382 research outputs found
Spatial spectrum and energy efficiency of random cellular networks
It is a great challenge to evaluate the network performance of cellular
mobile communication systems. In this paper, we propose new spatial spectrum
and energy efficiency models for Poisson-Voronoi tessellation (PVT) random
cellular networks. To evaluate the user access the network, a Markov chain
based wireless channel access model is first proposed for PVT random cellular
networks. On that basis, the outage probability and blocking probability of PVT
random cellular networks are derived, which can be computed numerically.
Furthermore, taking into account the call arrival rate, the path loss exponent
and the base station (BS) density in random cellular networks, spatial spectrum
and energy efficiency models are proposed and analyzed for PVT random cellular
networks. Numerical simulations are conducted to evaluate the network spectrum
and energy efficiency in PVT random cellular networks.Comment: appears in IEEE Transactions on Communications, April, 201
A New Action Recognition Framework for Video Highlights Summarization in Sporting Events
To date, machine learning for human action recognition in video has been
widely implemented in sports activities. Although some studies have been
successful in the past, precision is still the most significant concern. In
this study, we present a high-accuracy framework to automatically clip the
sports video stream by using a three-level prediction algorithm based on two
classical open-source structures, i.e., YOLO-v3 and OpenPose. It is found that
by using a modest amount of sports video training data, our methodology can
perform sports activity highlights clipping accurately. Comparing with the
previous systems, our methodology shows some advantages in accuracy. This study
may serve as a new clipping system to extend the potential applications of the
video summarization in sports field, as well as facilitates the development of
match analysis system.Comment: 18 pages, 3 figures, 4 table
Identification of diagnostic biomarkers in Alzheimer’s disease by integrated bioinformatic analysis and machine learning strategies
BackgroundAlzheimer’s disease (AD) is the most prevalent form of dementia, and is becoming one of the most burdening and lethal diseases. More useful biomarkers for diagnosing AD and reflecting the disease progression are in need and of significance.MethodsThe integrated bioinformatic analysis combined with machine-learning strategies was applied for exploring crucial functional pathways and identifying diagnostic biomarkers of AD. Four datasets (GSE5281, GSE131617, GSE48350, and GSE84422) with samples of AD frontal cortex are integrated as experimental datasets, and another two datasets (GSE33000 and GSE44772) with samples of AD frontal cortex were used to perform validation analyses. Functional Correlation enrichment analyses were conducted based on Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Reactome database to reveal AD-associated biological functions and key pathways. Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. The correlation analysis was performed to explore the correlation between the identified biomarkers with CDR scores and Braak staging.ResultsThe pathways of the immune response and oxidative stress were identified as playing a crucial role during AD. Thioredoxin interacting protein (TXNIP), early growth response 1 (EGR1), and insulin-like growth factor binding protein 5 (IGFBP5) were screened as diagnostic markers of AD. The diagnostic efficacy of TXNIP, EGR1, and IGFBP5 was validated with corresponding AUCs of 0.857, 0.888, and 0.856 in dataset GSE33000, 0.867, 0.909, and 0.841 in dataset GSE44770. And the AUCs of the combination of these three biomarkers as a diagnostic tool for AD were 0.954 and 0.938 in the two verification datasets.ConclusionThe pathways of immune response and oxidative stress can play a crucial role in the pathogenesis of AD. TXNIP, EGR1, and IGFBP5 are useful biomarkers for diagnosing AD and their mRNA level may reflect the development of the disease by correlation with the CDR scores and Breaking staging
Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model
With the emergence of large pre-trained vison-language model like CLIP,
transferable representations can be adapted to a wide range of downstream tasks
via prompt tuning. Prompt tuning tries to probe the beneficial information for
downstream tasks from the general knowledge stored in the pre-trained model. A
recently proposed method named Context Optimization (CoOp) introduces a set of
learnable vectors as text prompt from the language side. However, tuning the
text prompt alone can only adjust the synthesized "classifier", while the
