In this article, we propose a novel ensemble technique with a multi-scheme weighting based on a technique called coopetitive soft gating. This technique combines both, ensemble member competition and cooperation, in order to maximize the overall forecasting accuracy of the ensemble. The proposed algorithm combines the ideas of multiple ensemble paradigms (power forecasting model ensemble, weather forecasting model ensemble, and lagged ensemble) in a hierarchical structure. The technique is designed to be used in a flexible manner on single and multiple weather forecasting models, and for a variety of lead times. We compare the technique to other power forecasting models and ensemble techniques with a flexible number of weather forecasting models, which can have the same, or varying forecasting horizons. It is shown that the model is able to outperform those models on a number of publicly available data sets. The article closes with a discussion of properties of the proposed model which are relevant in its application. Keywords: Ensemble techniques, Power forecasting, Multi model ensembles, Combining forecasts, Model selection, Time series, Data mining 1. Introduction During the past decade, there has been a tremendous growth of the installed capacity of various forms of renewable energy generation. Wind turbines and photovoltaic powerplants contribute substantially to the new mix of energy, which consists of both nonrenewable and renewable energy power plants. Most renewable energy sources have intermittent generation characteristics, i.e., the amount of generated power highly depends on the weather situation and it cannot be regulated the way it is possible with traditional power plants. In order to guarantee grid stability, the power generation and load in the grid have to be balanced, as the intermediate storage of electrical energy is both inefficient and expensive. Intelligent Embedded Systems Homepage: http://www. Depending on the forecasting horizon, the forecast is of interest to different actors in the field, e.g., network operators, power plant operators, or electricity traders. Having an accurate power forecast, the technical and financial risks for all market participants can be reduced. The power forecasting process typically takes place in two steps: 1. A meteorological forecast for the desired area (the location of the renewable energy power plant) is computed. This forecast is called numerical weather prediction (NWP). In this article, we focus on the second step of the forecasting process, i.e., we assume the NWP as given.

We study machine learning-based assistants that support coordination between humans in congested facilities via congestion forecasts. In our theoretical analysis, we use game theory to study how an assistant's forecast that influences the outcome relates to Nash equilibria, and how they can be reached quickly in congestion game-like settings. Using information theory, we investigate approximations to given social choice functions under privacy constraints w.r.t. assistants. And we study dynamics and training for a specific exponential smoothing-based assistant via a linear dynamical systems and causal analysis. We report experiments conducted on a real congested cafeteria with about 400 daily customers where we evaluate this assistant and prediction baselines to gain further insight.

We propose a novel class of network models for temporal dyadic interaction data. Our goal is to capture a number of important features often observed in social interactions: sparsity, degree heterogeneity, community structure and reciprocity. We propose a family of models based on self-exciting Hawkes point processes in which events depend on the history of the process. The key component is the conditional intensity function of the Hawkes Process, which captures the fact that interactions may arise as a response to past interactions (reciprocity), or due to shared interests between individuals (community structure). In order to capture the sparsity and degree heterogeneity, the base (non time dependent) part of the intensity function builds on compound random measures following Todeschini et al. (2016). We conduct experiments on a variety of real-world temporal interaction data and show that the proposed model outperforms many competing approaches for link prediction, and leads to interpretable parameters.

In Arabic, Indian or Eastern cultures where people can't really have an open dating profile, the traditional Tinder/Happn/Bumble/Badoo etc dating apps don't work too well as they are based on images. It turns out chat bots may be the way forward in these markets if a recent VC investment is anything to go by. NearGroup, a chatbot allowing users to connect with like-minded people based on proximity, personality and their creative content has raised a $1.6m (£1.2m) seed round led by OpenOcean, with participation from Neotribe and BoostVC. NearGroup will use the funding to expand in Asia Pacific, the US and Europe. The NearGroup chatbot (open in for Messenger) allows users to read the'stories' of individuals nearby, and reply to people they want to start a conversation with.

