Miracle-dz/UniFAR-FTUs huggingface dataset:
A generalized parametric 3D shape representation for articulated pose estimation

We present a novel parametric 3D shape representation, Generalized sum of Gaussians (G-SoG), which is particularly suitable for pose estimation of articulated objects. Compared with the original sum-of-Gaussians (SoG), G-SoG can handle both isotropic and anisotropic Gaussians, leading to a more flexible and adaptable shape representation yet with much fewer anisotropic Gaussians involved. An articulated shape template can be developed by embedding G-SoG in a tree-structured skeleton model to represent an articulated object. We further derive a differentiable similarity function between G-SoG (the template) and SoG (observed data) that can be optimized analytically for efficient pose estimation. The experimental results on a standard human pose estimation dataset show the effectiveness and advantages of G-SoG over the original SoG as well as the promise compared with the recent algorithms that use more complicated shape models.
Using abuse frames to bound the scope of security problems

Security problems arise from the concern for protecting assets from security threats. In a systems development process, the security protection of a system is specified by security requirements, identified from the analysis of the threats to the system. However, as it is often not possible to obtain a full system description until late in the RE process, a security problem often has to be described in the context of a bounded scope, that is, one containing only the domains relevant to some part of the functionality of the full system. By binding the scope of a security problem, it can be described more explicitly and precisely, thereby facilitating the identification and analysis of threats, which in turn drive the elicitation and elaboration of security requirements. In this poster, we elaborate on an approach we developed based on abuse frames and suggest how it can provide a means for structuring and bounding the scope security problems.
Movie genre preference prediction using machine learning for customer-based information

This work introduces a movie genre preference predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from one thousand customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.
LDassoc: an online tool for interactively exploring genome-wide association study results and prioritizing variants for functional investigation

Motivation Existing approaches to plot association results from genome-wide association studies (GWAS) are in the form of static Manhattan plots and often lack data integration with rich databases on variant regulatory potential as well as population-specific linkage disequilibrium patterns. Summary We created an intuitive web module for uploading and efficiently exploring GWAS association results. Interactive plots and sortable tables allow researchers to query genomic regions of interest, facilitating the integration of data on linkage disequilibrium, variant regulatory potential and potential target genes. External links allow for visualization of association results in the UCSC genome browser as well as easy access to publically available databases (e.g. dbSNP and RegulomeDB). Through improved visualization and data integration, LDassoc offers genomic researchers a specialized environment to examine association signals and suggests variants for functional investigation. Availability and implementation LDassoc is a free and publically available web tool which can be accessed online at https://analysistools.nci. nih.gov/LDlink/? tab=ldassoc. Contact mitchell.machiela@nih.gov.
Comprehensive analysis of differentially expressed profiles of lncRNAs and circRNAs with associated co-expression and ceRNA networks in bladder carcinoma

Accumulating evidences indicate that long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) play important roles in tumorigenesis. However, the mechanisms remain largely unknown. To explore lncRNAs and circRNAs expression profiling and their biological functions in bladder cancer, we surveyed the lncRNA/circRNA and mRNA expression profiles of bladder cancer and para-cancer tissues using microarray for four patients. Thousands of significantly changed lncRNAs and mRNAs as well as hundreds of circRNAs were identified. Five dysregulated lncRNAs and four mRNAs were confirmed by quantitative real-time PCR in 30 pairs of samples. GO and KEGG pathway enrichment analyses were executed to determine the principal functions of the significantly deregulated genes. Further more, we constructed correlated expression networks including coding-noncoding co-expression (CNC), competing endogenous RNAs (ceRNA), cis regulation, lncRNAs-transcription factor (TF)-mRNA with bioinformatics methods. Co-expression analysis showed lncRNA APLP2 expression is correlated with apoptosis-related genes, including PTEN and TP53INP1. CeRNA network inferred that lncRNA H19 and circRNA MYLK could bind competitively with miRNA-29a-3p increasing target gene DNMT3B, VEGFA and ITGB1 expressions. Moreover, the nearby genes pattern displayed that overexpressing ADAM2 and C8orf4 are cis-regulated by lncRNA RP11-359E19.2, involving in progression of bladder cancer. In addition, lncRNAs-TF-mRNA diagram indicated that lncRNA BC041488 could trans-regulate CDK1 mRNA expression through SRF transcription factor. Taken together, these results suggested lncRNAs and circRNAs could implicate in the pathogenesis and development of bladder cancer. Our findings provide a novel perspective on lncRNAs and circRNAs and lay the foundation for future research of potential roles of lncRNAs and circRNAs in bladder carcinoma.
0.3-THz SiGe-Based High-Efficiency Push–Push VCOs With > 1-mW Peak Output Power Employing Common-Mode Impedance Enhancement

