Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond search: Event-driven summarization for web videos

The explosive growth of Web videos brings out the challenge of how to efficiently browse hundreds or even thousands of videos at a glance. Given an event-driven query, social media Web sites usually return a large number of videos that are diverse and noisy in a ranking list. Exploring such results will be time-consuming and thus degrades user experience. This article presents a novel scheme that is able to summarize the content of video search results by mining and threading “key” shots, such that users can get an overview of main content of these videos at a glance. The proposed framework mainly comprises four stages. First, given an event query, a set of Web videos is collected associated with their ranking order and tags. Second, key-shots are established and ranked based on near-duplicate keyframe detection and they are threaded in a chronological order. Third, we analyze the tags associated with key-shots. Irrelevant tags are filtered out via a representativeness and descriptiveness analysis, whereas the remaining tags are propagated among key-shots by random walk. Finally, summarization is formulated as an optimization framework that compromises relevance of key-shots and user-defined skimming ratio. We provide two types of summarization: video skimming and visual-textual storyboard. We conduct user studies on twenty event queries for over hundred hours of videos crawled from YouTube. The evaluation demonstrates the feasibility and effectiveness of the proposed solution.
Coalitional Games With Intervention: Application to Spectrum Leasing in Cognitive Radio

We consider a spectrum leasing system in which secondary networks offer offload services to a primary network (PN) in exchange for temporary access to the PN's spectrum. When the coverage areas of several secondary access nodes (SANs) overlap, they compete for primary users (PUs), which benefits the PN, except when the SANs collude and coordinate their prices, forming a cartel. As a result, the PN obtains lower transmission rates for the serviced PUs. Our coalitional game analysis shows that stable cartels always exist and can form easily. To protect the spectrum owner's interests and enforce market regulation, we propose an intervention framework in which an intervention manager counteracts cartel formation. The specific features that make wireless systems different from conventional markets enable the manager to modify the set of achievable outcomes. The intervention capability is limited; thus, the objective is to design an intervention rule maximizing the PN transmission rate within the given constraints. Importantly, the intervention can solely act as a threat or a warning that does not need to be executed in practice. To reduce the computational effort, we also propose a low-complexity intervention rule that performs similarly to the optimal one in terms of assurable PN rate increment and outperforms other effective approaches.
Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning

In this paper, an automated method for the detection, segmentation and classification of Abdominal Aortic Aneurysm (AAA) region in computed tomography (CT) images is introduced. Deep Belief Network (DBN) is applied for the purpose of AAA detection and severity classification in this study. Optimum parameters for training the DBN are determined for the training data from the selected dataset. AAA region can be successfully segmented from the CT images and the result is comparable to the existing methods.
An integrated approach for multilingual scene text detection

Text messages in an image usually contain useful information related to the scene, such as location, name, direction or warning. As such, robust and efficient scene text detection has gained increasing attention in the area of computer vision recently. However, most existing scene text detection methods are devised to process Latin-based languages. For the few researches that reported the investigation of Chinese text, the detection rate was inferior to the result for English. In this research, we propose a multilingual scene text detection algorithm for both Chinese and English. The method comprises of four stages: 1. Preprocessing by bilateral filter to make the text region more stable. 2. Extracting candidate text edge and region using Canny edge detector and Maximally Stable Extremal Region (MSER) respectively. Then combine these two features to achieve more robust results. 3. Linking candidate characters: considering both horizontal and vertical direction, character candidates are clustered into text candidates using geometrical constraints. 4. Classifying candidate texts using support vector machine (SVM), to separate text and non-text areas. Experimental results show that the proposed method detects both Chinese and English texts, and achieve satisfactory performance compared to those approaches designed only for English detection.
Sistema Inmune Artificial Basado en Teoría de la Información para la Extracción de Palabras Clave de Documentos de Texto

This paper presents a model for keyword extraction, extending the basic concepts commonly used in this task, in order to get a formal background that allows determining the importance of the keywords to the documents. The proposed model combines an artificial immune system with a mathematical background based on information theory; this new model has the advantage that does not need any domain knowledge, neither the use of a stopword list or any previous information about the content of the documents. The final result is a set of keywords for each category into the corpus used. Keywords—Keyword Extraction, Artificial Immune Systems, Information Theory.
Cell-Cell Adhesion Thymus Inhibits Ly-6A.2-Dependent Protein Expressed in Mouse Spleen and A Monoclonal Antibody Against the 66-kDa

