When working with tabular data, missingness is always one of the most painful problems. Throughout many years, researchers have continuously explored better and better ways to impute missing data. Recently, with the rapid development evolution in machine learning and deep learning, there is a new trend of leveraging generative models to solve the imputation task. While the imputing version of famous models such as V ariational Autoencoders or Generative Adversarial Networks were investigated, prior work has overlooked Neural Cellular Automata (NCA), a powerful computational model. In this paper, we propose a novel imputation method that is inspired by NCA. We show that, with some appropriate adaptations, an NCA-based model is able to address the missing data imputation problem. We also provide several experiments to evidence that our model outperforms state-of-the-art methods in terms of imputation error and post-imputation performance. Introduction There is no doubt that data plays a crucial role in this modern world. In numerous business and scientific applications, data is the foundation for decision-making process, enabling experts to detect noticeable patterns and take advantage of them. One of the most common types of data is tabular data, which presents in almost every domains from economics, finance to healthcare, demography. Being organized in structured rows and columns, one can straightforwardly apply statistical methods, perform calculations and draw meaningful insights from this data. Moreover, many machine learning algorithms, especially those used in supervised learning tasks, are designed to work optimally on tabular data.

The computational generation of poems is a complex task, which involves several sound, prosodic and rhythmic resources. In this work we present PROPOE 2, with the extension of structural and rhythmic possibilities compared to the original system, generating poems from metered sentences extracted from the prose of Brazilian literature, with multiple rhythmic assembly criteria. These advances allow for a more coherent exploration of rhythms and sound effects for the poem. Results of poems generated by the system are demonstrated, with variations in parameters to exemplify generation and evaluation using various criteria.

The intention of this article is to propose the use of artificial intelligence to detect through analysis by UFO ontology the emergence of verbal and physical aggression related to psychosocial deficiencies and their provoking agents, in an attempt to prevent catastrophic consequences within school environments.

Today's global society is constantly on the move. In this context, Artificial Intelligence, AI, has opened up a range of possibilities for changing the course of logistics work systems. Thanks to the application of AI companies are turning their trading routines into proactive schemes, so that traders can anticipate market behaviours. Consequently, logistics companies are adapting their resources to achieve greater profitability, efficiency, success and development. All developments linked to the incorporation of Artificial Intelligence in the logistics sector have been boosted by the momentum of various factors such as the increased level of competition in the market, which pushes logistics companies to invest in innovations such as this technology.

Abstract: Nonlinear independent component analysis (nICA) aims at recovering statistically independent latent components that are mixed by unknown nonlinear functions. Central to nICA is the identifiability of the latent components, which had been elusive until very recently. Specifically, Hyvärinen et al. have shown that the nonlinearly mixed latent components are identifiable (up to often inconsequential ambiguities) under a generalized contrastive learning (GCL) formulation, given that the latent components are independent conditioned on a certain auxiliary variable. The GCL-based identifiability of nICA is elegant, and establishes interesting connections between nICA and popular unsupervised/self-supervised learning paradigms in representation learning, causal learning, and factor disentanglement. However, existing identifiability analyses of nICA all build upon an unlimited sample assumption and the use of ideal universal function learners -- which creates a non-negligible gap between theory and practice.

Dr. Richard Benjamins is Chief AI & Data Strategist at Telefónica. He is among the 100 most influential people in data-driven business (DataIQ 100, 2018). He is also co-founder and Vice President of the Spanish observatory for ethical and social impacts of AI (OdiseIA). He was Group Chief Data Officer at AXA (Insurance) and before that held for 10 years executive positions at Telefónica on Big Data and Analytics. He is the founder of Telefónica's Big Data for Social Good department, expert to the EP's AI Observatory (EPAIO), member of the B2G data-sharing Expert Group of the EC, and a frequent speaker at Artificial Intelligence events.

Discovering causal structures among latent factors from observed data is a particularly challenging problem, in which many empirical researchers are interested. Despite its success in certain degrees, existing methods focus on the single-domain observed data only, while in many scenarios data may be originated from distinct domains, e.g. in neuroinformatics. In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for LAtent Factors (abbreviated as MD-LiNA model) to identify the underlying causal structure between latent factors (of interest), tackling not only single-domain observed data but multiple-domain ones, and provide its identification results. In particular, we first locate the latent factors and estimate the factor loadings matrix for each domain separately. Then to estimate the structure among latent factors (of interest), we derive a score function based on the characterization of independence relations between external influences and the dependence relations between multiple-domain latent factors and latent factors of interest, enforcing acyclicity, sparsity, and elastic net constraints. The resulting optimization thus produces asymptotically correct results. It also exhibits satisfactory capability in regimes of small sample sizes or highly-correlated variables and simultaneously estimates the causal directions and effects between latent factors. Experimental results on both synthetic and real-world data demonstrate the efficacy of our approach.

An innovative technology could soon greatly expand the world of some of Florida's most vulnerable children. The Florida Birth-Related Neurological Injury Compensation Association (NICA), a state program that provides lifetime support and care to families with children affected by catastrophic birth-related neurological injuries, has agreed to purchase Trexo Robotics gait trainers for all qualified children. The robotic "legs" offer newfound mobility, allowing them to be upright and walk extended distances, a step forward for children mostly limited to a wheelchair and/or passive movement through therapy. So far, three NICA children have received the new gait trainers and another seven are currently going through the approval process to receive this cutting-edge technology. "Meeting the needs of these vulnerable children and helping them live a more normal life is at the forefront of NICA's mission, so after reviewing the initial trial period for 10 families, we hope to provide the equipment for free to all qualifying families," said NICA Executive Director Kenney Shipley.

The nonlinear vector autoregressive (NVAR) model provides an appealing framework to analyze multivariate time series obtained from a nonlinear dynamical system. However, the innovation (or error), which plays a key role by driving the dynamics, is almost always assumed to be additive. Additivity greatly limits the generality of the model, hindering analysis of general NVAR process which have nonlinear interactions between the innovations. Here, we propose a new general framework called independent innovation analysis (IIA), which estimates the innovations from completely general NVAR. We assume mutual independence of the innovations as well as their modulation by a fully observable auxiliary variable (which is often taken as the time index and simply interpreted as nonstationarity). We show that IIA guarantees the identifiability of the innovations with arbitrary nonlinearities, up to a permutation and component-wise invertible nonlinearities. We propose two practical estimation methods, both of which can be easily implemented by ordinary neural network training. We thus provide the first rigorous identifiability result for general NVAR, as well as very general tools for learning such models.

New York has always been known as the city that never sleeps. In this crisis time, this definition has never been clearer. Citizens of NYC have been subjected to the stress of Coronavirus pressures, which have hit Americans with fatigue and feelings of hopelessness as a result of our grim economic situation. NYC continues to fight back and be resilient, combating the crisis situation with innovative companies like IPsoft that provide unique AI solutions for clients. By leveraging AI solutions, we can bring America back to its former glory, and instead transform the cause of sleepless nights from recession anxiety to the exhaustion of a memorable night out in the city that never sleeps.