We present a novel method of maximizing the output power and efficiency of millimeter-wave and terahertz signal sources, which are based on the push-push topology. In this method, the common-mode impedance of a differential Colpitts oscillator operating in the odd mode is maximized by introducing a fixed-valued capacitor (<inline-formula> <tex-math notation="LaTeX">$C_{r}$ </tex-math></inline-formula>) at the common-base node. This capacitor is designed to introduce a common-mode parallel resonance at the desired second harmonic, boosting the common-mode voltage swing and subsequently its output power. The proposed method is analyzed using a high-frequency even-mode <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula>-model. Analytical expressions of input impedance are derived and are used for calculating the common-mode resonance frequency and the required value of <inline-formula> <tex-math notation="LaTeX">$C_{r}$ </tex-math></inline-formula>. Two 0.3-THz voltage-controlled oscillators (VCOs) are implemented in a 130-nm SiGe BiCMOS process. It is shown that by using the proposed technique, the output power is improved by more than 6 dB, as compared with the conventional approaches. The implemented VCOs work from 292 to 318 GHz and 305 to 327 GHz, delivering a peak output power of 0.6 and 0.2 dBm, with a dc-to-RF efficiency of 0.8% and 0.9%, and can achieve a phase noise of −108 and −105 dBc/Hz at 10-MHz offset, respectively. As compared with the prior state-of-the-art Si-based tunable signal sources and arrays working above 270 GHz, this paper shows the lowest phase noise and the best figure-of-merit, while having an excellent output power, a tuning range, and a dc-to-RF efficiency.
Cancer-specific self-efficacy and psychosocial and functional adaptation to early stage breast cancer

Background: Although self-efficacy is considered a key psychological resource in adapting to chronic physical illness, this construct has received less attention among individuals coping with cancer.Purpose: To examine changes in cancer self-efficacy over time among women with early stage breast cancer and associations between task-specific domains of selfefficacy and specific psychological, relationship, and functional outcomes.Methods: Ninety-five women diagnosed with early stage breast cancer completed surveys postsurgery and 1 year later.Results: Cancer-related self-efficacy was relatively stable over 1 year, with only 2 domains of efficacy—(a) Activity Management and (b) Self-Satisfaction—evidencing significant increases over the 1-year time period. Cross-sectional findings were relatively consistent with predictions and suggested that specific domains of self-efficacy were more strongly related to relevant domains of adaptation. Longitudinal findings were not as consistent with the domain-specificity hypothesis but did suggest several predictive associations between self-efficacy and outcomes. Personal Management self-efficacy was associated with higher relationship satisfaction, higher Communication Self-Efficacy was associated with less functional impairment, and higher Affective Management self-efficacy was associated with higher self-esteem 1 year later.Conclusions: Specific domains of cancer-related self-efficacy are most closely related to relevant areas of adaptation when considered cross-sectionally, but further study is needed to clarify the nature of these relationships over time.
Enabling Strong Database Integrity using Trusted Execution Environments

Immutable and consistent sharing of data across organization boundaries enabl es a new class of applications. Because conventional datastores cannot provide this functionality, blockchain s have been proposed as one possible solution. Yet public blockchains are energy inefficient, hard to scale and suffer from limited throughput and high latencies, while permissioned blockchains depend on specially designated nodes, potentially le ak meta-information, and also suffer from scale and performance bottlenecks. This paper presents CreDB, a datastore that provides blockchain-like guarante es of integrity using trusted execution environments. CreDB employs three novel mechanisms to support a new class of applications. First, it creates a permanent record of every transaction, known as a witness , that clients can then use not only to audit the database, but to prove to thi rd parties that desired actions took place. Second, it associates with every object an inseparable and inviolable securit y policy, which not only performs access control but enables the datastore to i mplement state machines whose behavior is amenable to analysis. Finally, CreDB provides a protected function evaluation mechanism that allows integrity-protected computation over private data. The paper describes these mechanisms, and the applications they collectively enable, in detail. We have fully implemented CreDB on Intel SGX. Evaluation shows that these new mechanisms do not adversely affect performance.
A DDS-Based Energy Management Framework for Small Microgrid Operation and Control

