We provide an overview and perform an experimental comparison between the famous iterative reconstruction methods in terms of reconstruction quality in sparse view situations. We then derive the proximal operators for the four best methods. We show the flexibility of our framework by deriving solvers for two noise models: Gaussian and Poisson; and by plugging in three powerful regularizers. We compare our framework to state of the art methods, and show superior quality on both synthetic and real datasets.

Learning Granger causality for general point processes is a very challenging task. In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process. We reveal the relationship between Hawkes process's impact function and its Granger causality graph. Specifically, our model represents impact functions using a series of basis functions and recovers the Granger causality graph via group sparsity of the impact functions' coefficients. We propose an effective learning algorithm combining a maximum likelihood estimator (MLE) with a sparse-group-lasso (SGL) regularizer. Additionally, the flexibility of our model allows to incorporate the clustering structure event types into learning framework. We analyze our learning algorithm and propose an adaptive procedure to select basis functions. Experiments on both synthetic and real-world data show that our method can learn the Granger causality graph and the triggering patterns of the Hawkes processes simultaneously.

A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially valuable in oncology, where molecular and genetic heterogeneity of the cells has a major impact on the response. However, the prediction task is extremely challenging, raising the need for methods that can effectively model and predict drug responses. In this study, we propose a novel formulation of multi-task matrix factorization that allows selective data integration for predicting drug responses. To solve the modeling task, we extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF) method with component-wise multiple kernel learning. In addition, our approach exploits the known pathway information in a novel and biologically meaningful fashion to learn the drug response associations. Our method quantitatively outperforms the state of the art on predicting drug responses in two publicly available cancer data sets as well as on a synthetic data set. In addition, we validated our model predictions with lab experiments using an in-house cancer cell line panel. We finally show the practical applicability of the proposed method by utilizing prior knowledge to infer pathway-drug response associations, opening up the opportunity for elucidating drug action mechanisms. We demonstrate that pathway-response associations can be learned by the proposed model for the well known EGFR and MEK inhibitors.

A University of Surrey professor was'overwhelmed' after being appointed a CBE in the Queen's Birthday honours for his career and work in sociology and engineering. Professor Nigel Gilbert founded the Social and Computer Sciences research group in 1984 which focuses on applying social science to the design of artificial intelligence systems. He established the Centre for Research in Social Simulation in 1997 and it is still based at the Guildford university campus. Prof Gilbert said of being appointed a Commander of the Order of the British Empire: "It was very overwhelming. I received the letter about three weeks ago, a brown envelope from the cabinet office. "At first I thought it was a tax bill.

App developers rely on user demographics to effectively target their audiences – but just how much information do your apps really reveal about you? According to a new study, it might be more than you think. Researchers analysed the app choices of thousands of Android users to determine the predictability of certain attributes, and found that apps can provide insight on your gender, age, and even income. Researchers analysed the app choices of thousands of Android users to determine the predictability of certain attributes, and found that apps can provide insight on your gender, age, and even income. In the paper, published to the journal arXiv, researchers with Verto Analytics in Finland and the Qatar Computing Research Institute created a model based on the demographic attributes and apps of 3,760 Android users.

Synechron, a global consulting and technology innovator in the financial services industry, has released the results of a survey conducted by the TABB Group for Synechron on the potential of blockchain and artificial intelligence ( AI) in financial services, with respondents believing that they will have a major impact over the next 10 years. After conducting the survey with 92 banking and capital markets institutions, the findings found that there is still a perception among decision-makers that working with blockchain and artificial intelligence is in some way circumventing or changing key regulatory requirements. "Many people do not fully understand that the underlying technology is not aimed at changing the rules of compliance and regulation, but will, in fact, allow for more efficient and less risk-averse compliance with regulations," Synechron CEO Faisal Husain told Bitcoin Magazine . "The technologies are optimizing what financial institutions can achieve, not what rules they can circumvent." Husain believes that it is this perception that is the likely result of all the early hype that surrounded the use case for Bitcoin, suggesting that blockchain evangelists need to educate the markets about the realities of peer-to-peer cryptographic technologies.

To learn more about this license, http://creativecommons.org/licenses/b... This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

Synechron, a global consulting and technology innovator in the financial services industry, has released the results of a survey conducted by the TABB Group for Synechron on the potential of blockchain and artificial intelligence (AI) in financial services, with respondents believing that they will have a major impact over the next 10 years. After conducting the survey with 92 banking and capital markets institutions, the findings found that there is still a perception among decision-makers that working with blockchain and artificial intelligence is in some way circumventing or changing key regulatory requirements. "Many people do not fully understand that the underlying technology is not aimed at changing the rules of compliance and regulation, but will, in fact, allow for more efficient and less risk-averse compliance with regulations," Synechron CEO Faisal Husain told Bitcoin Magazine. "The technologies are optimizing what financial institutions can achieve, not what rules they can circumvent." Husain believes that it is this perception that is the likely result of all the early hype that surrounded the use case for Bitcoin, suggesting that blockchain evangelists need to educate the markets about the realities of peer-to-peer cryptographic technologies.

We met up with Régis Vincent, Head of Software at SRI Robotics, a unit of SRI International, which is a non-profit, independent research centre serving government and industry. Located in Menlo Park at the heart of Silicon Valley, SRI International runs projects for government agencies, notably the Defense Advanced Research Projects Agency (DARPA) – the US Ministry of Defence agency which develops technologies for military use – as well as private sector players, both large firms and startups – intended to develop disruptive innovations. The stated aim of SRI International is to move R&D from the laboratory to the marketplace. SRI is hardly a household name, but the organisation has nevertheless been behind a large number of the devices which we now use in our daily lives. Since the research centre was founded 65 years ago, its engineers have been closely involved in the development of such innovations as colour television then colour photographic film in the 1950s, ultrasound for medical diagnostics in the 1980s, computers as we know them today, Arpanet, a 1960s precursor to the Internet, and more recently Siri – the first-ever virtual personal assistant, later acquired by Apple.

Scientists have shown how the brain anticipates all of the new situations that it may encounter in a lifetime by creating a special kind of neural network that is "pre-adapted" to face any eventuality. This emerges from a new neuroscience study published in PLOS Computational Biology. Enel et al at the INSERM in France investigate one of the most noteworthy properties of primate behavior, its diversity and adaptability. Human and non-human primates can learn an astonishing variety of novel behaviors that could not have been directly anticipated by evolution--we now understand that this ability to cope with new situations is due to the "pre-adapted" nature of the primate brain. This study shows that this seemingly miraculous pre-adaptation comes from connections between neurons that form recurrent loops where inputs can rebound and mix in the network, like waves in a pond, thus called "reservoir" computing.