complexpapers

[complexpapers]

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The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. ...

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Time Series forecasting (univariate and multivariate) is a problem of high complexity due the different patterns that have to be detected in the input, ranging from high to low frequencies ones. In this paper we propose a new model for timeseries prediction that utilizes convolutional layers for feature extraction, a recurrent encoder and a linear autoregressive component. We motivate the model and we test and compare it against a baseline of wid...

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The study of influence maximization in social networks has largely ignored disparate effects these algorithms might have on the individuals contained in the social network. Individuals may place a high value on receiving information, e.g. job openings or advertisements for loans. While well-connected individuals at the center of the network are likely to receive the information that is being distributed through the network, poorly connected indiv...

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Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defence...

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The increasing popularity and diversity of social media sites has encouraged more and more people to participate in multiple online social networks to enjoy their services. Each user may create a user identity to represent his or her unique public figure in every social network. User identity linkage across online social networks is an emerging task and has attracted increasing attention, which could potentially impact various domains such as rec...

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Labeled data sets are necessary to train and evaluate anomaly-based network intrusion detection systems. This work provides a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet- and flow-based network data in detail. The paper identifies 15 different properties to assess the suitability of individual data sets for specific evaluation scenarios. These properties cover a wide range of cr...

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The profound impact of Darwin's theory of evolution on biology has led to the acceptance of the theory in many complex systems that lie well beyond its original domain. Culture is one example that also exhibits key Darwinian evolutionary properties: Differential adoption of cultural variants (variation and selection), new entities imitating older ones (inheritance), and convergence toward the most suitable state (adaptation). In this work we pres...

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We formulate a novel framework that unifies kernel density estimation and empirical Bayes, where we address a broad set of problems in unsupervised learning with a geometric interpretation rooted in the concentration of measure phenomenon. We start by energy estimation based on a denoising objective which recovers the original/clean data X from its measured/noisy version Y with empirical Bayes least squares estimator. The setup is rooted in kerne...

[complexpapers]

Human mobility in bike-sharing systems: Structure of local and non-local…

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The understanding of human mobility patterns in different transportation modes is an interdisciplinary research field with a direct impact in aspects as varied as urban planning, traffic optimization, sustainability, the reduction of operating costs as well as the mitigation of pollution in urban areas. In this paper, we study the global activity of users in bike-sharing systems operating in the cities of Chicago and New York. For this transporta...

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213106

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Neural network models can now recognise images, understand text, translate languages, and play many human games at human or superhuman levels. These systems are highly abstracted, but are inspired by biological brains and use only biologically plausible computations. In the coming years, neural networks are likely to become less reliant on learning from massive labelled datasets, and more robust and generalisable in their task performance. From t...

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Nature is known to be the best optimizer. Natural processes most often than not reach an optimal equilibrium. Scientists have always strived to understand and model such processes.Thus, many algorithms exist today that are inspired by nature. Many of these algorithms and heuristics can be used to solve problems for which no polynomial time algorithms exist,such as Job Shop Scheduling and many other Combinatorial Optimization problems. We will dis...

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Deceptive and anti-deceptive technologies have been developed for various specific applications. But there is a significant need for a general, holistic, and quantitative framework of deception. Game theory provides an ideal set of tools to develop such a framework of deception. In particular, game theory captures the strategic and self-interested nature of attackers and defenders in cybersecurity. Additionally, control theory can be used to quan...

[complexpapers]

Rare and everywhere: Perspectives on scale-free networks | Nature Communications

https://www.nature.com/articles/s41467-019-09038-8

[complexpapers]

Scale-free networks are rare | Nature Communications

https://www.nature.com/articles/s41467-019-08746-5?sfns=mo

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The best resources in Machine Learning & AI. 40 of the best resources for Machine Learning. Contribute. Recently Added. Introduction to Deep Learning · Courses Coursera, Course, Paid coursera.org/lear... The goal of this course is to give learners basic understanding of modern neural networks and ...

[complexpapers]

Artificial intelligence to support human instruction

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The popular media’s recent interest in artificial intelligence (AI) has focused on autonomous systems that might ultimately replace people in fields as diverse as medicine, customer service, and transportation and logistics. Often neglected is a subfield of AI that focuses on empowering people by improving how we learn, remember, perceive, and make decisions. This human-centered focus relies on interdisciplinary research from cognitive neuroscien...

https://www.pnas.org/content/116/10/3953

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Building a good predictive model requires an array of activities such as data imputation, feature transformations, estimator selection, hyper-parameter search and ensemble construction. Given the large, complex and heterogenous space of options, off-the-shelf optimization methods are infeasible for realistic response times. In practice, much of the predictive modeling process is conducted by experienced data scientists, who selectively make use o...