A robust model for time series forecasting is highly important in many domains, including but not limited to financial forecast, air temperature and electricity consumption. To improve forecasting performance, traditional approaches usually require additional feature sets. However, adding more feature sets from different sources of data is not always feasible due to its accessibility limitation. In this paper, we propose a novel self-boosted mechanism in which the original time series is decomposed into multiple time series. These time series played the role of additional features in which the closely related time series group is used to feed into multi-task learning model, and the loosely related group is fed into multi-view learning part to utilize its complementary information. We use three real-world datasets to validate our model and show the superiority of our proposed method over existing state-of-the-art baseline methods.

Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. We propose a backward elimination procedure called SurvNet, which is based on a new measure of variable importance that applies to a wide variety of networks. More importantly, SurvNet is able to estimate and control the false discovery rate of selected variables, while no existing methods provide such a quality control. Further, SurvNet adaptively determines how many variables to eliminate at each step in order to maximize the selection efficiency. To study its validity, SurvNet is applied to image data and gene expression data, as well as various simulation datasets.

In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and person-alisation of Interactive Narratives (INs). INs are partially observable for the players and their goals are dynamic as a result. We used Receding Horizon IRL (RHIRL) to learn players' goals in the form of reward functions, and derive policies to imitate their behaviour. Our preliminary results suggest that RHIRL is able to learn action sequences to complete a game, and provided insights towards generating behaviour more similar to specific players.

Vector is going to start using artificial intelligence to predict where storms will cause power outages. The Auckland lines company has partnered with IBM to pilot the new system, a first for the country, next month. It uses satellite imagery and artificial intelligence to show areas where trees might be encroaching on power lines. It can then suggest the locations most at risk of outages. Vector already uses artificial intelligence to manage electricity demand and network data across its network, since it partnered with Israeli technology company mPrest in 2017.

Where do you guys rent compute resources for training? What are your primary selection criteria (cost/reliability/bandwidth/ data location), for your particular use case? Do you also own your own AI/ML gears for consistent workload, in addition to the cloud? I am asking this as I am building an exchange where people can share quality compute resources at-cost or near-cost. Would this be something that you are interested in?

Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zero-shot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot setting.

But Dr Louise Cullen, an emergency physician at the Royal Brisbane and Women's Hospital and one of the study's authors, said there were arbitrary cut-offs for troponin levels considered to be an indicator of a heart attack. "We see people come to hospital with heart damage and high levels of troponin, some of them are having a heart attack and some have other causes," Dr Cullen said. "There's an arbitrary cut-off point for indicating a heart attack based on a so-called normal population. "The problem is we know the older you get and whether you're male or female makes a difference on what that value should be.

But Dr Louise Cullen, an emergency physician at the Royal Brisbane and Women's Hospital and one of the study's authors, said there were arbitrary cut-offs for troponin levels considered to be an indicator of a heart attack. "We see people come to hospital with heart damage and high levels of troponin, some of them are having a heart attack and some have other causes," Dr Cullen said. "There's an arbitrary cut-off point for indicating a heart attack based on a so-called normal population. "The problem is we know the older you get and whether you're male or female makes a difference on what that value should be.

Sudbury's Centre for Excellence in Mining Innovation (CEMI) has gone international in signing a memorandum of understanding (MOU) with an industry technology centre in Australia. CEMI and METS Ignited of Brisbane signed an agreement to establish a vehicle for each organization to collaborate and accelerate the commercialization of mining innovations in Canada and Australia. "We have boots on the ground in Australia now," said Charles Nyabeze, CEMI's vice-president of business development and commercialization, in a Sept. 13 phone interview. "We will have access to game-changing solutions not only for our Canadian mines, but also for the mines we work with globally." The two organizations intend to cross-promote each other in their respective countries when it comes to mining-related exploration, extraction, transportation; tailings, waste and water management technologies; and digitalization of mine operations using analytics, artificial intelligence, automation and robotics.

McDonald's announced it will McBuy the Bay Area voice-recognition startup Apprente for an undisclosed amount. According to McDonald's, Apprente's "sound-to-meaning" technology handles "complex, multilingual, multi-accent and multi-item conversational ordering," and believes the technology will help streamline the drive-thru process -- even faster food, you say?? As the earth turns and the centuries change, so does the way people wish to order a Big Mac, and Micky D's has the cash to listen. Back in March, the company bought Dynamic Yield, which customizes drive-thru menus based on factors like weather, time of day, and customer order profiles. A month later, it invested in New Zealand app-designer Plexure, which will help connect customers to its new smart drive-thrus, among other things.