Music generation in the audio domain using artificial intelligence (AI) has witnessed steady progress in recent years. However for some instruments, particularly the guitar, controllable instrument synthesis remains limited in expressivity. We introduce GuitarFlow, a model designed specifically for electric guitar synthesis. The generative process is guided using tablatures, an ubiquitous and intuitive guitar-specific symbolic format. The tablature format easily represents guitar-specific playing techniques (e.g. bends, muted strings and legatos), which are more difficult to represent in other common music notation formats such as MIDI. Our model relies on an intermediary step of first rendering the tablature to audio using a simple sample-based virtual instrument, then performing style transfer using Flow Matching in order to transform the virtual instrument audio into more realistic sounding examples. This results in a model that is quick to train and to perform inference, requiring less than 6 hours of training data. We present the results of objective evaluation metrics, together with a listening test, in which we show significant improvement in the realism of the generated guitar audio from tablatures.

Passivity-based control is a cornerstone of control theory and an established design approach in robotics. Its strength is based on the passivity theorem, which provides a powerful interconnection framework for robotics. However, the design of passivity-based controllers and their optimal tuning remain challenging. We propose here an intuitive design approach for fully actuated robots, where the control action is determined by a `virtual-mechanism' as in classical virtual model control. The result is a robot whose controlled behavior can be understood in terms of physics. We achieve optimal tuning by applying algorithmic differentiation to ODE simulations of the rigid body dynamics. Overall, this leads to a flexible design and optimization approach: stability is proven by passivity of the virtual mechanism, while performance is obtained by optimization using algorithmic differentiation.

Energy based control methods are at the core of modern robotic control algorithms. In this paper we present a general approach to virtual model/mechanism control, which is a powerful design tool to create energy based controllers. We present two novel virtual-mechanisms designed for robotic minimally invasive surgery, which control the position of a surgical instrument while passing through an incision. To these virtual mechanisms we apply the parameter tuning method of Larby and Forni 2022, which optimizes for local performance while ensuring global stability.

What Sassoon had heard were the early results of a curious project at the University of Edinburgh in Scotland, where Ducceschi was a researcher at the time. The Next Generation Sound Synthesis, or NESS, team had pulled together mathematicians, physicists, and computer scientists to produce the most lifelike digital music ever created, by running hyper-realistic simulations of trumpets, guitars, violins, and more on a supercomputer. Sassoon, who works with both orchestral and digital music, "trying to smash the two together," was hooked. He became a resident composer with NESS, traveling back and forth between Milan and Edinburgh for the next few years. It was a steep learning curve.

The Sun is the most important source of energy in the solar system. It is important for life to thrive on Earth but at the same time, can cause disruptions. Solar Flares - a sudden flash near sunspots occasionally accompanied by coronal mass ejection can cause interference in communication systems and even power grids. The Sun is an important factor that can impact the weather in space and on Earth, is constantly monitored by an array of telescopes and satellites. Scientists have figured out a more reliable method to study the spherical ball of plasma.

A NASA Frontier Development Lab (FDL) team has shown that by using deep learning, it is possible to virtually monitor the Sun's extreme ultraviolet (EUV) irradiance, which is a key driver of space weather. The Sun is vital for survival, but solar flares, which typically occur a few times a year, have the potential to cause severe disruptions in space and on Earth. These disruptions can impact spacecraft, satellites and even systems here on Earth, including GPS navigation, radio communications and the power grid. Deep learning can help get more value out of our current ability to monitor the Sun by providing virtual instruments to supplement physical devices. This research will be published in Science Advances on October 2, 2019 ("A deep learning virtual instrument for monitoring solar extreme ultraviolet spectral irradiance").

San Jose, Calif., October 1, 2019 – Virtual Instruments, the leader in hybrid infrastructure management for mission-critical workloads, announced today its participation at HitachiNEXT 2019, taking place October 8-10 in Las Vegas, and NetApp INSIGHT 2019, taking place October 28-30 in Las Vegas. By holistically monitoring, analyzing and optimizing the performance, availability, capacity and efficiency of hybrid IT infrastructure within the context of the application, VirtualWisdom enables enterprises to take a modern, AIOps-empowered approach to infrastructure management. The latest version of VirtualWisdom applies real-time, AI-based analytics to help enterprises proactively manage the hybrid infrastructure supporting their mission-critical applications. Meanwhile, WorkloadWisdom analyzes production storage workloads, models workloads, creates what-if testing scenarios, and produces workload performance analytics, ultimately offering users better insight into how workload behavior affects storage system performance. NEXT 2019 is the largest annual event for Hitachi Vantara users, with a focus on sharing the best ideas to store, protect, enrich, activate and monetize data.