Multivariate time-series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-series temporal correlations and inter-series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the time domain and resort to pre-defined priors as inter-series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting. StemGNN captures inter-series correlations and temporal dependencies \textit{jointly} in the \textit{spectral domain}. It combines Graph Fourier Transform (GFT) which models inter-series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN. Code is available at https://github.com/microsoft/StemGNN/

GlobalData research has found the top influencers in future cities based on their performance online and on social media.Using research from GlobalData's Influencer platform, Verdict has named ten of the most influential people and companies in digital construction on Twitter during Q4 2020. Ronald Van Loon is a principal analyst and CEO of the Intelligent World, an influencer network connecting businesses and experts with new tech, artificial intelligence (AI), analytics, and data enthusiasts. He is a recognised thought leader in technologies such as AI, the internet of things (IoT), machine learning, and 5G, among others. Loon is an advisory board member at Simplilearn, an education management company and has also served as director of Advertisement, an information technology and services company. Glen Gilmore is the founding faculty for digital marketing programmes at the Rutgers University School of Business.

Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observation of the object does not convey enough information over these properties. In contrast, haptic exploration is time consuming as it only provides information relevant to the explored parts of the object. In this work, we propose a joint visuo-haptic object model that enables the estimation of surface friction coefficient over an entire object by exploiting the correlation of visual and haptic information, together with a limited haptic exploration by a robotic arm. We demonstrate the validity of the proposed method by showing its ability to estimate varying friction coefficients on a range of real multi-material objects. Furthermore, we illustrate how the estimated friction coefficients can improve grasping success rate by guiding a grasp planner toward high friction areas.

We propose a complexity measure of a neural network mapping function based on the diversity of the set of tangent spaces from different inputs. Treating each tangent space as a linear PAC concept we use an entropy-based measure of the bundle of concepts in order to estimate the conceptual capacity of the network. The theoretical maximal capacity of a ReLU network is equivalent to the number of its neurons. In practice however, due to correlations between neuron activities within the network, the actual capacity can be remarkably small, even for very big networks. Empirical evaluations show that this new measure is correlated with the complexity of the mapping function and thus the generalisation capabilities of the corresponding network. It captures the effective, as oppose to the theoretical, complexity of the network function. We also showcase some uses of the proposed measure for analysis and comparison of trained neural network models.

Federated Learning (FL) is quickly becoming a goto distributed training paradigm for users to jointly train a global model without physically sharing their data. Users can indirectly contribute to, and directly benefit from a much larger aggregate data corpus used to train the global model. However, literature on successful application of FL in real-world problem settings is somewhat sparse. In this paper, we describe our experience applying a FL based solution to the Named Entity Recognition (NER) task for an adverse event detection application in the context of mass scale vaccination programs. We present a comprehensive empirical analysis of various dimensions of benefits gained with FL based training. Furthermore, we investigate effects of tighter Differential Privacy (DP) constraints in highly sensitive settings where federation users must enforce Local DP to ensure strict privacy guarantees. We show that local DP can severely cripple the global model's prediction accuracy, thus dis-incentivizing users from participating in the federation. In response, we demonstrate how recent innovation on personalization methods can help significantly recover the lost accuracy. We focus our analysis on the Federated Fine-Tuning algorithm, FedFT, and prove that it is not PAC Identifiable, thus making it even more attractive for FL-based training.

Abstract-- The General Data Protection Regulation (GDPR) aims to ensure that all personal data processing activities are fair and transparent for the European Union (EU) citizens, regardless of whether these are carried out within the EU or anywhere else. To this end, it sets strict requirements to transfer personal data outside the EU. However, checking these requirements is a daunting task for supervisory authorities, particularly in the mobile app domain due to the huge number of apps available and their dynamic nature. In this paper, we propose a fully automated method to assess compliance of mobile apps with the GDPR requirements for cross-border personal data transfers. We have applied the method to the top-free 10,080 apps from the Google Play Store. The results reveal that there is still a very significant gap between what app providers and third-party recipients do in practice and what is intended by the GDPR. A substantial 56% of analysed apps are potentially non-compliant with the GDPR cross-border transfer requirements. THE distributed nature of today's digital systems and services across the world [1], or shared between chains of thirdparty not only facilitates the collection of personal data service providers [6], even without the app developer's from individuals anywhere, but also their transfer to different knowledge [7]. Second, apps are distributed through countries around the world [1]. This raises potential global stores, enabling app providers to easily reach markets risks to the privacy of individuals, as the organizations and users beyond its country of residence. In this sending and receiving personal data can be subject to different context, there is a need for constant vigilance by the various data protection laws and, therefore, may not offer an stakeholders, including app developers, supervisory equivalent level of protection.

