395 research outputs found
LERC: Coordinated Cache Management for Data-Parallel Systems
Memory caches are being aggressively used in today's data-parallel frameworks
such as Spark, Tez and Storm. By caching input and intermediate data in memory,
compute tasks can witness speedup by orders of magnitude. To maximize the
chance of in-memory data access, existing cache algorithms, be it recency- or
frequency-based, settle on cache hit ratio as the optimization objective.
However, unlike the conventional belief, we show in this paper that simply
pursuing a higher cache hit ratio of individual data blocks does not
necessarily translate into faster task completion in data-parallel
environments. A data-parallel task typically depends on multiple input data
blocks. Unless all of these blocks are cached in memory, no speedup will
result. To capture this all-or-nothing property, we propose a more relevant
metric, called effective cache hit ratio. Specifically, a cache hit of a data
block is said to be effective if it can speed up a compute task. In order to
optimize the effective cache hit ratio, we propose the Least Effective
Reference Count (LERC) policy that persists the dependent blocks of a compute
task as a whole in memory. We have implemented the LERC policy as a memory
manager in Spark and evaluated its performance through Amazon EC2 deployment.
Evaluation results demonstrate that LERC helps speed up data-parallel jobs by
up to 37% compared with the widely employed least-recently-used (LRU) policy
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
Large Language Models (LLMs) have shown remarkable proficiency in language
understanding and have been successfully applied to a variety of real-world
tasks through task-specific fine-tuning or prompt engineering. Despite these
advancements, it remains an open question whether LLMs are fundamentally
capable of reasoning and planning, or if they primarily rely on recalling and
synthesizing information from their training data. In our research, we
introduce a novel task -- Minesweeper -- specifically designed in a format
unfamiliar to LLMs and absent from their training datasets. This task
challenges LLMs to identify the locations of mines based on numerical clues
provided by adjacent opened cells. Successfully completing this task requires
an understanding of each cell's state, discerning spatial relationships between
the clues and mines, and strategizing actions based on logical deductions drawn
from the arrangement of the cells. Our experiments, including trials with the
advanced GPT-4 model, indicate that while LLMs possess the foundational
abilities required for this task, they struggle to integrate these into a
coherent, multi-step logical reasoning process needed to solve Minesweeper.
These findings highlight the need for further research to understand and nature
of reasoning capabilities in LLMs under similar circumstances, and to explore
pathways towards more sophisticated AI reasoning and planning models.Comment: 24 pages, 5 figures, 3 table
Effect of Gelsemium elegans
Gelsemium elegans (GE) is a kind of well-known toxic plant. It can be detoxified by Mussaenda pubescens (MP), but the detoxification mechanism is still unclear. Thus, a detoxification herbal formula (GM) comprising GE and MP was derived. The Caco-2 cells monolayer model was used to evaluate GM effects on transporting six kinds of indole alkaloids of GE. The bidirectional transport studies demonstrated that absorbance percentage of indole alkaloids in GE increased linearly over time. But in GM, Papp (AP→BL) values of the most toxic members, gelsenicine, humantenidine, and gelsevirine, were lower than that of Papp (BL→AP) (P<0.05). The prominent analgesic effect members, gelsemine and koumine, were approximately 1.00 in γ values. Nowhere was this increasing efflux more pronounced than in the case of indole alkaloids with N-O structure. In the presence of verapamil, the γ values of humantenidine, gelsenicine, gelsevirine, and humantenine were decreased by 43.69, 41.42, 36.00, and 8.90 percent, respectively. The γ values in presence of ciclosporin were homologous with a decrease of 42.32, 40.59, 34.00, and 15.07 percent. It suggested that the efflux transport was affected by transporters. Taken together, due to the efflux transporters participation, the increasing efflux of indole alkaloids from GM was found in Caco-2 cells
LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
UNet and its variants have been widely used in medical image segmentation.
However, these models, especially those based on Transformer architectures,
pose challenges due to their large number of parameters and computational
loads, making them unsuitable for mobile health applications. Recently, State
Space Models (SSMs), exemplified by Mamba, have emerged as competitive
alternatives to CNN and Transformer architectures. Building upon this, we
employ Mamba as a lightweight substitute for CNN and Transformer within UNet,
aiming at tackling challenges stemming from computational resource limitations
in real medical settings. To this end, we introduce the Lightweight Mamba UNet
(LightM-UNet) that integrates Mamba and UNet in a lightweight framework.
Specifically, LightM-UNet leverages the Residual Vision Mamba Layer in a pure
Mamba fashion to extract deep semantic features and model long-range spatial
dependencies, with linear computational complexity. Extensive experiments
conducted on two real-world 2D/3D datasets demonstrate that LightM-UNet
surpasses existing state-of-the-art literature. Notably, when compared to the
renowned nnU-Net, LightM-UNet achieves superior segmentation performance while
drastically reducing parameter and computation costs by 116x and 21x,
respectively. This highlights the potential of Mamba in facilitating model
lightweighting. Our code implementation is publicly available at
https://github.com/MrBlankness/LightM-UNet
Real-Time Marker Localization Learning for GelStereo Tactile Sensing
Visuotactile sensing technology is becoming more popular in tactile sensing,
but the effectiveness of the existing marker detection localization methods
remains to be further explored. Instead of contour-based blob detection, this
paper presents a learning-based marker localization network for GelStereo
visuotactile sensing called Marknet. Specifically, the Marknet presents a grid
regression architecture to incorporate the distribution of the GelStereo
markers. Furthermore, a marker rationality evaluator (MRE) is modelled to
screen suitable prediction results. The experimental results show that the
Marknet combined with MRE achieves 93.90% precision for irregular markers in
contact areas, which outperforms the traditional contour-based blob detection
method by a large margin of 42.32%. Meanwhile, the proposed learning-based
marker localization method can achieve better real-time performance beyond the
blob detection interface provided by the OpenCV library through GPU
acceleration, which we believe will lead to considerable perceptual sensitivity
gains in various robotic manipulation tasks
Virtual sensing for gearbox condition monitoring based on extreme learning machine
Gearbox, as a critical component to convert speed and torque to maintain machinery normal operation in the industrial processes, has been received and still needs considerable attentions to ensure its reliable operation. Direct sensing and indirect sensing techniques are widely used for gearbox condition monitoring and fault diagnosis, but both have Pros and Cons. To bridge their gaps and enhance the performance of early fault diagnosis, this paper presents a new virtual sensing technique based on extreme learning machine (ELM) for gearbox degradation status estimation. By fusing the features extracted from indirect sensing measurements (e.g. in-process vibration measurement), ELM based virtual sensing model could infer the gearbox condition which was usually directly indicated by the direct sensing measurements (e.g. offline oil debris mass (ODM)). Different state-of-the-art dimension reduction techniques have been investigated for feature selection and fusion including principal component analysis (PCA) and its kernel version, locality preserving projection (LPP) method. The effectiveness of the presented virtual sensing technique is experimentally validated by the sensing measurements from a spiral bevel gear test rig. The experimental results show that the estimated gearbox condition by the virtual sensing model based on ELM and kernel PCA well follows the trend of truth data and presents the better performance over the support vector regression based virtual sensing scheme
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