The smart grid is seen as a power system with real-time communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper, a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service Ethernet network with appropriate quality-of-service profiles. The developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida, and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles.
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching

We present PPFNet - Point Pair Feature NETwork for deeply learning a globally informed 3D local feature descriptor to find correspondences in unorganized point clouds. PPFNet learns local descriptors on pure geometry and is highly aware of the global context, an important cue in deep learning. Our 3D representation is computed as a collection of point-pair-features combined with the points and normals within a local vicinity. Our permutation invariant network design is inspired by PointNet and sets PPFNet to be ordering-free. As opposed to voxelization, our method is able to consume raw point clouds to exploit the full sparsity. PPFNet uses a novel N-tuple loss and architecture injecting the global information naturally into the local descriptor. It shows that context awareness also boosts the local feature representation. Qualitative and quantitative evaluations of our network suggest increased recall, improved robustness and invariance as well as a vital step in the 3D descriptor extraction performance.
Self-Propelled Micromotors Monitored by Particle-Electrode Impact Voltammetry

Self-propelled micromotors, as the next frontier of smart miniaturized machines, are able to carry out a plethora of tasks and operations. To date, the evaluation of their capabilities and behavior has been carried out by optical microscopy coupled with computationally intensive imaging software. The use of particle-electrode impact voltammetry serves as an alternative means of quantifying and qualifying their locomotion. Perturbances of the diffusion layer at the electrode interface from the locomotion of the micromotor in solution resulted in spikes that were recorded by electrochemical signals. The number of micromotors and velocities of these self-propelled objects could be evaluated and examined in real-time in situ. This phenomenon was illustrated with Janus silver micromotors and tubular Cu/Pt micromotors. An on-site tracking of self-powered miniaturized entities propagating in solution serves as an alternative means for monitoring during their operations in environmental and biological applications.
Quality of reporting in abstracts of RCTs published in emergency medicine journals: a protocol for a systematic survey of the literature

Introduction The quality of reporting of abstracts of randomised controlled trials (RCTs) in major general medical journals and in some category-specific journals was shown to be poor before the publication of the ConsolidatedStandards of ReportingTrials (CONSORT) extension for abstracts in 2008, and an improvement in the quality of reporting of abstracts was observed after its publication. The effect of the publication of the CONSORT extension for abstracts on the quality of reporting of RCTs in emergency medicine journals has not been studied. In this paper, we present the protocol of a systematic survey of the literature, aimed at assessing the quality of reporting in abstracts of RCTs published in emergency medicine journals and at evaluating the effect of the publication of the CONSORT extension for abstracts on the quality of reporting. Methods and analysis The Medline database will be searched for RCTs published in the years 2005–2007 and 2014–2015 in the top 10 emergency medicine journals, according to their impact factor. Candidate studies will be screened for inclusion in the review. Exclusion criteria will be the following: the abstract is not available, they are published only as abstracts, still recruiting, or duplicate publications. The study outcomes will be the overall quality of reporting (number of items reported) according to the CONSORT extension and the compliance with its individual items. Two independent reviewers will screen each article for inclusion and will extract data on the CONSORT items and on other variables, which can possibly affect the quality of reporting. Ethics and dissemination This is a library-based study and therefore exempt from research ethics board review. The review results will be disseminated through abstract submission to conferences and publication in a peer-reviewed biomedical journal.
Identifying the Role of Information Systems in Achieving Energy-Related Environmental Sustainability using Text Mining

Environmental sustainability is one of the most important topics of our time. Energy consumption is a significant aspect of environmental sustainability, as it is a major source of pollution. In order to determine how information systems can assist organizations in reducing energy consumption, we first identify energy-related activities of organizations by examining the content of 4,999 sustainability reports that communicate corporate activities directed toward sustainability. We use latent semantic analysis (LSA) to extract the most prominent topics discussed in these reports and the concept of affordances in order to portray the potential role of information systems in supporting these activities. We identify five such affordances: (1) balancing supply and demand with consideration of the energy price; (2) capturing, reporting, and sharing energy-related data; (3) reducing energy consumption and emissions; (4) sustainable waste management; and (5) coordination of flexible charging of electric vehicles. These affordances support energy management through information systems in practice and set a foundation for further research in the important field of energy informatics.
MicroRNA-152 regulates immune response via targeting B7-H1 in gastric carcinoma

