Latent space models are frequently used for modeling single-layer networks and include many popular special cases, such as the stochastic block model and the random dot product graph. However, they are not well-developed for more complex network structures, which are becoming increasingly common in practice. Here we propose a new latent space model for multiplex networks: multiple, heterogeneous networks observed on a shared node set. Multiplex networks can represent a network sample with shared node labels, a network evolving over time, or a network with multiple types of edges. The key feature of our model is that it learns from data how much of the network structure is shared between layers and pools information across layers as appropriate. We establish identifiability, develop a fitting procedure using convex optimization in combination with a nuclear norm penalty, and prove a guarantee of recovery for the latent positions as long as there is sufficient separation between the shared and the individual latent subspaces. We compare the model to competing methods in the literature on simulated networks and on a multiplex network describing the worldwide trade of agricultural products.

The early phase of training has been shown to be important in two ways for deep neural networks. First, the degree of regularization in this phase significantly impacts the final generalization. Second, it is accompanied by a rapid change in the local loss curvature influenced by regularization choices. Connecting these two findings, we show that stochastic gradient descent (SGD) implicitly penalizes the trace of the Fisher Information Matrix (FIM) from the beginning of training. We argue it is an implicit regularizer in SGD by showing that explicitly penalizing the trace of the FIM can significantly improve generalization. We further show that the early value of the trace of the FIM correlates strongly with the final generalization. We highlight that in the absence of implicit or explicit regularization, the trace of the FIM can increase to a large value early in training, to which we refer as catastrophic Fisher explosion. Finally, to gain insight into the regularization effect of penalizing the trace of the FIM, we show that 1) it limits memorization by reducing the learning speed of examples with noisy labels more than that of the clean examples, and 2) trajectories with a low initial trace of the FIM end in flat minima, which are commonly associated with good generalization.

We study the interplay of fairness, welfare, and equity considerations Studying the case of personalized pricing is conceptually challenging in personalized pricing based on customer features. Sellers because prices are a shared tool in drastically different are increasingly able to conduct price personalization based on domains: we consider lending/insurance, consumer goods, and public predictive modeling of demand conditional on covariates: setting provision. A crucial distinction is between value-based pricing customized interest rates, targeted discounts of consumer goods, that offers different prices to customers based on their estimated and personalized subsidies of scarce resources with positive externalities willingness to pay, and risk-based pricing which offers different like vaccines and bed nets. These different application areas prices to customers based on their estimated costs, as in lending may lead to different concerns around fairness, welfare, and equity and insurance [34]. While discrimination law is strongest in insurance on different objectives: price burdens on consumers, price envy, and lending, in lending, discrimination concerns often firm revenue, access to a good, equal access, and distributional consequences arise from individual agents providing offers from an actuariallyfair when the good in question further impacts downstream securitized rate sheet [9]. In particular, distributional concerns outcomes of interest. We conduct a comprehensive literature review regarding price optimization reflect overall concern for differentially in order to disentangle these different normative considerations adept/prepared/educated negotiating customers in insurance and propose a taxonomy of different objectives with mathematical and lending, but slight optimism in value-based pricing since lowincome definitions. We focus on observational metrics that do not assume individuals may be more price-sensitive [9]. Hence, the access to an underlying valuation distribution which is either unobserved majority of our analysis will focus on value-based pricing, which due to binary feedback or ill-defined due to overriding lends itself more readily to price optimization.

Prediction #6: The U.S. federal government will adopt a more proactive policy approach to AI in 2021 ... [ ] under President Biden. Below are 10 bold predictions about what will unfold in the world of artificial intelligence in 2021, from academic research to startups to capital markets to regulation. To keep ourselves honest, we will revisit these predictions in December 2021 to grade how we did. Autonomous vehicle developers like Waymo and Cruise have massive ongoing cash needs. Public market investors are thirsty for IPOs.

