196 research outputs found
Monitoring the edges of product networks using distances
Foucaud {\it et al.} recently introduced and initiated the study of a new
graph-theoretic concept in the area of network monitoring. Let be a graph
with vertex set , a subset of , and be an edge in ,
and let be the set of pairs such that where and . is called a
\emph{distance-edge-monitoring set} if every edge of is monitored by
some vertex of , that is, the set is nonempty. The {\em
distance-edge-monitoring number} of , denoted by , is
defined as the smallest size of distance-edge-monitoring sets of . For two
graphs of order , respectively, in this paper we prove that
, where is the Cartesian
product operation. Moreover, we characterize the graphs attaining the upper and
lower bounds and show their applications on some known networks. We also obtain
the distance-edge-monitoring numbers of join, corona, cluster, and some
specific networks.Comment: 19 page
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition
Contrastive learning has been applied to Human Activity Recognition (HAR)
based on sensor data owing to its ability to achieve performance comparable to
supervised learning with a large amount of unlabeled data and a small amount of
labeled data. The pre-training task for contrastive learning is generally
instance discrimination, which specifies that each instance belongs to a single
class, but this will consider the same class of samples as negative examples.
Such a pre-training task is not conducive to human activity recognition tasks,
which are mainly classification tasks. To address this problem, we follow
SimCLR to propose a new contrastive learning framework that negative selection
by clustering in HAR, which is called ClusterCLHAR. Compared with SimCLR, it
redefines the negative pairs in the contrastive loss function by using
unsupervised clustering methods to generate soft labels that mask other samples
of the same cluster to avoid regarding them as negative samples. We evaluate
ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR,
using mean F1-score as the evaluation metric. The experiment results show that
it outperforms all the state-of-the-art methods applied to HAR in
self-supervised learning and semi-supervised learning.Comment: 11 pages, 5 figure
MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer
The massive generation of time-series data by largescale Internet of Things
(IoT) devices necessitates the exploration of more effective models for
multivariate time-series forecasting. In previous models, there was a
predominant use of the Channel Dependence (CD) strategy (where each channel
represents a univariate sequence). Current state-of-the-art (SOTA) models
primarily rely on the Channel Independence (CI) strategy. The CI strategy
treats all channels as a single channel, expanding the dataset to improve
generalization performance and avoiding inter-channel correlation that disrupts
long-term features. However, the CI strategy faces the challenge of
interchannel correlation forgetting. To address this issue, we propose an
innovative Mixed Channels strategy, combining the data expansion advantages of
the CI strategy with the ability to counteract inter-channel correlation
forgetting. Based on this strategy, we introduce MCformer, a multivariate
time-series forecasting model with mixed channel features. The model blends a
specific number of channels, leveraging an attention mechanism to effectively
capture inter-channel correlation information when modeling long-term features.
Experimental results demonstrate that the Mixed Channels strategy outperforms
pure CI strategy in multivariate time-series forecasting tasks
miRā155 promotes macrophage pyroptosis induced by Porphyromonas gingivalis through regulating the NLRP3 inflammasome
ObjectiveThe aim of this study is to detect pyroptosis in macrophages stimulated with Porphyromonas gingivalis and elucidate the mechanism by which P.Ā gingivalis induces pyroptosis in macrophages.MethodsThe immortalized human monocyte cell line U937 was stimulated with P.Ā gingivalis W83. Flow cytometry was carried out to detect pyroptosis in macrophages. The expression of miRā155 was detected by realātime PCR and inhibited using RNAi. Suppressor of cytokine signaling (SOCS) 1, cleaved GSDMD, caspase (CAS)ā1, caspaseā11, apoptosisāassociated speckālike protein (ASC), and NODālike receptor protein 3 (NLRP3) were detected by Western blotting, and ILā1Ī² and ILā18 were detected by ELISA.ResultsThe rate of pyroptosis in macrophages and the expression of miRā155 increased upon stimulation with P.Ā gingivalis and pyroptosis rate decreased when miRā155 was silenced. GSDMDāNT, CASā11, CASā1, ASC, NLRP3, ILā1Ī², and ILā18 levels increased, but SOCS1 decreased in U937 cells after stimulated with P.