You don't need to have aced physics at school to understand and predict how objects behave in the real world. Now scientists have discovered why we possess this innate ability, by pinpointing the brain's'physics engine'. The'engine' comes alive when we watch physical events unfold – such as a ball being thrown towards us – and is not in the brain's vision centre, but in a set of devoted to planning actions. Scientists have discovered the brain's'physics engine' (coloured in this illustration). The'engine' comes alive when we watch physical events unfold, the scientists said The physics engine is located in several regions of the brain.

Nvidia Corp. NVDA 5.59 % said its second-quarter profit surged as the chip maker exceeded its revenue guidance and saw strong demand for its new products. Nvidia shares rose 2.5% to 61.29 in after-hours trading. Analysts polled by Thomson Reuters projected 1.45 billion. Nvidia, based in Santa Clara, Calif., is a prominent name in graphics processing units used in gaming software. It has expanded in many new sectors ranging from virtual reality to self-driving cars.

A bit of healthy competition is often regarded as a good way of helping people improve their performance. But it seems we may owe our big brains to our tendency to size each other up too. A team of scientists have found that judging other people's standing in a group appears to have played a key role in the evolution of human brain size over the past two million years. Making judgement about other people is a complex task that requires us to made decisions using a range of information. This'sizing up' process (stock picture) can help us decide whether to cooperate with others and played a key role in the evolution of our brains According to the team, the research could also have future implications in developing intelligent and autonomous machines.

Implanting images into someone's mind sounds more like something that would happen in films such as Inception and Total Recall. But scientists have managed to use beams of light to create an image of something completely unknown in a mouse's brain. The findings suggest that the brain may be more easily moulded than previously thought, and could have a major impact on neuroscience and medicine. The thought of artificially implanting an image inside the mind might sound like a technique used by a hypnotist. But scientists have managed to use beams of light to create an image of something completely unknown in a mouse's brain The researchers were able to control and observe the brain of a living mouse using optogenetics.

Computational biologists answer biological and biomedical questions by using computation in support of--or in place of--laboratory procedures, hoping to obtain more accurate answers at a greatly reduced cost. The past two decades have seen unprecedented technological progress with regard to generating biological data; next-generation sequencing, mass spectrometry, microarrays, cryo-electron microscopy, and other high-throughput approaches have led to an explosion of data. However, this explosion is a mixed blessing. On the one hand, the scale and scope of data should allow new insights into genetic and infectious diseases, cancer, basic biology, and even human migration patterns. On the other hand, researchers are generating datasets so massive that it has become difficult to analyze them to discover patterns that give clues to the underlying biological processes. Certainly, computers are getting faster and more economical; the amount of processing available per dollar of computer hardware is more or less doubling every year or two; a similar claim can be made about storage capacity (Figure 1). In 2002, when the first human genome was sequenced, the growth in computing power was still matching the growth rate of genomic data. However, the sequencing technology used for the Human Genome Project--Sanger sequencing--was supplanted around 2004, with the advent of what is now known as next-generation sequencing. The material costs to sequence a genome have plummeted in the past decade, to the point where a whole human genome can be sequenced for less than US 1,000.

Among the innovations pioneered by John White during his years as CEO of ACM was a new relationship with the Klaus Tschira Foundation that sponsors the Heidelberg Laureate Foruma [HLF] in the third quarter of each year. The attendees include about 200 math or computer science students and recipients of the mathematics Fields Medal, the Nevanlinna Prize, the Abel Prize, and ACM's A.M. Turing award for computer science. I have had the pleasure of attending the first three meetings of the HLF. Since 1951, however, there has been an annual meeting of Nobel laureatesb with support from several organizations including the aforementioned Klaus Tschira Foundation. The HLF is patterned after the Nobel meeting: students meet with a collection of participating laureates. It was decided last year to link these two events by having a Nobel laureate address the participants of the HLF and to have an HLF laureate address the participants of the Nobel annual meeting.

The invention of religion is a big bang in human history. Gods and spirits helped explain the unexplainable, and religious belief gave meaning and purpose to people struggling to survive. But what if everything we thought we knew about religion was wrong? What if belief in the supernatural is window dressing on what really matters--elaborate rituals that foster group cohesion, creating personal bonds that people are willing to die for. Anthropologist Harvey Whitehouse thinks too much talk about religion is based on loose conjecture and simplistic explanations. Whitehouse directs the Institute of Cognitive and Evolutionary Anthropology at Oxford University. For years he's been collaborating with scholars around the world to build a massive body of data that grounds the study of religion in science. Whitehouse draws on an array of disciplines--archeology, ethnography, history, evolutionary psychology, cognitive science--to construct a profile of religious practices. Whitehouse's fascination with religion goes back to his own groundbreaking field study of traditional beliefs in Papua New Guinea in the 1980s.

Following the seminal idea of Tukey, data depth is a function that measures how close an arbitrary point of the space is located to an implicitly defined center of a data cloud. Having undergone theoretical and computational developments, it is now employed in numerous applications with classification being the most popular one. The R-package ddalpha is a software directed to fuse experience of the applicant with recent achievements in the area of data depth and depth-based classification. ddalpha provides an implementation for exact and approximate computation of most reasonable and widely applied notions of data depth. These can be further used in the depth-based multivariate and functional classifiers implemented in the package, where the $DD\alpha$-procedure is in the main focus. The package is expandable with user-defined custom depth methods and separators. The implemented functions for depth visualization and the built-in benchmark procedures may also serve to provide insights into the geometry of the data and the quality of pattern recognition.

We consider the problem of estimating the mean and covariance of a distribution from iid samples in $\mathbb{R}^n$, in the presence of an $\eta$ fraction of malicious noise; this is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type. The agnostic problem includes many interesting special cases, e.g., learning the parameters of a single Gaussian (or finding the best-fit Gaussian) when $\eta$ fraction of data is adversarially corrupted, agnostically learning a mixture of Gaussians, agnostic ICA, etc. We present polynomial-time algorithms to estimate the mean and covariance with error guarantees in terms of information-theoretic lower bounds. As a corollary, we also obtain an agnostic algorithm for Singular Value Decomposition.

Many modern statistical applications ask for the estimation of a covariance (or precision) matrix in settings where the number of variables is larger than the number of observations. There exists a broad class of ridge-type estimators that employs regularization to cope with the subsequent singularity of the sample covariance matrix. These estimators depend on a penalty parameter and choosing its value can be hard, in terms of being computationally unfeasible or tenable only for a restricted set of ridge-type estimators. Here we introduce a simple graphical tool, the spectral condition number plot, for informed heuristic penalty parameter selection. The proposed tool is computationally friendly and can be employed for the full class of ridge-type covariance (precision) estimators.