GC-MS is regarded as a gold standard in analysis of chemical composition in samples. However, due to the complexity of the instrument, a substance's retention time (RT) may not stay fixed across multiple GC-MS chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules, consequently, they are unable to handle the complexity of GC-MS data from human breath. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and show the model has very good true position rates (up to 99% for easy data sets and up to 92% for very complex data sets). We compared our model with the popular correlation optimized warping (COW) and show our model has much better overall performance. This method can easily be adapted to other similar data such as those from liquid chromatography.

The problem of combined state and input estimation of linear structural systems based on measured responses and a priori knowledge of structural model is considered. A novel methodology using Gaussian process latent force models is proposed to tackle the problem in a stochastic setting. Gaussian process latent force models (GPLFMs) are hybrid models that combine differential equations representing a physical system with data-driven non-parametric Gaussian process models. In this work, the unknown input forces acting on a structure are modelled as Gaussian processes with some chosen covariance functions which are combined with the mechanistic differential equation representing the structure to construct a GPLFM. The GPLFM is then conveniently formulated as an augmented stochastic state-space model with additional states representing the latent force components, and the joint input and state inference of the resulting model is implemented using Kalman filter. The augmented state-space model of GPLFM is shown as a generalization of the class of input-augmented state-space models, is proven observable, and is robust compared to conventional augmented formulations in terms of numerical stability. The hyperparameters governing the covariance functions are estimated using maximum likelihood optimization based on the observed data, thus overcoming the need for manual tuning of the hyperparameters by trial-and-error. To assess the performance of the proposed GPLFM method, several cases of state and input estimation are demonstrated using numerical simulations on a 10-dof shear building and a 76-storey ASCE benchmark office tower. Results obtained indicate the superior performance of the proposed approach over conventional Kalman filter based approaches.

Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

Remember the last time someone flipped you the bird? Whether or not that single finger was accompanied by spoken obscenities, you knew exactly what it meant. The conversion from movement into meaning is both seamless and direct, because we are endowed with the capacity to speak without talking and comprehend without hearing. We can direct attention by pointing, enhance narrative by miming, emphasize with rhythmic strokes and convey entire responses with a simple combination of fingers. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. The tendency to supplement communication with motion is universal, though the nuances of delivery vary slightly.

One day in 1992, Lawrence Mattis opened up his mail to find an unsolicited screenplay from two unknown writers. It was a dark, nasty, almost defiantly uncommercial tale of cannibalism and class warfare--the type of story that few execs in Hollywood would want to tell. Yet it was exactly the kind of movie Mattis was looking for. Only a few years earlier, Mattis, in his late twenties, had abandoned a promising legal career to start a talent company, Circle of Confusion, with the aim of discovering new writers to represent. He'd set up shop in New York City, despite being told repeatedly that his best hope for finding talent was to be in Los Angeles. Before that strange script showed up, Mattis was starting to wonder if those naysayers had been right. "I'd only sold a few options that paid about five hundred dollars each," Mattis says. "I was starting to think about going back to law. Then I get this letter from these two kids, saying'Could you please read our script?'" The screenplay, titled Carnivore, was a horror tale set in a soup kitchen, where the bodies of the rich are used to feed the poor. "It was funny, it was visceral, and it made it clear that whoever wrote it really knew movies," Mattis says. Its writers were Lilly and Lana Wachowski, two self-described "schmoes from Chicago" who, in later years, would be referred to by many colleagues and admirers simply as "the Wachowskis." By the time they contacted Mattis, the Wachowskis had been collaborating for years, having spent their childhood creating radio plays, comic books, and their own role-playing game. They'd been raised in a middle-class neighborhood on Chicago's South Side by their mother, a nurse and artist, and their father, a businessman. Growing up, their parents had encouraged them to discover art, especially film.

The UK government is among a group of countries that are attempting to thwart plans to formulate and impose a pre-emptive ban on killer robots. Delegates have been meeting at the UN in Geneva all week to discuss potential restrictions under international law to so-called lethal autonomous weapons systems, which use artificial intelligence to help decide when and who to kill. Most states taking part – and particularly those from the global south – support either a total ban or strict legal regulation governing their development and deployment, a position backed by the UN secretary general, António Guterres, who has described machines empowered to kill as "morally repugnant". But the UK is among a group of states – including Australia, Israel, Russia and the US – speaking forcefully against legal regulation. As discussions operate on a consensus basis, their objections are preventing any progress on regulation.

When you're shopping, you probably have a general look in mind. But how do you describe that to a shopping site? It's implementing a feature that uses computer vision to find items that resemble what you're looking at. Tap the three-dot menu next to a product and it'll give you both simple category buttons (such as "athletic shoes") as well as a "looks like this" option to find visually similar items. Eye a green set of sneakers, for instance, and you should see comparable footwear without having to construct an elaborate search. This AI-guided shopping is available in eBay's Android and iOS apps right now, though it's currently only available in the US, UK, Australia and Germany.

The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) properties of drug candidates are estimated to account for up to 50% of all clinical trial failures. Predicting ADMET properties has therefore been of great interest to the cheminformatics and medicinal chemistry communities in recent decades. Traditional cheminformatics approaches, whether the learner is a random forest or a deep neural network, leverage fixed fingerprint feature representations of molecules. In contrast, in this paper, we learn the features most relevant to each chemical task at hand by representing each molecule explicitly as a graph, where each node is an atom and each edge is a bond. By applying graph convolutions to this explicit molecular representation, we achieve, to our knowledge, unprecedented accuracy in prediction of ADMET properties. By challenging our methodology with rigorous cross-validation procedures and prospective analyses, we show that deep featurization better enables molecular predictors to not only interpolate but also extrapolate to new regions of chemical space.

Off-Policy reinforcement learning (RL) is an important class of methods for many problem domains, such as robotics, where the cost of collecting data is high and on-policy methods are consequently intractable. Standard methods for applying Q-learning to continuous-valued action domains involve iteratively sampling the Q-function to find a good action (e.g. via hill-climbing), or by learning a policy network at the same time as the Q-function (e.g. DDPG). Both approaches make tradeoffs between stability, speed, and accuracy. We propose a novel approach, called Cross-Entropy Guided Policies, or CGP, that draws inspiration from both classes of techniques. CGP aims to combine the stability and performance of iterative sampling policies with the low computational cost of a policy network. Our approach trains the Q-function using iterative sampling with the Cross-Entropy Method (CEM), while training a policy network to imitate CEM's sampling behavior. We demonstrate that our method is more stable to train than state of the art policy network methods, while preserving equivalent inference time compute costs, and achieving competitive total reward on standard benchmarks.

Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner. For instance, when learning how to play a board game, usually one of the first concepts learned is how the game ends, i.e. the actions that lead to a terminal state (win, lose or draw). The advantage of learning end-games first is that once the actions which lead to a terminal state are understood, it becomes possible to incrementally learn the consequences of actions that are further away from a terminal state - we call this an end-game-first curriculum. Currently the state-of-the-art machine learning player for general board games, AlphaZero by Google DeepMind, does not employ a structured training curriculum; instead learning from the entire game at all times. By employing an end-game-first training curriculum to train an AlphaZero inspired player, we empirically show that the rate of learning of an artificial player can be improved during the early stages of training when compared to a player not using a training curriculum.