The past years have seen seen the development and deployment of machine-learning algorithms to estimate personalized treatment-assignment policies from randomized controlled trials. Yet such algorithms for the assignment of treatment typically optimize expected outcomes without taking into account that treatment assignments are frequently subject to hypothesis testing. In this article, we explicitly take significance testing of the effect of treatment-assignment policies into account, and consider assignments that optimize the probability of finding a subset of individuals with a statistically significant positive treatment effect. We provide an efficient implementation using decision trees, and demonstrate its gain over selecting subsets based on positive (estimated) treatment effects. Compared to standard tree-based regression and classification tools, this approach tends to yield substantially higher power in detecting subgroups with positive treatment effects. INTRODUCTION Recent years have seen the development of machine-learning algorithms that estimate heterogeneous causal effects from randomized controlled trials. While the estimation of average effects - for example, how effective a vaccine is overall, whether a conditional cash transfer reduces poverty, or which ad leads to more clicks - can inform the decision whether to deploy a treatment or not, heterogeneous treatment effect estimation allows us to decide who should get treated. These algorithms aim to maximize realized outcomes, and thus focus on assigning treatment to individuals with positive (estimated) treatment effects. Yet in practice, the deployment of assignment policies often only happens after passing a test that the assignment produces a positive net effect relative to some status quo. For example, a drug manufacturer may have to demonstrate that the drug is effective on the target population by submitting a hypothesis test to the FDA for approval.

We investigate the potential of tensor network based machine learning methods to scale to large image and text data sets. For that, we study how the mutual information between a subregion and its complement scales with the subsystem size $L$, similarly to how it is done in quantum many-body physics. We find that for text, the mutual information scales as a power law $L^\nu$ with a close to volume law exponent, indicating that text cannot be efficiently described by 1D tensor networks. For images, the scaling is close to an area law, hinting at 2D tensor networks such as PEPS could have an adequate expressibility. For the numerical analysis, we introduce a mutual information estimator based on autoregressive networks, and we also use convolutional neural networks in a neural estimator method.

When optimizing over loss functions it is common practice to use momentum-based accelerated methods rather than vanilla gradient-based method. Despite widely applied to arbitrary loss function, their behaviour in generically non-convex, high dimensional landscapes is poorly understood. In this work we used dynamical mean field theory techniques to describe analytically the average behaviour of these methods in a prototypical non-convex model: the (spiked) matrix-tensor model. We derive a closed set of equations that describe the behaviours of several algorithms including heavy-ball momentum and Nesterov acceleration. Additionally we characterize the evolution of a mathematically equivalent physical system of massive particles relaxing toward the bottom of an energetic landscape. Under the correct mapping the two dynamics are equivalent and it can be noticed that having a large mass increases the effective time step of the heavy ball dynamics leading to a speed up.

Standard Neural Networks can learn mathematical operations, but they do not extrapolate. Extrapolation means that the model can apply to larger numbers, well beyond those observed during training. Recent architectures tackle arithmetic operations and can extrapolate; however, the equally important problem of quantitative reasoning remains unaddressed. In this work, we propose a novel architectural element, the Neural Status Register (NSR), for quantitative reasoning over numbers. Our NSR relaxes the discrete bit logic of physical status registers to continuous numbers and allows end-to-end learning with gradient descent. Experiments show that the NSR achieves solutions that extrapolate to numbers many orders of magnitude larger than those in the training set. We successfully train the NSR on number comparisons, piecewise discontinuous functions, counting in sequences, recurrently finding minimums, finding shortest paths in graphs, and comparing digits in images.