We consider the problem of learning a general graph $G=(V,E)$ using edge-detecting queries, where the number of vertices $|V|=n$ is given to the learner. The information theoretic lower bound gives $m\log n$ for the number of queries, where $m=|E|$ is the number of edges. In case the number of edges $m$ is also given to the learner, Angluin-Chen's Las Vegas algorithm \cite{AC08} runs in $4$ rounds and detects the edges in $O(m\log n)$ queries. In the other harder case where the number of edges $m$ is unknown, their algorithm runs in $5$ rounds and asks $O(m\log n+\sqrt{m}\log^2 n)$ queries. There have been two open problems: \emph{(i)} can the number of queries be reduced to $O(m\log n)$ in the second case, and, \emph{(ii)} can the number of rounds be reduced without substantially increasing the number of queries (in both cases). For the first open problem (when $m$ is unknown) we give two algorithms. The first is an $O(1)$-round Las Vegas algorithm that asks $m\log n+\sqrt{m}(\log^{[k]}n)\log n$ queries for any constant $k$ where $\log^{[k]}n=\log \stackrel{k}{\cdots} \log n$. The second is an $O(\log^*n)$-round Las Vegas algorithm that asks $O(m\log n)$ queries. This solves the first open problem for any practical $n$, for example, $n<2^{65536}$. We also show that no deterministic algorithm can solve this problem in a constant number of rounds. To solve the second problem we study the case when $m$ is known. We first show that any non-adaptive Monte Carlo algorithm (one-round) must ask at least $\Omega(m^2\log n)$ queries, and any two-round Las Vegas algorithm must ask at least $m^{4/3-o(1)}\log n$ queries on average. We then give two two-round Monte Carlo algorithms, the first asks $O(m^{4/3}\log n)$ queries for any $n$ and $m$, and the second asks $O(m\log n)$ queries when $n>2^m$. Finally, we give a $3$-round Monte Carlo algorithm that asks $O(m\log n)$ queries for any $n$ and $m$.

We address the problem of estimating statistics of hidden units in a neural network using a method of analytic moment propagation. These statistics are useful for approximate whitening of the inputs in front of saturating non-linearities such as a sigmoid function. This is important for initialization of training and for reducing the accumulated scale and bias dependencies (compensating covariate shift), which presumably eases the learning. In batch normalization, which is currently a very widely applied technique, sample estimates of statistics of hidden units over a batch are used. The proposed estimation uses an analytic propagation of mean and variance of the training set through the network. The result depends on the network structure and its current weights but not on the specific batch input. The estimates are suitable for initialization and normalization, efficient to compute and independent of the batch size. The experimental verification well supports these claims. However, the method does not share the generalization properties of BN, to which our experiments give some additional insight.

Probabilistic linear discriminant analysis (PLDA) is a method used for biometric problems like speaker or face recognition that models the variability of the samples using two latent variables, one that depends on the class of the sample and another one that is assumed independent across samples and models the within-class variability. In this work, we propose a generalization of PLDA that enables joint modeling of two sample-dependent factors: the class of interest and a nuisance condition. The approach does not change the basic form of PLDA but rather modifies the training procedure to consider the dependency across samples of the latent variable that models within-class variability. While the identity of the nuisance condition is needed during training, it is not needed during testing since we propose a scoring procedure that marginalizes over the corresponding latent variable. We show results on a multilingual speaker-verification task, where the language spoken is considered a nuisance condition. We show that the proposed joint PLDA approach leads to significant performance gains in this task for two different datasets, in particular when the training data contains mostly or only monolingual speakers.

Recently, the intervention calculus when the DAG is absent (IDA) method was developed to estimate lower bounds of causal effects from observational high-dimensional data. Originally it was introduced to assess the effect of baseline biomarkers which do not vary over time. However, in many clinical settings, measurements of biomarkers are repeated at fixed time points during treatment exposure and, therefore, this method need to be extended. The purpose of this paper is then to extend the first step of the IDA, the Peter Clarks (PC)-algorithm, to a time-dependent exposure in the context of a binary outcome. We generalised the PC-algorithm for taking into account the chronological order of repeated measurements of the exposure and propose to apply the IDA with our new version, the chronologically ordered PC-algorithm (COPC-algorithm). A simulation study has been performed before applying the method for estimating causal effects of time-dependent immunological biomarkers on toxicity, death and progression in patients with metastatic melanoma. The simulation study showed that the completed partially directed acyclic graphs (CPDAGs) obtained using COPC-algorithm were structurally closer to the true CPDAG than CPDAGs obtained using PC-algorithm. Also, causal effects were more accurate when they were estimated based on CPDAGs obtained using COPC-algorithm. Moreover, CPDAGs obtained by COPC-algorithm allowed removing non-chronologic arrows with a variable measured at a time t pointing to a variable measured at a time t' where t'< t. Bidirected edges were less present in CPDAGs obtained with the COPC-algorithm, supporting the fact that there was less variability in causal effects estimated from these CPDAGs. The COPC-algorithm provided CPDAGs that keep the chronological structure present in the data, thus allowed to estimate lower bounds of the causal effect of time-dependent biomarkers.

