There has been a surge in the number of large and flat data sets - data sets containing a large number of features and a relatively small number of observations - due to the growing ability to collect and store information in medical research and other fields. Hierarchical clustering is a widely used clustering tool. In hierarchical clustering, large and flat data sets may allow for a better coverage of clustering features (features that help explain the true underlying clusters) but, such data sets usually include a large fraction of noise features (non-clustering features) that may hide the underlying clusters. Witten and Tibshirani (2010) proposed a sparse hierarchical clustering framework to cluster the observations using an adaptively chosen subset of the features, however, we show that this framework has some limitations when the data sets contain clustering features with complex structure. In this paper, we propose the Multi-rank sparse hierarchical clustering (MrSHC). We show that, using simulation studies and real data examples, MrSHC produces superior feature selection and clustering performance comparing to the classical (of-the-shelf) hierarchical clustering and the existing sparse hierarchical clustering framework.

Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks. In this paper, we review several possibilites to reverse map these architectures to biologically more realistic spiking networks with the aim of emulating them on fast, low-power neuromorphic hardware. Since many of these devices employ analog components, which cannot be perfectly controlled, finding ways to compensate for the resulting effects represents a key challenge. Here, we discuss three different strategies to address this problem: the addition of auxiliary network components for stabilizing activity, the utilization of inherently robust architectures and a training method for hardware-emulated networks that functions without perfect knowledge of the system's dynamics and parameters. For all three scenarios, we corroborate our theoretical considerations with experimental results on accelerated analog neuromorphic platforms.

Recently, fundamental conditions on the sampling patterns have been obtained for finite completability of low-rank matrices or tensors given the corresponding ranks. In this paper, we consider the scenario where the rank is not given and we aim to approximate the unknown rank based on the location of sampled entries and some given completion. We consider a number of data models, including single-view matrix, multi-view matrix, CP tensor, tensor-train tensor and Tucker tensor. For each of these data models, we provide an upper bound on the rank when an arbitrary low-rank completion is given. We characterize these bounds both deterministically, i.e., with probability one given that the sampling pattern satisfies certain combinatorial properties, and probabilistically, i.e., with high probability given that the sampling probability is above some threshold. Moreover, for both single-view matrix and CP tensor, we are able to show that the obtained upper bound is exactly equal to the unknown rank if the lowest-rank completion is given. Furthermore, we provide numerical experiments for the case of single-view matrix, where we use nuclear norm minimization to find a low-rank completion of the sampled data and we observe that in most of the cases the proposed upper bound on the rank is equal to the true rank.

Automation and computer intelligence to support complex human decisions becomes essential to manage large and distributed systems in the Cloud and IoT era. Understanding the root cause of an observed symptom in a complex system has been a major problem for decades. As industry dives into the IoT world and the amount of data generated per year grows at an amazing speed, an important question is how to find appropriate mechanisms to determine root causes that can handle huge amounts of data or may provide valuable feedback in real-time. While many survey papers aim at summarizing the landscape of techniques for modelling system behavior and infering the root cause of a problem based in the resulting models, none of those focuses on analyzing how the different techniques in the literature fit growing requirements in terms of performance and scalability. In this survey, we provide a review of root-cause analysis, focusing on these particular aspects. We also provide guidance to choose the best root-cause analysis strategy depending on the requirements of a particular system and application.

The idea of building a computer that uses light rather than electricity goes back more than half a century. "Optical computing" has long promised faster performance while consuming much less energy than conventional electronic computers. The prospect of a practical optical computer has languished, however, as scientists have struggled to make the light-based components needed to outshine existing computers. Despite these setbacks, optical computers might now get a fresh start--researchers are testing a new type of photonic computer chip, which could pave the way for artificially intelligent devices as smart as self-driving cars, but small enough to fit in one's pocket. A conventional computer relies on electronic circuits that switch one another on and off in a dance carefully choreographed to correspond to, say, the multiplication of two numbers.

Do not go gentle into that good night, Old age should burn and rave at close of day; Rage, rage against the dying of the light. Welsh poet Dylan Thomas' famous lines are a passionate plea to fight against the inevitability of death. While the sentiment is poetic, the reality is far more prosaic. We are all going to die someday at a time and place that will likely remain a mystery to us until the very end. Researchers are now applying artificial intelligence, particularly machine learning and computer vision, to predict when someone may die.

Drones have already become common, but with commercial ones being tested by the likes of Google and Amazon, the government will sooner or later have to step in and determine how they should be managed. The Federal Aviation Administration (FAA) is working on remote identification system for drones, which would ensure their safety. It held a meeting with stakeholders such as Amazon and Ford and the New York Police Department on June 21 and releases a press statement later. Currently drones weighing over half a pound need to have ID tags, but this identification system might not work well, as seeing the ID tags while the drone is mid-air is near impossible. "The Aviation Rulemaking Committee considered issues such as existing regulations applicable to drone identification and tracking, air traffic management for drones, concerns and authorities of local law enforcement, and potential legal considerations. The group developed some preliminary questions and identification parameters, and reviewed a sample of existing identification technologies."

One of the hottest areas for VC investment at the moment is AI/machine learning -- that includes artificial intelligence algorithms, related machine learning systems, neural networks, and back-end processing to produce insightful and self-learning applications. As Nvidia's CEO recently said: VC investment in AI has risen from $3.2 billion in 2014 to $9.5 billion for the first five months of 2017 annualized, with the number of funding rounds nearly doubling since 2015 to over 1,200 on an annualized basis so far this year. Investors piling into a space aim for multiple exits worth hundreds of millions of dollars. However, the pattern of AI exits is the opposite. Most successfully-exited AI companies sell for below $50 million after raising only a small amount of money.

The United States appears poised to heighten scrutiny of Chinese investment in Silicon Valley to better shield sensitive technologies seen as vital to U.S. national security, current and former U.S. officials tell Reuters. Of particular concern is China's interest in fields such as artificial intelligence and machine learning, which have increasingly attracted Chinese capital in recent years. The worry is that cutting-edge technologies developed in the United States could be used by China to bolster its military capabilities and perhaps even push it ahead in strategic industries. The U.S. government is now looking to strengthen the role of the Committee on Foreign Investment in the United States (CFIUS), the inter-agency committee that reviews foreign acquisitions of U.S. companies on national security grounds. An unreleased Pentagon report, viewed by Reuters, warns that China is skirting U.S. oversight and gaining access to sensitive technology through transactions that currently don't trigger CFIUS review.

This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions. In particular, this work studies how fairness propagates through a compound decision-making processes, which we call a pipeline. Prior work in algorithmic fairness only focuses on fairness with respect to one decision. However, many decision-making processes require more than one decision. For instance, hiring is at least a two stage model: deciding who to interview from the applicant pool and then deciding who to hire from the interview pool. Perhaps surprisingly, we show that the composition of fair components may not guarantee a fair pipeline under a $(1+\varepsilon)$-equal opportunity definition of fair. However, we identify circumstances that do provide that guarantee. We also propose numerous directions for future work on more general compound machine learning decisions.