A John Deere autonomous tractor is on display at CES 2022 in Las Vegas, Nevada. A John Deere autonomous tractor is on display at CES 2022 in Las Vegas, Nevada. Like many parts of modern life, tractors have gone high-tech, often running on advanced computer systems. But some manufacturers are tight-lipped about how these electronics work, making it difficult or nearly impossible for farmers and independent repair shops to diagnose and fix problems with the equipment. An agreement by John Deere may finally give farmers a greater hand in repairing the company's products.

Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) \emph{delimitation}, i.e., narrowing down the target systems, (ii) \emph{conceptualization}, .e., identifying how to approach social organizations, (iii) formal \emph{representation} using a combination of agent-based and network models, (iv) \emph{operationalization}, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the \emph{robustness} of social organizations and their \emph{adaptivity}, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.

As Verne understood, the U.S. Civil War (during which 60,000 amputations were performed) inaugurated the modern prosthetics era in the United States, thanks to federal funding and a wave of design patents filed by entrepreneurial prosthetists. The two World Wars solidified the for-profit prosthetics industry in both the United States and Western Europe, and the ongoing War on Terror helped catapult it into a US $6 billion dollar industry across the globe. This recent investment is not, however, a result of a disproportionately large number of amputations in military conflict: Around 1,500 U.S. soldiers and 300 British soldiers lost limbs in Iraq and Afghanistan. Limb loss in the general population dwarfs those figures. A much smaller subset--between 1,500 to 4,500 children each year--are born with limb differences or absences, myself included.

The spontaneous formation and subsequent growth, dissolution, merger and competition of social groups bears similarities to physical phase transitions in metastable finite systems. We examine three different scenarios, percolation, spinodal decomposition and nucleation, to describe the formation of social groups of varying size and density. In our agent-based model, we use a feedback between the opinions of agents and their ability to establish links. Groups can restrict further link formation, but agents can also leave if costs exceed the group benefits. We identify the critical parameters for costs/benefits and social influence to obtain either one large group or the stable coexistence of several groups with different opinions. Analytic investigations allow to derive different critical densities that control the formation and coexistence of groups. Our novel approach sheds new light on the early stage of network growth and the emergence of large connected components.

Damaging earthquakes can strike at any time. While we can't prevent them from occurring, we can make sure casualties, economic loss and disruption of essential services are kept to a minimum. Building more resilient cities is key to withstanding earthquake disasters. If we had a better idea of when earthquakes would strike, authorities could initiate local emergency, evacuation and shelter plans. But unfortunately, this is not the case.

Message passing neural networks (MPNN) have provable limitations, which can be overcome by universal networks. However, universal networks are typically impractical. The only exception is random node initialization (RNI), a data augmentation method that results in provably universal networks. Unfortunately, RNI suffers from severe drawbacks such as slow convergence and high sensitivity to changes in hyperparameters. We transfer powerful techniques from the practical world of graph isomorphism testing to MPNNs, resolving these drawbacks. This culminates in individualization-refinement node initialization (IRNI). We replace the indiscriminate and haphazard randomness used in RNI by a surgical incision of only a few random bits at well-selected nodes. Our novel non-intrusive data-augmentation scheme maintains the networks' universality while resolving the trainability issues. We formally prove the claimed universality and corroborate experimentally -- on synthetic benchmarks sets previously explicitly designed for that purpose -- that IRNI overcomes the limitations of MPNNs. We also verify the practical efficacy of our approach on the standard benchmark data sets PROTEINS and NCI1.

The graph isomorphism problem remains one of those mysteries in theoretical computer science that fascinates laypersons and experts alike. In 1979, Garey and Johnson mentioned the problem in their renowned book on computers and intractability but, in fact, it dates back even earlier and has been unresolved for over half a century. In 2015, a major advance hit the media: Babai's quasipolynomial algorithm. This was the first improvement for the general problem in over 30 years. And yet it remains an open problem. Maybe surprisingly, there are various and quite distinct areas in which the problem finds applications.

Unprecedented volumes of data and advances in AI are transforming the marketing services industry, according to new research from technology advisory and investment firm GP Bullhound. The firm's new report, AI and the Services Revolution, reveals that investments in marketing-related AI have grown steadily, with $2.5 billion of investments in emerging companies in 2018--and $1 billion invested in Q2 2019 alone. In all, a total of $11 billion has been invested in the sector since 2014. While North America accounts for the majority of funding into AI marketing companies, with a yearly volume of around $1.5 billion in 2014-2018, Europe is showing the highest growth rate. The European market grew to represent 30 percent of all transactions in H1 2019, while Asian transactions have declined from 20 percent in 2018 to only 5 percent as of H1 2019.

We address the problem of approximating a matrix by the linear combination of a column sparse matrix and a low rank matrix. Two variants of a heuristic search algorithm are described. The first produces an optimal solution but may be slow, as these problems are believed to be NP-hard. The second is much faster, but only guarantees a suboptimal solution. The quality of the approximation and the optimality criterion can be specified in terms of unitarily invariant norms.

When communities are faced with natural disasters, it usually isn't anything they see coming. Fires, flooding, severe weather, earthquakes or active shooter situations can all be devastating and leave little time to prepare a response. Many of us remember 2011 when an EF5 tornado swept through Joplin, Missouri, leaving devastation in its wake. Or this year, flooding in many parts of the country, including California, Michigan, Texas and Louisiana, has left people displaced and emergency crews trying to find ways to help everyone affected. For first responders, extensive training can help prepare appropriate response to these kinds of disasters and a wealth of others; and for many of them, technology is beginning to offer more insight into a situation, often resulting in more lives saved.