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Spikesorting is crucial in studying neural individually and synergistically encoding and decoding behaviors. However, existent spike sorting algorithms perform unsatisfactorily in real scenarios where heavy noises and overlapping samples are commonly in the spikes, and the spikes from different neurons are similar. To address such challenging scenarios, we propose an automatic spike sporting method in this paper, which integrally combines low-rank and sparse representation (LRSR) into a unified model. In particular, LRSR models spikes through low-rank optimization, uncovering global data structure for handling similar and overlapped samples. To eliminate the influence of the embedded noises, LRSR uses a sparse constraint, effectively separating spikes from noise. The optimization is solved using alternate augmented Lagrange multipliers methods. Moreover, we conclude with an automatic spike-sorting framework that employs the spectral clustering theorem to estimate the number of neurons. Extensive experiments over various simulated and real-world datasets demonstrate that our proposed method, LRSR, can handle spike sorting effectively and efficiently.

This article introduces a versatile multi-task learning framework (UMT-Net) and an adaptive task weighting (ATW) training method, specifically designed for resource-constrained scenarios that demand parameter-efficient networks. The adaptable UMT-Net architecture includes a global-shared backbone based on an encoder, task-specific self-attention modules, inter-task joint-attention fusion modules, and feature-aggregating decoders. The ATW technique accounts for both short-term variation and long-term statistics of task losses, leading to a more stable training process. Extensive experiments on CityScapes and NYUv2 datasets reveal that UMT-Net outperforms baseline methods while requiring fewer computations, model parameters, and inference latency. In addition, we also conducted experiments on the autonomous driving dataset BDD100 K and achieved state-of-the-art performance. Furthermore, we deployed the model and carried out tests for generalization in real-world scenarios. Finally, our network architecture possesses the capability to be designed as a compact model with much fewer parameters, computational requirements, and inference time, while maintaining competitive performance, making it suitable for deployment on mobile devices.

The convergence of HPC and AI has brought about a diversification of precision, posing significant hardware implementation challenges. This brief aims to address this issue by presenting a low-cost floating-point (FP) fused multiply-add (FMA) unit that is capable of supporting a wide range of FP formats. For the fewer-than-64-bit formats, this innovative FMA unit performs standard or mixed-precision operations fully pipelined in parallel for SP, TF32, BF16, and HP formats. For the 64-bit DP format, the FMA and ADD operations, whether independent or data-related, can be organized into package operations that are executed in two consecutive cycles to eliminate pipeline stall and then improve performance. The proposed FMA unit utilizes iteration and hardware vectorization methods to balance between cost and performance. Compared to a conventional DP FMA unit, the proposed design not only supports a wider range of FP formats and functions but also achieves higher performance with less cost. It can improve performance up to 1.5x more than the dual-mode FMA unit when performing HPC-AI applications.

Ferrofluids are oil-based liquids containing magnetic particles that interact with magnetic fields without solidifying. Leveraging the exploration of new applications of these promising materials (such as in optics, medicine and engineering) requires high fidelity modeling and simulation capabilities in order to accurately explore ferrofluids in silico. While recent work addressed the macroscopic simulation of large-scale ferrofluids using smoothed-particle hydrodynamics (SPH), such simulations are computationally expensive. In their work, the Kelvin force model has been used to calculate interactions between different SPH particles. The application of this model results in a force pointing outwards with respect to the fluid surface causing significant levitation problems. This drawback limits the application of more advanced and efficient SPH frameworks such as divergence-free SPH (DFSPH) or implicit incompressible SPH (IISPH). In this contribution, we propose a current loop magnetic force model which enables the fast macroscopic simulation of ferrofluids. Our new force model results in a force term pointing inwards allowing for more stable and fast simulations of ferrofluids using DFSPH and IISPH.