Coldplay's Chris Martin made a surprise appearance at a couple's wedding to play the music for their first dance. The groom's mother had asked the singer for a video message to be played at the wedding of Abbie and James Hotchkiss from Stafford. He went one better, though, and said he would appear in person, with only the newlyweds and the groom's parents in on the secret. Surprised guests saw him walk into the wedding venue, Blithfield Lakeside Barns in Staffordshire, wearing a white beanie hat to perform All My Love at the piano while the couple danced. Guests took a while to notice it was actually him, but didn't want to ruin our wedding day so asked us loads of questions once he'd gone, Mrs Hotchkiss said.

Precipitation nowcasting, key for early warning of disasters, currently relies on computationally expensive and restrictive methods that limit access to many countries. To overcome this challenge, we propose precipitation nowcasting using satellite imagery with physics constraints for improved accuracy and physical consistency. We use a novel physics-informed dual neural operator (PIANO) structure to enforce the fundamental equation of advection-diffusion during training to predict satellite imagery using a PINN loss. Then, we use a generative model to convert satellite images to radar images, which are used for precipitation nowcasting. Compared to baseline models, our proposed model shows a notable improvement in moderate (4mm/h) precipitation event prediction alongside short-term heavy (8mm/h) precipitation event prediction. It also demonstrates low seasonal variability in predictions, indicating robustness for generalization. This study suggests the potential of the PIANO and serves as a good baseline for physics-informed precipitation nowcasting.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Quality and originality: The main claims of the authors make intuitive sense. Specifically, figure 1 presents a generative model which separates note onsets from activation and spectral information. This is in keeping with the physics of a piano, where a pianist initiates a note onset by sending the hammer in free-flight. Those harmonics change over time based both on decay and on the piano's physical dampers.

Solving time-dependent partial differential equations (PDEs) is fundamental to modeling critical phenomena across science and engineering. Physics-Informed Neural Networks (PINNs) solve PDEs using deep learning. However, PINNs perform pointwise predictions that neglect the autoregressive property of dynamical systems, leading to instabilities and inaccurate predictions. We introduce Physics-Informed Autoregressive Networks (PIANO) -- a framework that redesigns PINNs to model dynamical systems. PIANO operates autoregressively, explicitly conditioning future predictions on the past. It is trained through a self-supervised rollout mechanism while enforcing physical constraints. We present a rigorous theoretical analysis demonstrating that PINNs suffer from temporal instability, while PIANO achieves stability through autoregressive modeling. Extensive experiments on challenging time-dependent PDEs demonstrate that PIANO achieves state-of-the-art performance, significantly improving accuracy and stability over existing methods. We further show that PIANO outperforms existing methods in weather forecasting.

--Floorplanning is a critical step in VLSI physical design, increasingly complicated by modern constraints such as fixed-outline requirements, whitespace removal, and the presence of pre-placed modules. However, traditional floorplanners often overlook pin assignment with modern constraints during the floorplanning stage. In this work, we introduce Piano, a floorplanning framework that simultaneously optimizes module placement and pin assignment under multiple constraints. Specifically, we construct a graph based on the geometric relationships among modules and their netlist connections, then iteratively search for shortest paths to determine pin assignments. This graph-based method also enables accurate evaluation of feedthrough and unplaced pins, thereby guiding overall layout quality. T o further improve the design, we adopt a whitespace removal strategy and employ three local optimizers to enhance layout metrics under multi-constraint scenarios. Experimental results on widely used benchmark circuits demonstrate that Piano achieves an average 6.81% reduction in HPWL, a 13.39% decrease in feedthrough wirelength, a 16.36% reduction in the number of feedthrough modules, and a 21.21% drop in unplaced pins, while maintaining zero whitespace. Floorplanning is the first step in modern VLSI physical design as it needs to determine the shape and location of large circuit modules on a chip canvas, while assigning the pins to each module's boundary for inter-module connections, thereby laying the foundation for subsequent detailed placement and routing stages.

The funeral director said "AI" as if it were a normal element of memorial services, like caskets or flowers. Of all places, I had not expected artificial intelligence to follow me into the small, windowless room of the mortuary. But here it was, ready to assist me in the task of making sense of death. It was already Wednesday, and I'd just learned that I had to write an obituary for my mother by Thursday afternoon if I wanted it to run in Sunday's paper. AI could help me do this.

This paper presents , an interactive music piece for a human pianist and a computer-controlled piano that integrates real-time automatic music transcription into a score-driven framework. Unlike previous approaches that primarily focus on improvisation-based interactions, our work establishes a balanced framework that combines composed structure with dynamic interaction. Through real-time automatic transcription as its core mechanism, the computer interprets and responds to the human performer's input in real time, creating a musical dialogue that balances compositional intent with live interaction while incorporating elements of unpredictability. In this paper, we present the development process from composition to premiere performance, including technical implementation, rehearsal process, and performance considerations.

Abstract--Towards the grand challenge of achieving humanlevel manipulation in robots, playing piano is a compelling testbed that requires strategic, precise, and flowing movements. Over the years, several works demonstrated hand-designed controllers on real world piano playing, while other works evaluated robot learning approaches on simulated piano scenarios. In this paper, we develop the first piano playing robotic system that makes use of learning approaches while also being deployed on a real world dexterous robot. Specifically, we make use of Sim2Real to train a policy in simulation using reinforcement learning before deploying the learned policy on a real world dexterous robot. In our experiments, we thoroughly evaluate the interplay between domain randomization and the accuracy of the dynamics model used in simulation. Moreover, we evaluate the robot's performance across multiple songs with varying complexity to study the generalization of our learned policy. Experimental results show that the robot can learn Playing the piano requires humans to master contact-rich to play several simple pieces successfully, after training exclusively hand movements dictated by the timing and tone they intend in simulation. This mastery is not learned quickly but through extensive practice, which requires humans to control their actions based on the haptic and auditory feedback received the natural movements of human hands. This makes it an ideal with each key pressed on the piano. In addition, human hands scenario for exploring Sim2Real transfer, where the objective are an extraordinary research subject due to their unmatched is to train an agent in simulation capable of performing in the dexterity, precision, and adaptability.

We present a new probabilistic model for transcribing piano music from audio to a symbolic form. Our model reflects the process by which discrete musical events give rise to acoustic signals that are then superimposed to produce the observed data. As a result, the inference procedure for our model naturally resolves the source separation problem introduced by the the piano's polyphony. In order to adapt to the properties of a new instrument or acoustic environment being transcribed, we learn recording-specific spectral profiles and temporal envelopes in an unsupervised fashion. Our system outperforms the best published approaches on a standard piano transcription task, achieving a 10.6% relative gain in note onset F

Roli is no stranger to quirky musical instruments. After all, it pioneered the idea of a "squishy" MIDI controller. The company's latest tool, however, could be its weirdest. The Roli Airwave is an AI-infused piano teaching gadget that also doubles as a digital theremin. Yes, the same high-pitched theremin that has appeared on hit records like The Beach Boys' "Good Vibrations" and Erykah Badu's "Incense." The Airwave is basically a tall stand with a camera on top.