549 research outputs found
An Email Attachment is Worth a Thousand Words, or Is It?
There is an extensive body of research on Social Network Analysis (SNA) based
on the email archive. The network used in the analysis is generally extracted
either by capturing the email communication in From, To, Cc and Bcc email
header fields or by the entities contained in the email message. In the latter
case, the entities could be, for instance, the bag of words, url's, names,
phones, etc. It could also include the textual content of attachments, for
instance Microsoft Word documents, excel spreadsheets, or Adobe pdfs. The nodes
in this network represent users and entities. The edges represent communication
between users and relations to the entities. We suggest taking a different
approach to the network extraction and use attachments shared between users as
the edges. The motivation for this is two-fold. First, attachments represent
the "intimacy" manifestation of the relation's strength. Second, the
statistical analysis of private email archives that we collected and Enron
email corpus shows that the attachments contribute in average around 80-90% to
the archive's disk-space usage, which means that most of the data is presently
ignored in the SNA of email archives. Consequently, we hypothesize that this
approach might provide more insight into the social structure of the email
archive. We extract the communication and shared attachments networks from
Enron email corpus. We further analyze degree, betweenness, closeness, and
eigenvector centrality measures in both networks and review the differences and
what can be learned from them. We use nearest neighbor algorithm to generate
similarity groups for five Enron employees. The groups are consistent with
Enron's organizational chart, which validates our approach.Comment: 12 pages, 4 figures, 7 tables, IML'17, Liverpool, U
The Implications of Diverse Applications and Scalable Data Sets in Benchmarking Big Data Systems
Now we live in an era of big data, and big data applications are becoming
more and more pervasive. How to benchmark data center computer systems running
big data applications (in short big data systems) is a hot topic. In this
paper, we focus on measuring the performance impacts of diverse applications
and scalable volumes of data sets on big data systems. For four typical data
analysis applications---an important class of big data applications, we find
two major results through experiments: first, the data scale has a significant
impact on the performance of big data systems, so we must provide scalable
volumes of data sets in big data benchmarks. Second, for the four applications,
even all of them use the simple algorithms, the performance trends are
different with increasing data scales, and hence we must consider not only
variety of data sets but also variety of applications in benchmarking big data
systems.Comment: 16 pages, 3 figure
Research on Temperature Field of the Support Structure for the Independent LNG Tank
The independent LNG (Liquified Nature Gas) containment is widely used for small or medium-sized LNG carrier and ship using LNG as fuels. The common tank pattern includes single-spherical-cylindrical tank and double-spherical-cylindrical tank, which is the key to design the hull structure and its support. The support is designed to connect the hull structure and LNG tank. Its main functions are heat transferring and force loading. This paper focus on the temperature field distribution of hull and its support structure. The thermal boundary conditions are simulated according to the heat transfer action, such as thermal convection, heat conduction and thermal radiation. The method on how to carry out thermal analysis is presented for an independent LNG containment. The case study is carried out with two typical independent LNG tanks. One is a tank with double spherical cylindrical in the LNG carrier, and the other is a tank with single spherical cylindrical on the deck of the ship using LNG as fuels. The result shows the method presented in this paper is a good reference for the structural design with independent LNG containment
Associations of systemic inflammatory markers with the risks of chronic heart failure: A case-control study
Objective: As a greater proportion of patients survived their initial cardiac insult, Chronic Heart Failure (CHF) is becoming a major cause of worldwide morbidity and mortality. However, the mechanism underlying the inflammation in patients with CHF has not yet been elaborated. This study aims to explore the associations between inflammation and CHF patients, and the predictive performance of inflammatory indicators in identifying patients with CHF.
Methods: A matched case-control study was conducted by recruiting 385 patients who were diagnosed with CHF from January 2018 to December 2019 in The First Affiliated Hospital of Chongqing Medical University. Each CHF patient was matched against one control subject without CHF on the criteria of age, sex, Body Mass Index (BMI), smoking status, and comorbidities. The clinical data and systemic inflammatory indicators were compared between the two groups, independent risk factors of CHF were identified by multivariate regression analysis, and the predictive values of systemic inflammatory indicators for CHF were analyzed by Receiver Operating Characteristic (ROC) curve analysis.
