Crossmodal processing is one of the characteristics of the human brain which is necessary for understanding the world around us. The meaningful processing of crossmodal information allows us to enhance our perceptual experience [1] also for unisensory stimuli [2], to solve associative incongruence and conflicts [3], and to learn new concepts [4]. Computational models for crossmodal learning have been proposed in the past to enhance tasks such as classification, regression, and prediction. Most of these models propose solutions for crossmodal fusion at an early [5] or late stage [6], [7], e.g., by using crossmodal representations to increase the level of abstraction for a perception task. However, these models typically rely on individual and independent mechanisms for processing unimodal representations where modalities do not influence each other [8], [9].

Modeling the Point Spread Function (PSF) of wide-field surveys is vital for many astrophysical applications and cosmological probes including weak gravitational lensing. The PSF smears the image of any recorded object and therefore needs to be taken into account when inferring properties of galaxies from astronomical images. In the case of cosmic shear, the PSF is one of the dominant sources of systematic errors and must be treated carefully to avoid biases in cosmological parameters. Recently, forward modeling approaches to calibrate shear measurements within the Monte-Carlo Control Loops ($MCCL$) framework have been developed. These methods typically require simulating a large amount of wide-field images, thus, the simulations need to be very fast yet have realistic properties in key features such as the PSF pattern. Hence, such forward modeling approaches require a very flexible PSF model, which is quick to evaluate and whose parameters can be estimated reliably from survey data. We present a PSF model that meets these requirements based on a fast deep-learning method to estimate its free parameters. We demonstrate our approach on publicly available SDSS data. We extract the most important features of the SDSS sample via principal component analysis. Next, we construct our model based on perturbations of a fixed base profile, ensuring that it captures these features. We then train a Convolutional Neural Network to estimate the free parameters of the model from noisy images of the PSF. This allows us to render a model image of each star, which we compare to the SDSS stars to evaluate the performance of our method. We find that our approach is able to accurately reproduce the SDSS PSF at the pixel level, which, due to the speed of both the model evaluation and the parameter estimation, offers good prospects for incorporating our method into the $MCCL$ framework.

Detecting a change point is a crucial task in statistics that has been recently extended to the quantum realm. A source state generator that emits a series of single photons in a default state suffers an alteration at some point and starts to emit photons in a mutated state. The problem consists in identifying the point where the change took place. In this work, we consider a learning agent that applies Bayesian inference on experimental data to solve this problem. This learning machine adjusts the measurement over each photon according to the past experimental results finds the change position in an online fashion. Our results show that the local-detection success probability can be largely improved by using such a machine learning technique. This protocol provides a tool for improvement in many applications where a sequence of identical quantum states is required.

Consider a structured matrix factorization model where one factor is restricted to have its columns lying in the unit simplex. This simplex-structured matrix factorization (SSMF) model and the associated factorization techniques have spurred much interest in research topics over different areas, such as hyperspectral unmixing in remote sensing, topic discovery in machine learning, to name a few. In this paper we develop a new theoretical SSMF framework whose idea is to study a maximum volume ellipsoid inscribed in the convex hull of the data points. This maximum volume inscribed ellipsoid (MVIE) idea has not been attempted in prior literature, and we show a sufficient condition under which the MVIE framework guarantees exact recovery of the factors. The sufficient recovery condition we show for MVIE is much more relaxed than that of separable non-negative matrix factorization (or pure-pixel search); coincidentally it is also identical to that of minimum volume enclosing simplex, which is known to be a powerful SSMF framework for non-separable problem instances. We also show that MVIE can be practically implemented by performing facet enumeration and then by solving a convex optimization problem. The potential of the MVIE framework is illustrated by numerical results.

Google announced today that it's expanding its AI research efforts, setting up a new research team in France that will work with the country's AI research community on issues ranging from health to the environment. Google says the team's work will be published and any code it produces will be open source. Along with creating a dedicated AI team at Google France, the company is also expanding its workforce by 50 percent and opening four hubs that will provide free digital literacy training to the residents of France. While Facebook already has an AI lab in the country, it announced today that it would put €10 million towards accelerating AI innovation in France. That money will be used for scholarships, funding servers and open datasets that public institutions can use and adding 30 additional fellowship positions to Facebook AI Research Paris' PhD program.

Google CEO Sundar Pichai believes artificial intelligence could have "more profound" implications for humanity than electricity or fire, according to recent comments. Pichai also warned that the development of artificial intelligence could pose as much risk as that of fire if its potential is not harnessed correctly. "AI is one of the most important things humanity is working on," Pichai said in an interview with MSNBC and Recode, set to air on Friday, January 26. "It's more profound than, I don't know, electricity or fire." Pichai went on to warn of the potential dangers associated with developing advanced AI, saying that developers need to learn to harness its benefits in the same way humanity did with fire.

THE idea that we are living in a historic, even apocalyptic, age exerts a powerful pull on the human mind. Eschatology – the theology of end times – is a religious concept, but crops up in many other systems of thought. Marxism and neo-liberalism were both driven by an "end-of-history" narrative. Scientific thinking isn't immune either: the technological singularity has been called eschatology for geeks, and the study of existential risk even has its own centre at the University of Cambridge. You don't have to believe in the four horsemen to see the apocalypse coming.

A French university has set up a panel to explore the ethical implications of research into new technologies such as artificial intelligence. Research ethics boards are commonplace for scientists working in biomedicine and psychology. When a study involves humans or animals, a board scrutinises its aims, its proposed research methods and its progress as well as looking at the risks and benefits of the work. But academics at the University of Paris-Saclay want to take this practice a step further and incorporate ethical considerations into work being done in other parts of the university, such as the engineering and computer science departments. Its Research Ethics and Scientific Integrity Council will provide a forum for researchers working on emerging technologies, such as artificial intelligence and the internet of things, to discuss any ethical conundrums their work throws up.

As the global environment has changed dramatically today with geopolitical fissures, technological advances and a shared economy, the World Economic Forum's annual meeting will kick off on Jan. 23 in Davos, Switzerland, with more than 3,000 of the world's influential and wealthy individuals coming from 100 countries. This year's meeting in the snow-capped Alpine town will focus on the theme of "Creating a Shared Future in a Fractured World," which will see discussions on possible solutions to the rifts that have emerged politically, economically and societally. "Creating a shared future in a fractured world requires addressing issues on the global agenda in a holistic, interconnected and future-oriented way," said Klaus Schwab, founder and executive chairman of the WEF. "Our annual meeting in Davos provides an exceptional platform for collaboration to create new global initiatives." One of the highlights of the four-day meeting will be the expected attendance of major political leaders, including British Prime Minister Theresa May, French President Emmanuel Macron and U.S. President Donald Trump.

We would like to invite submissions to the 5th International Conference on Movement and Computing (MOCO'18), Genoa, Italy, June 28-30, 2018. Contributions can be submitted to three different tracks: Papers and Posters, Practice Works, and Doctoral Consortium. MOCO is an interdisciplinary conference that explores how computer science and technology can contribute to a deeper understanding of human movement practice, to support and facilitate movement expression and communication, and to design and develop new paradigms for interacting with computers through movement (e.g., movement interfaces). This requires to tackle computational challenges, including modeling, representation, segmentation, recognition, classification, and generation of movement information. To this aim, an interdisciplinary approach to movement understanding, ranging from biomechanics to embodied cognition, to the phenomenology of bodily experience as well as contributions from the performing arts is needed.