Chemical reactions could be probed in even greater detail using a method invented by Imperial researchers that better characterises ultrafast X-rays. X-rays can be used to investigate the structures of, and reactions between, molecules on very small scales and at high speed. To do this, scientists use free electron lasers (FELs) to create a train of X-ray pulses. This allows researchers to probe some of the fundamental processes in chemistry and biology – such as the mechanisms of photosynthesis and the reactions of amino acids, which are the building blocks of life. However, FELs are inherently unstable, meaning the properties of the resulting X-rays can vary from one pulse to the next.

How many people start every year with a promise to themselves that they are going to eat better, drink less and exercise more? I don't have any fancy stats to hand, but I'll go out on a limb and say it's a lot of people (including myself). Of these people, how many are still on track a few weeks later? Even with all the will power in the world it's really difficult. Unfortunately, there's no magic bullet either, nothing that can convert a pizza-eating wine guzzler into a quinoa loving, spirulina sipping health fanatic.

As computers and technology continue to evolve at breakneck speed, it can be difficult for the average person to understand just how much work goes into getting computers to do things. Getting computers to'see' and to identify images, for instance, is one task that many an engineer, robotics technician and data scientist have attempted to achieve over the years. This technology is only now starting to demonstrate its true potential, as seen in recent tests of self-driving vehicles, but it is far from being perfect. Nevertheless, thanks to machine learning and drastically improved image recognition technologies, industries like manufacturing are about to undergo a significant evolution. Machine Learning refers to one of the ways Artificial Intelligence is used.

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We are in the golden age of machine learning. From Microsoft's new computer vision system outperforming humans to Google's AI algorithm mastering the ancient game of Go, scientists have already achieved what many thought would take years to accomplish. There is growing excitement about what new applications deep learning will enable next. Will we soon rely on computers to keep us safe on our daily commute in self-driving cars? Will we use machines to diagnose us based on our symptoms and medical history?

In this work, we design a machine learning based method, online adaptive primal support vector regression (SVR), to model the implied volatility surface. The algorithm proposed is the first derivation and implementation of an online primal kernel SVR. It features enhancements that allow online adaptive learning by embedding the idea of local fitness and budget maintenance. To accelerate our algorithm, we implement its most computationally intensive parts in a Field Programmable Gate Arrays hardware. Using intraday tick data from the E-mini S&P 500 options market, we show that our algorithm outperforms two competing methods and the Gaussian kernel is a better choice than the linear kernel. Sensitivity analysis is also presented to demonstrate how hyper parameters affect the error rates and the number of support vectors in our models.

It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers. For the Poisson gamma belief network (PGBN), a recently proposed deep discrete LVM, we derive an alternative representation that is referred to as deep latent Dirichlet allocation (DLDA). Exploiting data augmentation and marginalization techniques, we derive a block-diagonal Fisher information matrix and its inverse for the simplex-constrained global model parameters of DLDA. Exploiting that Fisher information matrix with stochastic gradient MCMC, we present topic-layer-adaptive stochastic gradient Riemannian (TLASGR) MCMC that jointly learns simplex-constrained global parameters across all layers and topics, with topic and layer specific learning rates. State-of-the-art results are demonstrated on big data sets.

We study the problem of online learning in a class of Markov decision processes known as linearly solvable MDPs. In the stationary version of this problem, a learner interacts with its environment by directly controlling the state transitions, attempting to balance a fixed state-dependent cost and a certain smooth cost penalizing extreme control inputs. In the current paper, we consider an online setting where the state costs may change arbitrarily between consecutive rounds, and the learner only observes the costs at the end of each respective round. We are interested in constructing algorithms for the learner that guarantee small regret against the best stationary control policy chosen in full knowledge of the cost sequence. Our main result is showing that the smoothness of the control cost enables the simple algorithm of following the leader to achieve a regret of order $\log^2 T$ after $T$ rounds, vastly improving on the best known regret bound of order $T^{3/4}$ for this setting.

Multi-Task Learning (MTL) can enhance a classifier's generalization performance by learning multiple related tasks simultaneously. Conventional MTL works under the offline or batch setting, and suffers from expensive training cost and poor scalability. To address such inefficiency issues, online learning techniques have been applied to solve MTL problems. However, most existing algorithms of online MTL constrain task relatedness into a presumed structure via a single weight matrix, which is a strict restriction that does not always hold in practice. In this paper, we propose a robust online MTL framework that overcomes this restriction by decomposing the weight matrix into two components: the first one captures the low-rank common structure among tasks via a nuclear norm and the second one identifies the personalized patterns of outlier tasks via a group lasso. Theoretical analysis shows the proposed algorithm can achieve a sub-linear regret with respect to the best linear model in hindsight. Even though the above framework achieves good performance, the nuclear norm that simply adds all nonzero singular values together may not be a good low-rank approximation. To improve the results, we use a log-determinant function as a non-convex rank approximation. The gradient scheme is applied to optimize log-determinant function and can obtain a closed-form solution for this refined problem. Experimental results on a number of real-world applications verify the efficacy of our method.

Most of us regard self-driving cars, voice assistants, and other artificially intelligent technologies as revolutionary. For the next generation, however, these wonders will have always existed. AI for them will be more than a tool; in many cases, AI will be their co-worker and a ubiquitous part of their lives. If the next generation is to use AI and big data effectively – if they're to understand their inherent limitations, and build even better platforms and intelligent systems -- we need to prepare them now. That will mean some adjustments in elementary education and some major, long-overdue upgrades in computer science instruction at the secondary level.