computed visual features of the image encoder can not be affected , thus
leading to sub-optimal solutions. In this paper, we propose a novel
Dual-modality Prompt Tuning (DPT) paradigm through learning text and visual
prompts simultaneously. To make the final image feature concentrate more on the
target visual concept, a Class-Aware Visual Prompt Tuning (CAVPT) scheme is
further proposed in our DPT, where the class-aware visual prompt is generated
dynamically by performing the cross attention between text prompts features and
image patch token embeddings to encode both the downstream task-related
information and visual instance information. Extensive experimental results on
11 datasets demonstrate the effectiveness and generalization ability of the
proposed method. Our code is available in https://github.com/fanrena/DPT.Comment: 12 pages, 7 figure
Ground-to-Aerial Person Search: Benchmark Dataset and Approach
In this work, we construct a large-scale dataset for Ground-to-Aerial Person
Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding
boxes for 2,644 identities appearing in both of the UAVs and ground
surveillance cameras. To our knowledge, this is the first dataset for
cross-platform intelligent surveillance applications, where the UAVs could work
as a powerful complement for the ground surveillance cameras. To more
realistically simulate the actual cross-platform Ground-to-Aerial surveillance
scenarios, the surveillance cameras are fixed about 2 meters above the ground,
while the UAVs capture videos of persons at different location, with a variety
of view-angles, flight attitudes and flight modes. Therefore, the dataset has
the following unique characteristics: 1) drastic view-angle changes between
query and gallery person images from cross-platform cameras; 2) diverse
resolutions, poses and views of the person images under 9 rich real-world
scenarios. On basis of the G2APS benchmark dataset, we demonstrate detailed
analysis about current two-step and end-to-end person search methods, and
further propose a simple yet effective knowledge distillation scheme on the
head of the ReID network, which achieves state-of-the-art performances on both
of the G2APS and the previous two public person search datasets, i.e., PRW and
CUHK-SYSU. The dataset and source code available on
\url{https://github.com/yqc123456/HKD_for_person_search}.Comment: Accepted by ACM MM 202
Iron and zinc binding activity of Escherichia coli topoisomerase I homolog YrdD
YrdD, a homolog of the C-terminal zinc-binding region of Escherichia coli topoisomerase I, is highly conserved among proteobacteria and enterobacteria. However, the function of YrdD remains elusive. Here we report that YrdD purified from E. coli cells grown in LB media contains both zinc and iron. Supplement of exogenous zinc in the medium abolishes the iron binding of YrdD in E. coli cells, indicating that iron and zinc may compete for the same metal binding sites in the protein. While the zinc-bound YrdD is able to bind single-stranded (ss) DNA and protect ssDNA from the DNase I digestion in vitro, the iron-bound YrdD has very little or no binding activity for ssDNA, suggesting that the zinc-bound YrdD may have an important role in DNA repair by interacting with ssDNA in cells. © 2014 Springer Science+Business Media
Copper binding in IscA inhibits iron-sulphur cluster assembly in Escherichia coli
© 2014 John Wiley & Sons Ltd. Among the iron-sulphur cluster assembly proteins encoded by gene cluster iscSUA-hscBA-fdx in Escherichia coli, IscA has a unique and strong iron binding activity and can provide iron for iron-sulphur cluster assembly in proteins in vitro. Deletion of IscA and its paralogue SufA results in an E. coli mutant that fails to assemble [4Fe-4S] clusters in proteins under aerobic conditions, suggesting that IscA has a crucial role for iron-sulphur cluster biogenesis. Here we report that among the iron-sulphur cluster assembly proteins, IscA also has a strong and specific binding activity for Cu(I) in vivo and in vitro. The Cu(I) centre in IscA is stable and resistant to oxidation under aerobic conditions. Mutation of the conserved cysteine residues that are essential for the iron binding in IscA abolishes the copper binding activity, indicating that copper and iron may share the same binding site in the protein. Additional studies reveal that copper can compete with iron for the metal binding site in IscA and effectively inhibits the IscA-mediated [4Fe-4S] cluster assembly in E. coli cells. The results suggest that copper may not only attack the [4Fe-4S] clusters in dehydratases, but also block the [4Fe-4S] cluster assembly in proteins by targeting IscA in cells. Copyrigh
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