality data to train models properly. It might seem surprising, as pharmaceutical research generates enormous amounts of data daily.

A breakthrough discovery shows that pterodactyls could fly from birth, something no other species before or since has been able to do. And British scientists said that the revelation has a'profound impact' on our understanding of the reptiles. The common belief was the pterodactyls, like birds and bats, only took to the air once they were fully grown. A new study shows pterodactyls could fly from birth, something no other species before or since can do. The findings have a'profound impact' on our understanding of reptiles Pterodactyls used both their arms and legs to push themselves off the ground during take-off, in a manoeuvre known as the'quadrupedal launch'. They were almost as tall as a giraffe with wing spans of around 32ft (10 metres).

Differential privacy is the gold standard in data privacy, with applications in the public and private sectors. While differential privacy is a formal mathematical definition from the theoretical computer science literature, it is also understood by statisticians and data experts thanks to its hypothesis testing interpretation. This informally says that one cannot effectively test whether a specific individual has contributed her data by observing the output of a private mechanism---any test cannot have both high significance and high power. In this paper, we show that recently proposed relaxations of differential privacy based on R\'enyi divergence do not enjoy a similar interpretation. Specifically, we introduce the notion of $k$-generatedness for an arbitrary divergence, where the parameter $k$ captures the hypothesis testing complexity of the divergence. We show that the divergence used for differential privacy is 2-generated, and hence it satisfies the hypothesis testing interpretation. In contrast, R\'enyi divergence is only $\infty$-generated, and hence has no hypothesis testing interpretation. We also show sufficient conditions for general divergences to be $k$-generated.

Hypothetical Testing is an application of your statistical model to the questions from the real world. In the hypothetical testing, you first assume the result as an assumption. It is called the null hypothesis. After the assumption, you hold an experiment for testing this hypothesis. Then after based on the results of the experiment.

Hypothesis Testing, as such an important statistical technique applied widely in A/B testing for various business cases, has been relatively confusing to many people at the same time. This article aims to summarize the concept of a few key elements of hypothesis testing as well as how they impact the test results. The story starts from hypothesis. When we want to know any characteristics about a population like the form of distribution, the parameter of interest(mean, variance etc.), we make an assumption about it, which is called the hypothesis of population. Then we pull samples from population, and test whether the sample results make sense given the assumption. For example, your manager somehow knew that the mean of the click-through-rate per user from company's website across the user base is 0.06(mean of CTR of population), while you doubt that and believe the CTR should be higher.