With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

Product title compression for voice and mobile commerce is a well studied problem with several supervised models proposed so far. However these models have 2 major limitations; they are not designed to generate compressions dynamically based on cues at inference time, and they do not transfer well to different categories at test time. To address these shortcomings we model title compression as a meta learning problem where we ask can we learn a title compression model given only 1 example compression? We adopt an unsupervised approach to meta training by proposing an automatic task generation algorithm that models the observed label generation process as the outcome of 4 unobserved processes. We create parameterized approximations to each of these 4 latent processes to get a principled way of generating random compression rules, which are treated as different tasks. For our main meta learner, we use 2 models; M1 and M2. M1 is a task agnostic embedding generator whose output feeds into M2 which is a task specific label generator. We pre-train M1 on a novel unsupervised segment rank prediction task that allows us to treat M1 as a segment generator that also learns to rank segments during the meta-training process. Our experiments on 16000 crowd generated meta-test examples show that our unsupervised meta training regime is able to acquire a learning algorithm for different tasks after seeing only 1 example for each task. Further, we show that our model trained end to end as a black box meta learner, outperforms non parametric approaches. Our best model obtains an F1 score of 0.8412, beating the baseline by a large margin of 25 F1 points.

Your brain has been altered, neurologically rewired as you acquired a particular skill. This renovation has left you with a specialized area in your left ventral occipital temporal region, shifted facial recognition into your right hemisphere, reduced your inclination toward holistic visual processing, increased your verbal memory, and thickened your corpus callosum, which is the information highway that connects the left and right hemispheres of your brain. You are likely highly literate. As you learned to read, probably as a child, your brain reorganized itself to better accommodate your efforts, which had both functional and inadvertent consequences for your mind. So, to account for these changes to your brain--e.g,

We introduce a Content-based Document Alignment approach (CDA), an efficient method to align multilingual web documents based on content in creating parallel training data for machine translation (MT) systems operating at the industrial level. CDA works in two steps: (i) projecting documents of a web domain to a shared multilingual space; then (ii) aligning them based on the similarity of their representations in such space. We leverage lexical translation models to build vector representations using TF-IDF. CDA achieves performance comparable with state-of-the-art systems in the WMT-16 Bilingual Document Alignment Shared Task benchmark while operating in multilingual space. Besides, we created two web-scale datasets to examine the robustness of CDA in an industrial setting involving up to 28 languages and millions of documents. The experiments show that CDA is robust, cost-effective, and is significantly superior in (i) processing large and noisy web data and (ii) scaling to new and low-resourced languages.

Implicit regularization in deep learning is perceived as a tendency of gradient-based optimization to fit training data with predictors of minimal "complexity." The fact that only some types of data give rise to generalization is understood to result from them being especially amenable to fitting with low complexity predictors. A major challenge towards formalizing this intuition is to define complexity measures that are quantitative yet capture the essence of data that admits generalization. With an eye towards this challenge, we analyze the implicit regularization in tensor factorization, equivalent to a certain non-linear neural network. We characterize the dynamics that gradient descent induces on the factorization, and establish a bias towards low tensor rank, in compliance with existing empirical evidence. Then, motivated by tensor rank capturing implicit regularization of a non-linear neural network, we empirically explore it as a measure of complexity, and find that it stays extremely low when fitting standard datasets. This leads us to believe that tensor rank may pave way to explaining both implicit regularization of neural networks, and the properties of real-world data translating it to generalization.

Generating functions, which are widely used in combinatorics and probability theory, encode function values into the coefficients of a polynomial. In this paper, we explore their use as a tractable probabilistic model, and propose probabilistic generating circuits (PGCs) for their efficient representation. PGCs strictly subsume many existing tractable probabilistic models, including determinantal point processes (DPPs), probabilistic circuits (PCs) such as sum-product networks, and tractable graphical models. We contend that PGCs are not just a theoretical framework that unifies vastly different existing models, but also show huge potential in modeling realistic data. We exhibit a simple class of PGCs that are not trivially subsumed by simple combinations of PCs and DPPs, and obtain competitive performance on a suite of density estimation benchmarks. We also highlight PGCs' connection to the theory of strongly Rayleigh distributions.

We propose a novel approach to interactive theorem-proving (ITP) using deep reinforcement learning. Unlike previous work, our framework is able to prove theorems both end-to-end and from scratch (i.e., without relying on example proofs from human experts). We formulate the process of ITP as a Markov decision process (MDP) in which each state represents a set of potential derivation paths. The agent learns to select promising derivations as well as appropriate tactics within each derivation using deep policy gradients. This structure allows us to introduce a novel backtracking mechanism which enables the agent to efficiently discard (predicted) dead-end derivations and restart the derivation from promising alternatives. Experimental results show that the framework provides comparable performance to that of the approaches that use human experts, and that it is also capable of proving theorems that it has never seen during training. We further elaborate the role of each component of the framework using ablation studies.

After a thankfully uneventful seven-month journey, NASA's Mars 2020 mission is set to safely reach the Red Planet and insert itself into orbit on Thursday ahead of deploying the Perseverance rover and Ingenuity helicopter prototype that it's been toting down to the planet's surface in search for evidence of ancient microbial life. However, this expedition has been in the works for far longer than Perseverance has been travelling through interplanetary space. First announced in 2012, the mission marks the culmination of nearly a decade's work by hundreds of machinists, designers, rocket scientists and engineers at NASA's Jet Propulsion Lab. But not just anyone can get hired there, working for the world's premiere spacecraft production facility and building equipment that will grace the surfaces of neighboring planets. For Mohamed Abid, a Deputy Chief Mechanical Engineer on the Mars 2020 mission, the path to working at the JPL began in Tunisia, where he grew up.

There has been an increasing demand for demographic modelling and forecasting over the last few decades, driven by many developed countries are now suffering a rapid decline in mortality and fertility, leading to a significant increase in expenditures on health services for an ageing population and a shortage of future labour. A better understanding of the mortality and fertility patterns and trends is always of importance for all stakeholders in a society as the mortality forecasts, for example, play a vital role for the insurance and pensions industries in pricing their insurance products. The fertility predictions are also of great interest to the government and education sectors in planing children's welfare and educational services. Unlike the biological and the medical methods, statisticians have developed very different and purely mathematical methods to model the demographic patterns and trends which are well-documented by Preston et al. (2000). The history of demographic modelling with the mathematical approaches can be traced back to some deterministic models proposed in the midnineteenth century, see, for example, Gompertz (1825) and Makeham (1860). The deterministic models are, however, restricted with few fixed factors and have no stochastic process considered owing to the lack of computing capability in that early period.

It has been empirically observed that, in deep neural networks, the solutions found by stochastic gradient descent from different random initializations can be often connected by a path with low loss. Recent works have shed light on this intriguing phenomenon by assuming either the over-parameterization of the network or the dropout stability of the solutions. In this paper, we reconcile these two views and present a novel condition for ensuring the connectivity of two arbitrary points in parameter space. This condition is provably milder than dropout stability, and it provides a connection between the problem of finding low-loss paths and the memorization capacity of neural nets. This last point brings about a trade-off between the quality of features at each layer and the over-parameterization of the network. As an extreme example of this trade-off, we show that (i) if subsets of features at each layer are linearly separable, then almost no over-parameterization is needed, and (ii) under generic assumptions on the features at each layer, it suffices that the last two hidden layers have $\Omega(\sqrt{N})$ neurons, $N$ being the number of samples. Finally, we provide experimental evidence demonstrating that the presented condition is satisfied in practical settings even when dropout stability does not hold.