The increased digitalisation and monitoring of the energy system opens up numerous opportunities % and solutions which can help to decarbonise the energy system. Applications on low voltage (LV), localised networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known LV level open datasets to encourage further research and development.

The graphical structure of Probabilistic Graphical The implication problem is the task of determining whether Models (PGMs) encodes the conditional independence a set of CIs termed antecedents logically entail another (CI) relations that hold in the modeled distribution. CI, called the consequent, and it has received considerable Graph algorithms, such as d-separation, attention from both the AI and Database communities use this structure to infer additional conditional [10, 12, 15, 16, 22, 23]. Known algorithms for deriving independencies, and to query whether a specific CIs from the topological structure of the graphical model CI holds in the distribution. The premise of all are, in fact, an instance of implication. Notably, the DAG current systems-of-inference for deriving CIs in structure of Bayesian Networks is generated based on a set PGMs, is that the set of CIs used for the construction of CIs termed the recursive basis [11], and the d-separation of the PGM hold exactly. In practice, algorithms algorithm is used to derive additional CIs, implied by this for extracting the structure of PGMs from set. The d-separation algorithm is a sound and complete data, discover approximate CIs that do not hold exactly method for deriving CIs in probability distributions represented in the distribution. In this paper, we ask how by DAGs [10, 11], and hence completely characterizes the error in this set propagates to the inferred CIs the CIs that hold in the distribution.

Bayesian neural networks and deep ensembles represent two modern paradigms of uncertainty quantification in deep learning. Yet these approaches struggle to scale mainly due to memory inefficiency issues, since they require parameter storage several times higher than their deterministic counterparts. To address this, we augment the weight matrix of each layer with a small number of inducing weights, thereby projecting the uncertainty quantification into such low dimensional spaces. We further extend Matheron's conditional Gaussian sampling rule to enable fast weight sampling, which enables our inference method to maintain reasonable run-time as compared with ensembles. Importantly, our approach achieves competitive performance to the state-of-the-art in prediction and uncertainty estimation tasks with fully connected neural networks and ResNets, while reducing the parameter size to $\leq 24.3\%$ of that of a $single$ neural network.

This paper proposes various new analysis techniques for Bayes networks in which conditional probability tables (CPTs) may contain symbolic variables. The key idea is to exploit scalable and powerful techniques for synthesis problems in parametric Markov chains. Our techniques are applicable to arbitrarily many, possibly dependent parameters that may occur in various CPTs. This lifts the severe restrictions on parameters, e.g., by restricting the number of parametrized CPTs to one or two, or by avoiding parameter dependencies between several CPTs, in existing works for parametric Bayes networks (pBNs). We describe how our techniques can be used for various pBN synthesis problems studied in the literature such as computing sensitivity functions (and values), simple and difference parameter tuning, ratio parameter tuning, and minimal change tuning. Experiments on several benchmarks show that our prototypical tool built on top of the probabilistic model checker Storm can handle several hundreds of parameters.

Conditional density estimation is the task of estimating the probability of an event, conditioned on some inputs. A neural network can be used to compute the output distribution explicitly. For such a task, there are many ways to represent a continuous-domain distribution using the output of a neural network, but each comes with its own limitations for what distributions it can accurately render. If the family of functions is too restrictive, it will not be appropriate for many datasets. In this paper, we demonstrate the benefits of modeling free-form distributions using deconvolution. It has the advantage of being flexible, but also takes advantage of the topological smoothness offered by the deconvolution layers. We compare our method to a number of other density-estimation approaches, and show that our Deconvolutional Density Network (DDN) outperforms the competing methods on many artificial and real tasks, without committing to a restrictive parametric model.

Stochastic sparse linear bandits offer a practical model for high-dimensional online decision-making problems and have a rich information-regret structure. In this work we explore the use of information-directed sampling (IDS), which naturally balances the information-regret trade-off. We develop a class of information-theoretic Bayesian regret bounds that nearly match existing lower bounds on a variety of problem instances, demonstrating the adaptivity of IDS. To efficiently implement sparse IDS, we propose an empirical Bayesian approach for sparse posterior sampling using a spike-and-slab Gaussian-Laplace prior. Numerical results demonstrate significant regret reductions by sparse IDS relative to several baselines.

Graphon is a nonparametric model that generates graphs with arbitrary sizes and can be induced from graphs easily. Based on this model, we propose a novel algorithmic framework called \textit{graphon autoencoder} to build an interpretable and scalable graph generative model. This framework treats observed graphs as induced graphons in functional space and derives their latent representations by an encoder that aggregates Chebshev graphon filters. A linear graphon factorization model works as a decoder, leveraging the latent representations to reconstruct the induced graphons (and the corresponding observed graphs). We develop an efficient learning algorithm to learn the encoder and the decoder, minimizing the Wasserstein distance between the model and data distributions. This algorithm takes the KL divergence of the graph distributions conditioned on different graphons as the underlying distance and leads to a reward-augmented maximum likelihood estimation. The graphon autoencoder provides a new paradigm to represent and generate graphs, which has good generalizability and transferability.

Network activities recognition has always been a significant component of intrusion detection. However, with the increasing network traffic flow and complexity of network behavior, it is becoming more and more difficult to identify the specific behavior quickly and accurately by user network monitoring software. It also requires the system security staff to pay close attention to the latest intrusion monitoring technology and methods. All of these greatly increase the difficulty and complexity of intrusion detection tasks. The application of machine learning methods based on supervised classification technology would help to liberate the network security staff from the heavy and boring tasks. A finetuned model would accurately recognize user behavior, which could provide persistent monitoring with a relative high accuracy and good adaptability. Finally, the results of network activities recognition by J48 and Na\"ive Bayes algorithms are introduced and evaluated.

This paper elaborates on the notion of uncertainty in the context of annotation in large text corpora, specifically focusing on (but not limited to) historical languages. Such uncertainty might be due to inherent properties of the language, for example, linguistic ambiguity and overlapping categories of linguistic description, but could also be caused by lacking annotation expertise. By examining annotation uncertainty in more detail, we identify the sources and deepen our understanding of the nature and different types of uncertainty encountered in daily annotation practice. Moreover, some practical implications of our theoretical findings are also discussed. Last but not least, this article can be seen as an attempt to reconcile the perspectives of the main scientific disciplines involved in corpus projects, linguistics and computer science, to develop a unified view and to highlight the potential synergies between these disciplines.

Most MCMC algorithms contain user-controlled hyperparameters which need to be carefully selected to ensure that the MCMC algorithm explores the posterior distribution efficiently. Optimal tuning rates for many popular MCMC algorithms such the random-walk (Gelman et al., 1997) or Metropolis-adjusted Langevin algorithms (Roberts and Rosenthal, 1998) rely on setting the tuning parameters according to the Metropolis-Hastings acceptance rate. Using metrics such as the acceptance rate, hyperparameters can be optimized on-the-fly within the MCMC algorithm using adaptive MCMC (Andrieu and Thoms, 2008; Vihola, 2012). However, in the context of stochastic gradient MCMC (SGMCMC), there is no acceptance rate to tune against and the trade-off between bias and variance for a fixed computational budget means that tuning approaches designed for target invariant MCMC algorithms are not applicable. Related work Previous adaptive SGMCMC algorithms have focused on embedding ideas from the optimization literature within the SGMCMC framework, e.g.