Results: After processed in the univariate and multivariate regression analysis models, three systemic inflammatory indicators (hs-CRP [high sensitivity C Reactive Protein], LMR [lymphocyte-to-monocyte ratio], and Monocyte-to-High-density-lipoprotein Ratio [MHR]) were considered as independent predictors of CHF, among which the hs-CRP exhibited the best predictive performance (AUC = 0.752, 95%CI 0.717‒0.786, p < 0.001), followed by LMR (AUC = 0.711, 95% CI 0.675‒0.747, p < 0.001) and MHR (AUC = 0.673, 95% CI 0.635‒0.710, p < 0.001). The three-indicator combination showed an improved diagnostic performance (AUC = 0.757, 95% CI 0.724‒0.791, p < 0.001). In addition, the results of subgroup comparisons demonstrated that hs-CRP and MHR were associated with the severity of CHF (p < 0.001).
Conclusions: The systemic inflammatory indicators such as hs-CRP, LMR, and MHR were independently correlated with the attack of CHF and might be the complementary markers of the diagnosis of CHF
An Implementation of Multimodal Fusion System for Intelligent Digital Human Generation
With the rapid development of artificial intelligence (AI), digital humans
have attracted more and more attention and are expected to achieve a wide range
of applications in several industries. Then, most of the existing digital
humans still rely on manual modeling by designers, which is a cumbersome
process and has a long development cycle. Therefore, facing the rise of digital
humans, there is an urgent need for a digital human generation system combined
with AI to improve development efficiency. In this paper, an implementation
scheme of an intelligent digital human generation system with multimodal fusion
is proposed. Specifically, text, speech and image are taken as inputs, and
interactive speech is synthesized using large language model (LLM), voiceprint
extraction, and text-to-speech conversion techniques. Then the input image is
age-transformed and a suitable image is selected as the driving image. Then,
the modification and generation of digital human video content is realized by
digital human driving, novel view synthesis, and intelligent dressing
techniques. Finally, we enhance the user experience through style transfer,
super-resolution, and quality evaluation. Experimental results show that the
system can effectively realize digital human generation. The related code is
released at https://github.com/zyj-2000/CUMT_2D_PhotoSpeaker
Architectural Implications of GNN Aggregation Programming Abstractions
Graph neural networks (GNNs) have gained significant popularity due to the
powerful capability to extract useful representations from graph data. As the
need for efficient GNN computation intensifies, a variety of programming
abstractions designed for optimizing GNN Aggregation have emerged to facilitate
acceleration. However, there is no comprehensive evaluation and analysis upon
existing abstractions, thus no clear consensus on which approach is better. In
this letter, we classify existing programming abstractions for GNN Aggregation
by the dimension of data organization and propagation method. By constructing
these abstractions on a state-of-the-art GNN library, we perform a thorough and
detailed characterization study to compare their performance and efficiency,
and provide several insights on future GNN acceleration based on our analysis.Comment: 4 pages, to be published in IEEE Computer Architecture Letters (CAL
Geometry-Aware Video Quality Assessment for Dynamic Digital Human
Dynamic Digital Humans (DDHs) are 3D digital models that are animated using
predefined motions and are inevitably bothered by noise/shift during the
generation process and compression distortion during the transmission process,
which needs to be perceptually evaluated. Usually, DDHs are displayed as 2D
rendered animation videos and it is natural to adapt video quality assessment
(VQA) methods to DDH quality assessment (DDH-QA) tasks. However, the VQA
methods are highly dependent on viewpoints and less sensitive to geometry-based
distortions. Therefore, in this paper, we propose a novel no-reference (NR)
geometry-aware video quality assessment method for DDH-QA challenge. Geometry
characteristics are described by the statistical parameters estimated from the
DDHs' geometry attribute distributions. Spatial and temporal features are
acquired from the rendered videos. Finally, all kinds of features are
integrated and regressed into quality values. Experimental results show that
the proposed method achieves state-of-the-art performance on the DDH-QA
database
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