I love to travel, but I know how prohibitively expensive it can be. Thankfully, the internet has given us myriad ways to make it more affordable: Skyscanner, HotelTonight, Scott's Cheap Flights… even Groupon. Now we can add BoltFare to that list. Boltfare is a pretty nifty Facebook Chatbot that can find you ultra-cheap fares without you having to do anything. Just tell it where your home airport is, and where you want to go. Within one hour of me first using it, BoltFare had found me an itinerary that cost just £309 (roughly $383), and gave me a 3 nights in Amsterdam, followed by ten nights in Panama City, and finally two nights in Paris.

According to the government, a quarter of all telecoms satellites are "substantially built" in the UK. It hopes that with local launch capabilities, Britain can become a "one-stop shop" at the forefront of the burgeoning private space industry; not to mention the opportunities it could grant researchers and the public sector ("using satellite data and machine learning technology to support the roll out of charging points for electric vehicles," for example). There are several potential spaceport locations still under consideration, and the government has earmarked £10 million in funding that'll go towards breaking ground and complementary projects. Should a UK spaceport become a reality in the next few years, it would be the first in Europe. The European Space Agency (ESA) does have one of its own, but that's situated in the South American country of French Guiana.

How would you describe Buzzoole in a few words? Buzzoole is an end-to-end platform able to connect brands and Content Creators for mutual benefit. It uses a proprietary algorithm to identify suitable content creators to help brands take their message beyond traditional marketing channels. The resulting social amplification helps generate word of mouth, engagement, footfall, and purchase. What inspired you to create the platform?

Both industries see huge potential revenues in the market for autonomous vehicles, which could be available for wide use in just two years. But it remains unclear how many drivers will be ready to give up control, and many countries must still put laws in place to allow self-driving cars to hit the road.

Approximate message passing (AMP) refers to a class of efficient algorithms for statistical estimation in high-dimensional problems such as compressed sensing and low-rank matrix estimation. This paper analyzes the performance of AMP in the regime where the problem dimension is large but finite. For concreteness, we consider the setting of high-dimensional regression, where the goal is to estimate a high-dimensional vector $\beta_0$ from a noisy measurement $y=A \beta_0 + w$. AMP is a low-complexity, scalable algorithm for this problem. Under suitable assumptions on the measurement matrix $A$, AMP has the attractive feature that its performance can be accurately characterized in the large system limit by a simple scalar iteration called state evolution. Previous proofs of the validity of state evolution have all been asymptotic convergence results. In this paper, we derive a concentration inequality for AMP with i.i.d. Gaussian measurement matrices with finite size $n \times N$. The result shows that the probability of deviation from the state evolution prediction falls exponentially in $n$. This provides theoretical support for empirical findings that have demonstrated excellent agreement of AMP performance with state evolution predictions for moderately large dimensions. The concentration inequality also indicates that the number of AMP iterations $t$ can grow no faster than order $\frac{\log n}{\log \log n}$ for the performance to be close to the state evolution predictions with high probability. The analysis can be extended to obtain similar non-asymptotic results for AMP in other settings such as low-rank matrix estimation.

This paper considers the problem of estimating a high-dimensional vector of parameters $\boldsymbol{\theta} \in \mathbb{R}^n$ from a noisy observation. The noise vector is i.i.d. Gaussian with known variance. For a squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (ML) estimator when the dimension $n$ exceeds two. The JS-estimator shrinks the observed vector towards the origin, and the risk reduction over the ML-estimator is greatest for $\boldsymbol{\theta}$ that lie close to the origin. JS-estimators can be generalized to shrink the data towards any target subspace. Such estimators also dominate the ML-estimator, but the risk reduction is significant only when $\boldsymbol{\theta}$ lies close to the subspace. This leads to the question: in the absence of prior information about $\boldsymbol{\theta}$, how do we design estimators that give significant risk reduction over the ML-estimator for a wide range of $\boldsymbol{\theta}$? In this paper, we propose shrinkage estimators that attempt to infer the structure of $\boldsymbol{\theta}$ from the observed data in order to construct a good attracting subspace. In particular, the components of the observed vector are separated into clusters, and the elements in each cluster shrunk towards a common attractor. The number of clusters and the attractor for each cluster are determined from the observed vector. We provide concentration results for the squared-error loss and convergence results for the risk of the proposed estimators. The results show that the estimators give significant risk reduction over the ML-estimator for a wide range of $\boldsymbol{\theta}$, particularly for large $n$. Simulation results are provided to support the theoretical claims.