Ā gingivalis. These changes were weakened in P.Ā gingivalisāstimulated U937 macrophages transfected with lentiviruses carrying miRā155 shRNA compared to those transfected with nonātargeting control sequence. However, there was no significant difference in ASC expression between P.Ā gingivalisāstimulated shCont and shMiRā155 cells.ConclusionsPorphyromonas gingivalis promotes pyroptosis in macrophages during early infection. miRā155 is involved in this process through regulating the NLRP3 inflammasome.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152887/1/odi13198_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152887/2/odi13198.pd
A SialidaseāDeficient Porphyromonas gingivalis Mutant Strain Induces Less Interleukinā1Ī² and Tumor Necrosis FactorāĪ± in Epi4 Cells Than W83 Strain Through Regulation of cāJun NāTerminal Kinase Pathway
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142178/1/jpere129.pd
Detection and exposure assessment of pesticide residues in leek in Heānan Province
ObjectiveTo evaluate the health risk of pesticide exposure from leekļ¼ the pesticide residue in leek from Henan market was investigated.MethodsThe residues of 16 pesticides in leek sold on Henan market in 2020 were detected and analyzed. According to health guidance values such as food consumption data of the World Health Organizationļ¼ acute reference dose formulated by Joint Meeting on Pesticide Residues and adaptable daily intake in āNational food safety standard-Maximum residue limits for pesticides in foodāļ¼ the acute and chronic exposure risks of pesticide residues in leek were evaluated by point assessment methodļ¼ and the cumulative exposure was evaluated by hazard index method.ResultsThere were many types of pesticide residues in leek samples and 93.81% ļ¼424/452ļ¼ of the samples were positive. 7 of the 14 pesticides exceeded their MRLsļ¼ and the violation rate of all samples was 16.15%. The detection of multiple pesticides was relatively seriousļ¼ and 56.42% of the samples contained more than two pesticide residues. In the acute exposure assessmentļ¼ the acute risks of carbofuranļ¼ procymidone and phorate exceeded the acceptable level. In the chronic exposure assessmentļ¼ the chronic risk of omethoate exceeded the acceptable level. And insecticide pesticides had cumulative poisoning risk.ConclusionThe situation of pesticide residues in leek in Henan province was relatively prominent. To ensure the safety of agricultural productsļ¼ it was recommended that the routine monitoring and use of pesticideļ¼ especially high-risk pesticides such as omethoateļ¼ carbofuranļ¼ procymidone and phorate should be strengthened
Pruning random resistive memory for optimizing analogue AI
The rapid advancement of artificial intelligence (AI) has been marked by the
large language models exhibiting human-like intelligence. However, these models
also present unprecedented challenges to energy consumption and environmental
sustainability. One promising solution is to revisit analogue computing, a
technique that predates digital computing and exploits emerging analogue
electronic devices, such as resistive memory, which features in-memory
computing, high scalability, and nonvolatility. However, analogue computing
still faces the same challenges as before: programming nonidealities and
expensive programming due to the underlying devices physics. Here, we report a
universal solution, software-hardware co-design using structural
plasticity-inspired edge pruning to optimize the topology of a randomly
weighted analogue resistive memory neural network. Software-wise, the topology
of a randomly weighted neural network is optimized by pruning connections
rather than precisely tuning resistive memory weights. Hardware-wise, we reveal
the physical origin of the programming stochasticity using transmission
electron microscopy, which is leveraged for large-scale and low-cost
implementation of an overparameterized random neural network containing
high-performance sub-networks. We implemented the co-design on a 40nm 256K
resistive memory macro, observing 17.3% and 19.9% accuracy improvements in
image and audio classification on FashionMNIST and Spoken digits datasets, as
well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE
datasets, respectively. This is accompanied by 82.1%, 51.2%, and 99.8%
improvement in energy efficiency thanks to analogue in-memory computing. By
embracing the intrinsic stochasticity and in-memory computing, this work may
solve the biggest obstacle of analogue computing systems and thus unleash their
immense potential for next-generation AI hardware
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