Undercurrent is an upcoming immersive art event featuring audiovisual installations from around 40 musicians, headlined by Bon Iver, Grimes and The 1975, designed to inspire climate activism. Before the physical exhibition arrives in Brooklyn on September 9th, a digital sister event is today launching online that showcases 3D interactive music videos from some of the support acts. The Undercurrent digital platform includes original, unreleased music from Nosaj Thing, Mount Kimbie, Actress, Aluna, and Jayda G. Again, the focus is on spurring change around environmental issues through immersive art. Each musician's work ends with a call to action, whether it be donating to or volunteering for a non-profit. The virtual event could also be a way for budding visitors to get a feel for the main exhibition.

Decisions, decisions…we all want to make the right ones. So, in an era of artificial intelligence, how can technology help employees make challenging choices that can delight customers while improving efficiency and ultimately the bottom line? And worryingly, a survey by McKinsey revealed that 60% of executives thought that bad decisions were as frequent as good ones. Often it was attributed to cognitive biases. You only have to look at mistakes made during the recent pandemic when the rush to digitize processes and assist remote teams led to bad decisions on introducing automation.

Picture this: Colossal, gas-powered autonomous robots bulldoze across acres of homogeneous farmland under a blackened sky that reeks of pollution. The trees have all been chopped down and there are no animals in sight. Pesticides are sprayed in excess because humans no longer tend to the fields. The machines do their jobs--producing massive amounts of food to feed our growing population--but it's not without ecological cost. Or, envision another future: Smaller robots cultivate mosaic plots of many different crops, working around the trees, streams, and wildlife of the natural landscape.

In the corner of an Ohio field, a laser-armed robot inches through a sea of onions, zapping weeds as it goes. This field doesn't belong to a dystopian future but to Shay Myers, a third-generation farmer whose TikTok posts about farming life often go viral. He began using two robots last year to weed his 12-hectare (30-acre) crop. The robots – which are nearly three metres long, weigh 4,300kg (9,500lb), and resemble a small car – clamber slowly across a field, scanning beneath them for weeds which they then target with laser bursts. "For microseconds you watch these reddish color bursts. You see the weed, it lights up as the laser hits, and it's just gone," said Myers.

It's an unusual time to be in the paper and pulp industry. The markets for paper products change on an almost monthly basis. It can be difficult to know how you should respond to keep your operations productive, profitable, and efficient. Should you invest in automation for long-term efficiency gains? Or should you batten down the hatches and reduce spending until the markets level out? Several market forces have impacted the paper and pulp industry in recent years.

Process mining techniques can help organizations to improve their operational processes. Organizations can benefit from process mining techniques in finding and amending the root causes of performance or compliance problems. Considering the volume of the data and the number of features captured by the information system of today's companies, the task of discovering the set of features that should be considered in root cause analysis can be quite involving. In this paper, we propose a method for finding the set of (aggregated) features with a possible effect on the problem. The root cause analysis task is usually done by applying a machine learning technique to the data gathered from the information system supporting the processes. To prevent mixing up correlation and causation, which may happen because of interpreting the findings of machine learning techniques as causal, we propose a method for discovering the structural equation model of the process that can be used for root cause analysis. We have implemented the proposed method as a plugin in ProM and we have evaluated it using two real and synthetic event logs. These experiments show the validity and effectiveness of the proposed methods.

We propose a method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a priori. By contrast, existing methods that learn articulation models from raw observations typically only predict point estimates of the model parameters, which are insufficient to guarantee the safe manipulation of articulated objects. Our core contributions include a novel representation for distributions over rigid body transformations and articulation model parameters based on screw theory, von Mises-Fisher distributions, and Stiefel manifolds. Combining these concepts allows for an efficient, mathematically sound representation that implicitly satisfies the constraints that rigid body transformations and articulations must adhere to. Leveraging this representation, we introduce a novel deep learning based approach, DUST-net, that performs category-independent articulation model estimation while also providing model uncertainties. We evaluate our approach on several benchmarking datasets and real-world objects and compare its performance with two current state-of-the-art methods. Our results demonstrate that DUST-net can successfully learn distributions over articulation models for novel objects across articulation model categories, which generate point estimates with better accuracy than state-of-the-art methods and effectively capture the uncertainty over predicted model parameters due to noisy inputs.

In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to industries, which in turn belong to ever coarser divisions such as sectors; microbes are commonly arranged in a taxonomic hierarchy from species to kingdoms; street blocks belong to neighborhoods, which in turn belong to larger-scale regions. The problem of tree-based aggregation that we consider in this paper asks which of these tree-defined subgroups of leaves should really be treated as a single entity and which of these entities should be distinguished from each other. We introduce the "false split rate", an error measure that describes the degree to which subgroups have been split when they should not have been. We then propose a multiple hypothesis testing algorithm for tree-based aggregation, which we prove controls this error measure. We focus on two main examples of tree-based aggregation, one which involves aggregating means and the other which involves aggregating regression coefficients. We apply this methodology to aggregate stocks based on their volatility and to aggregate neighborhoods of New York City based on taxi fares.

Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments. Proximity detection using on-robot sensors can be used to avoid or mitigate impending collisions. However, existing proximity sensing methods are orientation and placement dependent, resulting in blind spots even with large numbers of sensors. In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW. AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-the-shelf robots with minimal modifications. We further introduce a set of signal processing techniques and a lightweight neural network to address the unique challenges in using the LSW for proximity sensing. Finally, we demonstrate a prototype system consisting of a single piezoelectric element pair on a robot manipulator, which validates our design. We conducted several micro benchmark experiments and performed more than 2000 on-robot proximity detection trials with various potential robot arm materials, colliding objects, approach patterns, and robot movement patterns. AuraSense achieves 100% and 95.3% true positive proximity detection rates when the arm approaches static and mobile obstacles respectively, with a true negative rate over 99%, showing the real-world viability of this system.

Among daily tasks of database administrators (DBAs), the analysis of query workloads to identify schema issues and improving performances is crucial. Although DBAs can easily pinpoint queries repeatedly causing performance issues, it remains challenging to automatically identify subsets of queries that share some properties only (a pattern) and simultaneously foster some target measures, such as execution time. Patterns are defined on combinations of query clauses, environment variables, database alerts and metrics and help answer questions like what makes SQL queries slow? What makes I/O communications high? Automatically discovering these patterns in a huge search space and providing them as hypotheses for helping to localize issues and root-causes is important in the context of explainable AI. To tackle it, we introduce an original approach rooted on Subgroup Discovery. We show how to instantiate and develop this generic data-mining framework to identify potential causes of SQL workloads issues. We believe that such data-mining technique is not trivial to apply for DBAs. As such, we also provide a visualization tool for interactive knowledge discovery. We analyse a one week workload from hundreds of databases from our company, make both the dataset and source code available, and experimentally show that insightful hypotheses can be discovered.