By 2020, a quarter of Europeans will be over 60. In their silver age, many would like to stay in their homes and will require care from family or social workers. Unfortunately, the number of caregivers is diminishing year-after-year due to shifting demographics and an increase in working families. This leads to a'care deficit' that poses a major challenge to most European societies. And today's social workers are often hard pressed, wishing they had more time to connect with the people they care for, rather than the minuted dance of tasks that need to be done.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

We derive generalized estimators for a number of spatial statistics that have been used in the analysis of spatially resolved omics data, such as Ripley's K, H and L functions, clustering index, and degree of clustering, which allow these statistics to be calculated on data modelled by arbitrary random measures (RMs). Our estimators generalize those typically used to calculate these statistics on point process data, allowing them to be calculated on RMs which assign continuous values to spatial regions, for instance to model protein intensity. The clustering index (H*) compares Ripley's H function calculated empirically to its distribution under complete spatial randomness (CSR), leading us to consider CSR null hypotheses for RMs which are not point-processes when generalizing this statistic. We thus consider restricted classes of completely random measures which can be simulated directly (Gamma processes and Marked Poisson Processes), as well as the general class of all CSR RMs, for which we derive an exact permutation-based H* estimator. We establish several properties of the estimators, including bounds on the accuracy of our general Ripley K estimator, its relationship to a previous estimator for the cross-correlation measure, and the relationship of our generalized H* estimator to previous statistics. To test the ability of our approach to identify spatial patterning, we use Fluorescent In Situ Hybridization (FISH) and Immunofluorescence (IF) data to probe for mRNA and protein subcellular localization patterns respectively in polarizing mouse fibroblasts on micropattened cells. We observe correlated patterns of clustering over time for corresponding mRNAs and proteins, suggesting a deterministic effect of mRNA localization on protein localization for several pairs tested, including one case in which spatial patterning at the mRNA level has not been previously demonstrated.

We propose a novel method to determine the dissimilarity between subjects for functional data clustering. Spline smoothing or interpolation is common to deal with data of such type. Instead of estimating the best-representing curve for each subject as fixed during clustering, we measure the dissimilarity between subjects based on varying curve estimates with commutation of smoothing parameters pair-by-pair (of subjects). The intuitions are that smoothing parameters of smoothing splines reflect inverse signal-to-noise ratios and that applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. The effectiveness of our proposal is shown through simulations comparing to other dissimilarity measures. It also has several pragmatic advantages. First, missing values or irregular time points can be handled directly, thanks to the nature of smoothing splines. Second, conventional clustering method based on dissimilarity can be employed straightforward, and the dissimilarity also serves as a useful tool for outlier detection. Third, the implementation is almost handy since subroutines for smoothing splines and numerical integration are widely available. Fourth, the computational complexity does not increase and is parallel with that in calculating Euclidean distance between curves estimated by smoothing splines.

Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated expression of modules (and module groups) of functionally related genes across the genome. Coregulation can also arise through the physical association of multi-gene complexes through chromosomal looping, which are then transcribed together. We present a general formalism for modeling coregulation rules in the framework of Random Boolean Networks (RBN), and develop specific models for transcription factor networks with modular structure (including module groups, and multi-input modules (MIM) with autoregulation) and multi-gene complexes (including hierarchical differentiation between multi-gene complex members). We develop a mean-field approach to analyse the stability of large networks incorporating coregulation, and show that autoregulated MIM and hierarchical gene-complex models can achieve greater stability than networks without coregulation whose rules have matching activation frequency. We provide further analysis of the stability of small networks of both kinds through simulations. We also characterize several general properties of the transients and attractors in the hierarchical coregulation model, and show using simulations that the steady-state distribution factorizes hierarchically as a Bayesian network in a Markov Jump Process analogue of the RBN model.

Darktrace, the leader in Enterprise Immune System technology, presented a radical vision of cyber defense at InfoSec World 2016, Orlando, yesterday, where'immune system'-inspired technology can automatically find and respond to evolving cyber-threats. IT Security Architect at Steelcase, Stuart Berman, joined Sean O'Connor, Director at Darktrace on the conference stage as a guest speaker, to discuss how enterprises can tackle the cyber security challenges of tomorrow. As one of the world's leading manufacturers of corporate office environments, Steelcase is known for embracing new technology and innovation, and was quick to recognize the importance of adopting new models of security. Speaking at the InfoSec World Conference in Florida yesterday, Stuart Berman, who has over 20 years' experience in information security, shared his views on the future of cyber defense. "Math and machine learning are an important part of advanced threat defense, in the context of today's fast-moving, distributed work environments," Berman commented.

About these poems: Self Published Amazon Poems By Marco Essomba (@marcoessomba) a Network and Security Expert, Self Confessed Geek, CTO/Cofounder of AMPS Intl, a leading UK based Application Delivery Infrastructures (ADI) Solutions Provider with niche expertise in the world's most advanced ADCs (www.amps-global.com). This poem is part of a collection of encrypted poems for which only the author has the keys. But for those who are brave, a long journey to attempt to decrypt the new digital transformation and information technology related topics based on the author real life experience in the network and security field and career spanning more than 10 years. In the spirit of open source poetry, all feedback (including bad ones) will be much appreciated. Keep reading those poems, for you may learn something about life on earth.

The Internet of Things (IoT), Artificial Intelligence (AI) and cloud computing are three technologies that are converging to disrupt nearly every industry. IoT refers to a connected network of objects embedded with technology that enables the collection and exchange of data. Cloud computing is the storing and retrieval of data, and accessing application programs via the Internet. Artificial Intelligence is the simulation of human intelligence by machines. We are currently in the midst of the rise of the first wave of this technological convergence.

Microsoft has trained its artificial intelligence software to replicate and produce original paintings based upon the works of Rembrandt van Rijn in the hope to refine its deep learning and facial recognition programmes. In actual fact, the above image is an entirely original tribute to the master's works, created by a machine. The battle between humans and AI has been reignited after Google reaped the benefits of the excessive man hours applied to its AlphaGo software in March, which saw the computer program defeat Go champion Lee Se-dol 4-1 repeatedly in the Chinese board game, embarrassing one of humanity's finest players. Now Microsoft is now flouting its AI abilities, having tasked it with studying classic paintings to produce a brand new Rembrandt painting. ING, Microsoft, Delft University of Technology, The Mauritshuis and Museum Het Rembrandthuis all pitched in to birth the aptly named'The Next Rembrandt' project.

SpaceX has finally managed to safely land its re-usable rocket onto a barge, after previous repeated attempts saw the Falcon 9 kit explode. Successfully landing the booster onto the large "drone ship" is a huge step forward for SpaceX and its found Elon Musk, and for private space travel more generally. The company hopes that the re-usable rockets will make space travel much cheaper in future, since they can be re-filled and then sent back into space rather than re-building from scratch. Mr Musk celebrated the successful landing by referencing the T-Pain song "I'm On A Boat". He later deleted the tweet.