Tipiano: Cascaded Piano Hand Motion Synthesis via Fingertip Priors

Synthesizing realistic piano hand motions requires both precision and naturalness. Physics-based methods achieve precision but produce stiff motions; data-driven models learn natural dynamics but struggle with positional accuracy. Piano motion exhibits a natural hierarchy: fingertip positions are nearly deterministic given piano geometry and fingering, while wrist and intermediate joints offer stylistic freedom. We present [OURS], a four-stage framework exploiting this hierarchy: (1) statistics-based fingertip positioning, (2) FiLM-conditioned trajectory refinement, (3) wrist estimation, and (4) STGCN-based pose synthesis. We contribute expert-annotated fingerings for the FürElise dataset (153 pieces, ~10 hours). Experiments demonstrate F1 = 0.910, substantially outperforming diffusion baselines (F1 = 0.121), with user study (N=41) confirming quality approaching motion capture. Expert evaluation by professional pianists (N=5) identified anticipatory motion as the key remaining gap, providing concrete directions for future improvement.

Switch-DFT: Adaptive Waveform and MIMO Switching for Energy-Efficient Base Stations

Energy efficiency has emerged as a critical challenge in modern base stations (BSs), as the power amplifier (PA) consumes a substantial portion of the total power due to its limited efficiency. We investigate waveform and mode adaptation to enhance the energy efficiency of BSs. We propose Switch-DFT, an adaptive switching framework that selects between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform-spread-OFDM (DFT-s-OFDM) waveforms, as well as between single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) modes. Switch-DFT improves efficiency by reducing PA backoff with DFT-s-OFDM and achieves the target rate at lower power by leveraging higher MIMO throughput. This results in superior energy efficiency over a wide range of the spectral efficiencies compared with static configurations.

Accelerating vRAN and O-RAN with SIMD: Architectural Perspectives and Performance Evaluation

The evolution of radio access networks (RANs) toward virtualization and openness creates new opportunities for flexible, cost-effective, and high-performance deployments. Achieving real-time and energy-efficient baseband processing on commercial off-the-shelf platforms, however, remains a critical challenge. This article explores how single instruction multiple data (SIMD) architectures can accelerate RAN workloads. We first outline why key physical-layer functions, such as channel estimation, multiple-input multiple-output (MIMO) detection, and forward error correction, are well aligned with SIMD's data-level parallelism. We then present practical design guidelines and prototype results, showing significant improvements in throughput and energy efficiency compared to conventional CPU-only processing, while retaining programmability and ease of integration. Finally, we discuss open challenges in workload balancing and hardware heterogeneity, and highlight the role of SIMD as an enabling technology for flexible, efficient, and sustainable 6G-ready RANs.