Data is the sustenance that keeps machine learning going. No matter how powerful a machine learning and/or deep learning model is, it can never do what we want it to do with bad data. While the validation process cannot directly find what is wrong, the process can show us sometimes that there is a problem with the stability of the model. A typical ratio for this might be 80/10/10 to make sure you still have enough training data. After training the model with the training set, the user will move onto validating the results and tuning the hyperparameters with the validation set till the user reaches a satisfactory performance metric.

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In William Gibson's novel Zero History, a key character dons the ugliest T-shirt in the world – a ridiculous-looking garment that magically renders the wearer invisible to CCTV. Now, as states across the world deploy artificially intelligent surveillance systems to track, trace and monitor citizens, we may find ourselves wearing ugly T-shirts of our own. Researchers at Northeastern University, MIT and IBM have designed a top printed with a kaleidoscopic patch of colour that renders the wearer undetectable to AI. It's part of a growing number of "adversarial examples" – physical objects designed to counteract the creep of digital surveillance. "The adversarial T-shirt works on the neural networks used for object detection," explains Xue Lin, an assistant professor of electrical and computer engineering at Northeastern, and co-author of a recent paper on the subject.

Then, a dynamics model learned to infer the motion and dynamic relationships among the different objects. Third, a semantic parser turned each question into a functional program. Finally, a symbolic program executor ran the program, using information about the objects and their relationships to produce an answer to the question," stated the paper. Researchers found that NS-DR outperformed the deep learning models significantly across all categories of questions. While the complexities of tasks that neural networks can accomplish have reached a new high with GANs, neuro-symbolic AI gives hope in performing more complex tasks. By combining the best of two systems, it can create AI systems which require fewer data and demonstrate common sense, thereby accomplishing more complex tasks.

It has been shown that machine learning models, especially deep neural networks, are vulnerable to small adversarial perturbations, i.e., a small carefully crafted perturbation added to the input may significantly change the prediction results (Szegedy et al., 2014; Goodfellow et al., 2015; Biggio and Roli, 2018; Fawzi et al., 2018). Therefore, the problem of finding those perturbations, also known as adversarial attacks, has become an important way to evaluate the model robustness: the more difficult to attack a given model, the more robust it is. Depending on the information an adversary can access, the adversarial attacks can be classified into white-box and black-box settings. In the white-box setting, the target model is completely exposed to the attacker, and adversarial perturbations could be easily crafted by exploiting the first-order information, i.e., gradients with respect to the input (Carlini and Wagner, 2017; Madry et al., 2018). Despite of its efficiency and effectiveness, the white-box setting is an overly strong and pessimistic threat model, and white-box attacks are usually not practical when attacking real-world machine learning systems due to the invisibility of the gradient information. Instead, we focus on the problem of black-box attacks, where the model structure and parameters (weights) are not available to the attacker.

This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.

Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and demanding training algorithms, highlighting the need for more efficient solutions. The class of Reservoir Computing (RC) models can play an important role in this context, enabling to develop fruitful graph embeddings through untrained recursive architectures. In this paper, we study progressive simplifications to the design strategy of RC neural networks for graphs. Our core proposal is based on shaping the organization of the hidden neurons to follow a ring topology. Experimental results on graph classification tasks indicate that ring-reservoirs architectures enable particularly effective network configurations, showing consistent advantages in terms of predictive performance.

Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteristics extracted from the first night of data. As a single night is a very small amount of data to train a sleep staging model, we propose a Kullback-Leibler (KL) divergence regularized transfer learning approach to address this problem. We employ the pretrained SeqSleepNet (i.e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model. This is done by adding the KL divergence between the output of the subject independent model and the output of the personalized model to the loss function during finetuning. In effect, KL-divergence regularization prevents the personalized model from overfitting to the single-night data and straying too far away from the subject independent model. Experimental results on the Sleep-EDF Expanded database with 75 subjects show that sleep staging personalization with a single-night data is possible with help of the proposed KL-divergence regularization. On average, we achieve a personalized sleep staging accuracy of 79.6%, a Cohen's kappa of 0.706, a macro F1-score of 73.0%, a sensitivity of 71.8%, and a specificity of 94.2%. We find both that the approach is robust against overfitting and that it improves the accuracy by 4.5 percentage points compared to non-personalization and 2.2 percentage points compared to personalization without regularization.

Heavy rainfall events can cause major disruption to human activities. It is desirable to predict these events ahead of time so that decision makers can take action to protect life, property and prosperity. Nowcasting, or short-term forecasting from observations, remains an important tool in predicting these events. The essential goals of nowcasting are identical to those of all weather forecasting, with the only difference being the spatial and temporal scales involved. The World Meteorological Organization (WMO, 2016) distinguishes among the various forecasting time horizons as: "Usually forecasts for the next 0-2 hours are called nowcasting, from 2-12 hours very short-range forecasting (VSRF), and short-range forecasting beyond that; but the capabilities of the different ranges can vary upon variables and weather situations." Radar-based nowcasting emerged in an era of mainly synoptic and mesoscale weather prediction. Predicting rainfall during that time was a challenge for numerical weather prediction (NWP) models, since computational restrictions limited the resolution at which NWP models could operate. As a result, NWP models were able to capture mesoscale weather patterns such as fronts, but not the smaller-scale convective patterns that occur within mesoscale systems. Thus, these models had limited utility in predicting rainfall in the early hours of the forecast because of its dependence on the unrepresented small scales.

In many scientific problems such as video surveillance, modern genomic analysis, and clinical studies, data are often collected from diverse domains across time that exhibit time-dependent heterogeneous properties. It is important to not only integrate data from multiple sources (called multiview data), but also to incorporate time dependency for deep understanding of the underlying system. Latent factor models are popular tools for exploring multi-view data. However, it is frequently observed that these models do not perform well for complex systems and they are not applicable to time-series data. Therefore, we propose a generative model based on variational autoencoder and recurrent neural network to infer the latent dynamic factors for multivariate timeseries data. This approach allows us to identify the disentangled latent embeddings across multiple modalities while accounting for the time factor. We invoke our proposed model for analyzing three datasets on which we demonstrate the effectiveness and the interpretability of the model.