Recently, one-stage detectors have achieved competitive accuracy and faster
speed compared with traditional two-stage detectors on image data. However, in
the field of video object detection (VOD), most existing VOD methods are still
based on two-stage detectors. Moreover, directly adapting existing VOD methods
to one-stage detectors introduces unaffordable computational costs. In this
paper, we first analyse the computational bottlenecks of using one-stage
detectors for VOD. Based on the analysis, we present a simple yet efficient
framework to address the computational bottlenecks and achieve efficient
one-stage VOD by exploiting the temporal consistency in video frames.
Specifically, our method consists of a location-prior network to filter out
background regions and a size-prior network to skip unnecessary computations on
low-level feature maps for specific frames. We test our method on various
modern one-stage detectors and conduct extensive experiments on the ImageNet
VID dataset. Excellent experimental results demonstrate the superior
effectiveness, efficiency, and compatibility of our method. The code is
available at this https URL

我们提出了一个简单而高效的框架，通过利用视频帧中的时间一致性来解决计算上的瓶颈，从而实现了具有高效性的单阶段视频目标检测。

利用时间一致性进行高效的单阶段视频目标检测

Efficient One-stage Video Object Detection by Exploiting Temporal  Consistency

Mass spectrometry-based proteomics is a key enabler for personalized
healthcare, providing a deep dive into the complex protein compositions of
biological systems. This technology has vast applications in biotechnology and
biomedicine but faces significant computational bottlenecks. Current
methodologies often require multiple hours or even days to process extensive
datasets, particularly in the domain of spectral clustering. To tackle these
inefficiencies, we introduce SpecHD, a hyperdimensional computing (HDC)
framework supplemented by an FPGA-accelerated architecture with integrated
near-storage preprocessing. Utilizing streamlined binary operations in an HDC
environment, SpecHD capitalizes on the low-latency and parallel capabilities of
FPGAs. This approach markedly improves clustering speed and efficiency, serving
as a catalyst for real-time, high-throughput data analysis in future healthcare
applications. Our evaluations demonstrate that SpecHD not only maintains but
often surpasses existing clustering quality metrics while drastically cutting
computational time. Specifically, it can cluster a large-scale human proteome
dataset-comprising 25 million MS/MS spectra and 131 GB of MS data-in just 5
minutes. With energy efficiency exceeding 31x and a speedup factor that spans a
range of 6x to 54x over existing state of-the-art solutions, SpecHD emerges as
a promising solution for the rapid analysis of mass spectrometry data with
great implications for personalized healthcare.

利用 FPGA 加速架构的超维计算框架 SpecHD 有效解决了质谱数据处理的计算瓶颈问题，在保持聚类质量的同时将计算时间显著缩短，实现了大规模质谱数据的实时高通量分析，对个性化医疗具有重大影响。

SpecHD: FPGA 基于质谱聚类的高维计算框架

SpecHD: Hyperdimensional Computing Framework for FPGA-based Mass  Spectrometry Clustering

We study the practical performance of quantum-inspired algorithms for
recommendation systems and linear systems of equations. These algorithms were
shown to have an exponential asymptotic speedup compared to previously known
classical methods for problems involving low-rank matrices, but with complexity
bounds that exhibit a hefty polynomial overhead compared to quantum algorithms.
This raised the question of whether these methods were actually useful in
practice. We conduct a theoretical analysis aimed at identifying their
computational bottlenecks, then implement and benchmark the algorithms on a
variety of problems, including applications to portfolio optimization and movie
recommendations. On the one hand, our analysis reveals that the performance of
these algorithms is better than the theoretical complexity bounds would
suggest. On the other hand, their performance as seen in our implementation
degrades noticeably as the rank and condition number of the input matrix are
increased. Overall, our results indicate that quantum-inspired algorithms can
perform well in practice provided that stringent conditions are met: low rank,
low condition number, and very large dimension of the input matrix. By
contrast, practical datasets are often sparse and high-rank, precisely the type
that can be handled by quantum algorithms.

研究量子启发式算法在推荐系统和线性方程组方面的实际性能优化，证明这些算法可以应用于低秩矩阵问题的实际情境下表现良好，但当输入矩阵的秩或条件数增加时，他们的性能会明显降低。

量子启发式算法实践

Quantum-inspired algorithms in practice

One of the most compelling features of Gaussian process (GP) regression is
its ability to provide well-calibrated posterior distributions. Recent advances
in inducing point methods have sped up GP marginal likelihood and posterior
mean computations, leaving posterior covariance estimation and sampling as the
remaining computational bottlenecks. In this paper we address these
shortcomings by using the Lanczos algorithm to rapidly approximate the
predictive covariance matrix. Our approach, which we refer to as LOVE (LanczOs
Variance Estimates), substantially improves time and space complexity. In our
experiments, LOVE computes covariances up to 2,000 times faster and draws
samples 18,000 times faster than existing methods, all without sacrificing
accuracy.

本文提出一种基于 Lanczos 算法的方法 LOVE（LanczOs Variance Estimates）来解决高斯过程回归的计算瓶颈，大大提高了协方差的计算效率和采样速度。