In recent years, several studies have provided insight on the functioning of the brain which consists of neurons and form networks via interconnection among them by synapses. Neural networks are formed by interconnected systems of neurons, and are of two types, namely, the Artificial Neural Network (ANNs) and Biological Neural Network (interconnected nerve cells). The ANNs are computationally influenced by human neurons and are used in modelling neural systems. The reasoning foundations of ANNs have been useful in anomaly detection, in areas of medicine such as instant physician, electronic noses, pattern recognition, and modelling biological systems. Advancing research in artificial intelligence using the architecture of the human brain seeks to model systems by studying the brain rather than looking to technology for brain models. This study explores the concept of ANNs as a simulator of the biological neuron, and its area of applications. It also explores why brain-like intelligence is needed and how it differs from computational framework by comparing neural networks to contemporary computers and their modern day implementation.

Gaming never went out of style, but in 2020, it evolved from a fun hobby into an essential lifeline. Staying sane isn't easy when you're stuck in isolation for months on end. You can only watch so much Netflix before your brain starts dripping out of your ears. Games provide more active experiences that can help you forget that you've been staring at the same walls for weeks, letting you explore far-away virtual worlds or hang out with friends in multiplayer lobbies. In 2020, gaming became vital.

The Chinese artist Xu Bing has long experimented to stunning effect with the limits of the written form. Last year I visited the Centre del Carme in Valencia, Spain, to see a retrospective of his work. One installation, Book from the Sky, featured scrolls of paper looping down from the ceiling and lying along the floor of a large room, printed Chinese characters emerging into view as I moved closer to the reams of paper. But this was no ordinary Chinese text: Xu Bing had taken the form, even constituent parts, of real characters, to create around 4,000 entirely false versions. The result was a text which looked readable but had no meaning at all.

This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with quarantine policies, can handle different objectives, is dynamic and adaptive in the sense that it continually adapts to changes in real-time data. The bandit algorithm uses contact tracing, location-based sampling and random sampling in order to select specific individuals to test. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. Experiments also suggest that smart-testing can significantly reduce the death rates as compared to current methods such as testing symptomatic individuals with or without contact tracing.

An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes. Designing systems to automatically detect hateful content is of paramount importance if we are to mitigate its undesirable effects on the society at large. The detection of multimodal hate speech is an intrinsically difficult and open problem: memes convey a message using both images and text and, hence, require multimodal reasoning and joint visual and language understanding. In this work, we seek to advance this line of research and develop a multimodal framework for the detection of hateful memes. We improve the performance of existing multimodal approaches beyond simple fine-tuning and, among others, show the effectiveness of upsampling of contrastive examples to encourage multimodality and ensemble learning based on cross-validation to improve robustness. We furthermore analyze model misclassifications and discuss a number of hypothesis-driven augmentations and their effects on performance, presenting important implications for future research in the field. Our best approach comprises an ensemble of UNITER-based models and achieves an AUROC score of 80.53, placing us 4th on phase 2 of the 2020 Hateful Memes Challenge organized by Facebook.

Dubai/Washington – An American nuclear-powered guided-missile submarine traversed the strategically vital waterway between Iran and the Arabian Peninsula on Monday, the U.S. Navy said, in a rare announcement that comes amid rising tensions with Iran. The Navy's 5th Fleet, based in Bahrain, said the Ohio-class guided-missile submarine USS Georgia, accompanied by two other warships, passed through the Strait of Hormuz, a narrow passageway through which a fifth of the world's oil supplies travel. The unusual transit in the Persian Gulf's shallow waters, aimed at underscoring American military might in the region, follows the killing last month of Mohsen Fakhrizadeh, an Iranian scientist named by the West as the leader of the Islamic Republic's disbanded military nuclear program. It also comes some two weeks before the anniversary of the American drone strike near Baghdad airport in Iraq that killed top Iranian military commander Gen. Qassem Soleimani on Jan. 3. Iran has promised to seek revenge for both killings. The Ohio-class ballistic-missile submarine's presence in Mideast waterways signals the U.S. Navy's "commitment to regional partners and maritime security with a full spectrum of capabilities," the Navy said, demonstrating its readiness "to defend against any threat at any time."