Christian Derix is a principal of Woods Bagot and the Global Leader of SUPERSPACE, the design research agency of Woods Bagot. SUPERSPACE has been a pioneer in computational design since 2004 when Derix set up the first global professional Computational Design group at Aedas in London, to focus on users and spatial design. The work of the group won awards in various domains such as the 2010 President's Medal commendation of the Royal Institute of British Architects (RIBA) for Research in Practice for its Open Framework for Spatial Simulation, the 2011 Compasso d'Oro Honoray Mention for the interactive and generative web-based furniture system called VITA and the 2012 CTBUH Innovation Award for the Activated Façade for Al Bahr Towers. Derix holds a PhD from Technical University Vienna and has been teaching at various European universities, completing visiting professorships at Technical University Munich, Germany and University of Sheffield, United Kingdom. His master project for design computing in 2000 developed an unsupervised ANN for urban analysis using SOMs.

Over the past two decades, technological innovations have brought significant change for organisations throughout Ireland. Through the development of the internet, the explosion in the number of mobile devices, and the emergence of the sharing economy, technology is now central to all our lives – from how we share information, access services and interact with others. And that change is accelerating. We are on the verge of a new era of digital transformation. Emerging technologies ranging from Artificial Intelligence (AI), Virtual Reality (VR) Augmented Reality (AR), cloud computing and robotics, to machine learning will revolutionise how we work, live, play, and learn.

Facebook CEO Mark Zuckerberg is to testify before Congress over his social network's role in the harvesting millions of users' data without their knowledge. Zuckerberg turned down a similar request from British MPs to answer questions on the role of the data firm Cambridge Analytica in the US presidential election campaign. But the whistle-blower behind the scandal did agree to give evidence and, in doing so, revealed that the Brexit vote was also subject to manipulation by the firm.

Researchers at Volkswagen have been at the cutting edge of implementing D-Wave quantum computers for a number of complex optimization problems, including traffic flow optimization, among other potential use cases. These efforts are generally focused on developing algorithms suitable for the company's recently purchased 2000-qubit quantum system and have expanded to a range of new machine learning possibilities, including what a research team at the company's U.S. R&D office and the Volkswagen Data:Lab in Munich are calling quantum-assisted cluster analysis. The art and science of clustering is well known for machine learning on classical computing architectures, but the VW approach that has been tailored to perform well on a quantum processor has undergone major alterations. As the team describes, to be mapped to the D-Wave architectures, the problem has to be expressed as a quadratic unconstrained binary optimization problem. Once done, the accuracy of results is similar to the what a classical clustering algorithm can produce.

Valohai, a machine learning (ML) platform-as-a-service company, has raised $1.8M in funding to help international companies accelerate machine learning development and scale their model deployment. The round was led by Nordic seed stage investment company Superhero Capital, with participation from Reaktor Ventures and Business Finland, the Finnish Funding Agency for Innovation.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. KASPAR (Kinesics and Synchronization in Personal Assistant Robotics) is a robot originally conceived as part of a research project begun in the late 1990s by artificial intelligence researcher Kerstin Dautenhahn and her collaborators at the University of Reading in England. Initially, the objective was to develop "robotic therapy games" to facilitate communication with autistic children and to help them interact with others. In 2005, now at the University of Hertfordshire, the KASPAR Project was formally launched with the aim of developing a "social" robot having two missions: first, and mainly, to be a "social mediator" responsible for facilitating communication between autistic children and the people with whom they are in daily contact--other children (autistic or not), therapists, teachers, and parents--and also to serve as a therapeutic and learning tool designed to stimulate social development in these children. The objective was to teach young people with autism a variety of skills that most of us master, more or less fully, without any need of special education: understanding others' emotions and reacting appropriately, expressing our own feelings, playing in a group while letting everyone take turns, and imitating and cooperating with others.