MiR-152 has been reported may be involved in carcinogenesis in gastric cancer. However, its role has not been comprehensively investigated in gastric cancer. We found miR-152 in human gastric cancer tissues were significantly lower than that in matched adjacent normal tissues. Meanwhile, lower miR-152 was also found in gastric cancer cell lines. The stage, tumor size and lymph node metastasis rate were significant higher in low–miR-152 group in clinical patients. Furthermore, there was a marked correlation between the levels of miR-152 and B7-H1 mRNA in gastric cancer tissues. Mechanistically, miR-152 directly bind to B7-H1 3′ untranslated region in gastric cancer cell and inhibited B7-H1 expression. Functional study demonstrated that elevation of miR-152 enhanced T cells proliferation and effector cytokines production via inhibiting B7-H1/PD-1 pathway. In conclusion, our work identified a novel mechanism by which immune response is increased by expression of miR-152 via targeting B7-H1. MiR-152 may be a potential therapeutic approach for gastric cancer.
Adaptive compressed sensing for acceleration data transmission in human motion capture

In a human motion capture system, massive acceleration data will cost much transmission time. In order to reduce the acceleration data transmission volume while guaranteeing measurement accuracy, this paper presents an adaptive compressed sensing algorithm for acceleration data transmission in human motion capture. An innovative feature of our approach is to associate the sparsity and best Compression Ratio of acceleration data in a learning phase. Based on this correlation, the sensor node uses sparsity to describe the structural characteristics of acceleration data, and it adjusts best Compression Ratios of data according to the characteristics of the real-time acceleration data. The results of experiment show that algorithm can adjust the best Compression Ratios of original acceleration data at different parts of body in several typical motions, while ensuring Reconstruction Accuracy. For example, in running, the approach can achieve that the Compression Ratios at arms and legs separately reach to approximately 80% and 60%, while the compression data can achieve the same Reconstruction Accuracy. What's more, the algorithm can increase the Compression Ratio of capture system by about 20% compared with the current BSBL-BO algorithm, while achieving the same minimum Reconstruction Accuracy of capture system.
Randomized pilot of a self-guided internet coping group for women with early-stage breast cancer

Background: Internet-based methods for provision of psychological support and intervention to cancer survivors hold promise for increasing the public impact of such treatments.Purpose: The goal of this controlled pilot study was to examine the effect and potential mechanisms of action of a self-guided, Internet-based coping-skills training group on quality of life outcomes in women with early-stage breast cancer.Methods: Sixty-two women completed baseline evaluations and were randomized into either a small online coping group or a waiting-list control condition.Results: No main effects for treatment were observed at the 12-week follow up. However, there was a significant interaction between baseline self-reported health status and treatment, such that women with poorer self-perceived health status showed greater improvement in perceived health over time when assigned to the treatment condition. Linguistic analyses revealed that positive changes across quality of life variables were associated with greater expression of negative emotions such as sadness and anxiety, greater cognitive processing, and lower expression of health-related concerns.Conclusions: These results demonstrate the potential efficacy of self-guided Internet coping groups while highlighting the limitations of such groups.
Caffeine and cholesterol: interactions with hostility.

&NA; The consumption of caffeinated beverages has been linked to elevated serum cholesterol and an increased risk of coronary disease, although the relationships are inconsistent across studies and remain controversial. The effect of caffeine on cholesterol and coronary disease risk may be modulated by other factors. Using cohort data from a subsample of the University of North Carolina Alumni Heart Study, we investigated whether the relationships between caffeinated beverage consumption and serum lipid and lipoprotein levels in middle‐aged men and women were modulated by levels of trait hostility. After adjustment for other risk factors, higher caffeinated beverage intake was associated with higher low‐density lipoprotein cholesterol levels and a higher ratio of total to high‐density lipoprotein cholesterol, both indicative of greater coronary disease risk. The interactive effects of hostility and caffeine intake were ambiguous, although there were trends for caffeine intake to have stronger effects on low‐density lipoprotein and on total cholesterol in people with less hostility. Additional studies of personality characteristics and other factors that can modulate the cholesterol‐raising effects of coffee drinking may be warranted because they might clarify the health consequences associated with coffee drinking and lead to the identification of individuals who would benefit most from changes in their coffee drinking.