The Ly-6 locus encodes several cell surface proteins of 10–12 kDa. Some members of this multigene family may function in cell signaling and/or cell adhesion processes. T lymphocytes overexpressing Ly-6A.2 (one member of the Ly-6 gene family) protein homotypically aggregate when cultured in vitro. Further analysis of this homotypic aggregation suggests that Ly-6A.2 participates in cell-cell adhesion. These observations indicated the presence of a Ly-6 ligand(s) on the surface of lymphoid cells. In this study we report generation of a hamster mAb, 9AB2, that blocks Ly-6A.2-dependent cell-cell adhesion. The 9AB2 Ab recognizes a 66-kDa glycoprotein with unique tissue expression. The 9AB2 mAb does not bind Ly-6A.2, but coimmunoprecipitates Ly-6A.2 molecule. Moreover, 9AB2 Ag-expressing thymocytes speciﬁcally bind to Chinese hamster ovary cells overexpressing Ly-6A.2 protein, and this binding is speciﬁcally blocked by 9AB2 and anti-Ly-6A.2 Abs. These results suggest that the 66-kDa protein recognized by 9AB2 mAb is the putative ligand for Ly-6A.2. The Journal of Immunology, 2000, 165: 3763–3771.
Auto-Tuned Spline Synopses for Database Statistics Management

Data distribution statistics are vital for database systems and other data-mining platforms in order to predict the running time of complex queries for data ltering and extraction. State-of-theart database systems are in exible in that they maintain histograms on a xed set of single attributes, each with a xed number of buckets regardless of the underlying distribution and precision requirements for selectivity estimation. Despite many proposals for more advanced types of "data synopses", research seems to have ignored the critical tuning issue of deciding on which attribute combinations synopses should be built and how many buckets (or, analogously, transform coe cients, etc.) these should have with a given amount of memory that is available for statistics management overall. This paper develops a method for the automatic tuning of variable-size spline-based data synopses for multidimensional attribute-value frequency as well as density distributions such that an overall error metric is minimized for a given amount of memory. Our method automatically uses more space for distributions that are harder or more important to capture with good precision. Experimental studies with synthetic and real data demonstrate the viability of the developed auto-tuning method.
Modeling a soft robotic mechanism articulated with dielectric elastomer actuators

In this paper, a translational actuation mechanism in the form of a parallel-crank mechanism (i.e. double-crank 4-bar mechanism) articulated with two dielectric elastomer actuators working in parallel are fabricated. This structure, which is a fully soft mechanism, is established by stretching a dielectric elastomer (DE) film over a PET (polyethylene terephthalate) frame so that the energy released from the stretched DE film is stored in the frame as bending energy. The mechanism generates a translational output under a driving voltage applied between two electrodes of the DE film. The visco-elastic models are proposed for the mechanism so that the mechanical properties of the actuator can more accurately be incorporated into the mechanism model. The proposed model accurately predicts the experimental frequency response of the mechanism at different voltages.
Effective Design and Use of Computer Decision Models

Computer design models often provide useful results as management planning tools. However, these tools are frequently limited to firms with staffs of specialists who can assimilate the technical nature of the models. For other firms, the success of decision models such as simulation have not been demonstrated. This paper looks at recent literature regarding decision model deficiencies, evaluates selected financial simulation model packages, and suggests design needs for expanding the use of decision models to a broader range of firms.
Zinc oxide nanoparticles-induced epigenetic change and G2/M arrest are associated with apoptosis in human epidermal keratinocytes

As an engineered nanomaterial, zinc oxide nanoparticles (ZnO NPs) are used frequently in biological applications and can make contact with human skin. Here, we systematically investigated the effects of ZnO NPs on non-tumorigenic human epidermal keratinocytes, which were used as a test model for this in vitro study, at the epigenetic and molecular levels. Our results showed that ZnO NPs induced cell cycle arrest at the G2/M checkpoint before the viability of human epidermal keratinocytes was reduced, which was associated with the chromatin changes at the epigenetic level, including increased methylation of histone H3K9 and decreased acetylation of histone H4K5 accompanied by chromatin condensation at 24 hours. The mRNA expression of the methyltransferase genes G9a and GLP was also increased upon treatment with ZnO NPs, and the acetyltransferase genes GCN5, P300, and CBP were downregulated. Reactive oxygen species were found to be more abundant after treatment with ZnO NPs for 6 hours, and DNA damage was observed at 24 hours. Transmission electron microscopy and flow cytometry confirmed that ZnO NPs were absorbed into the cell when they were added to the medium. Apoptotic human epidermal keratinocytes were detected, and the expression of the proapoptotic genes Bax, Noxa, and Puma increased significantly, while the expression of the antiapoptotic gene Bcl-xl decreased 24 hours after exposure to ZnO NPs. These findings suggest that the ZnO NPs induced cell cycle arrest at G2/M, which was associated with epigenetic changes and accompanied by p53-Bax mitochondrial pathway-mediated apoptosis.
Motion blur identification in noisy images using fuzzy sets

Motion blur is one of the most common blurs that degrades images. Restoration of such images are highly dependent to estimation of motion blur parameters. Many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms are different in their performance, time complexity, precision and their robustness in noisy environments. In this paper we have presented a novel algorithm to estimate linear motion blur parameters such as direction and extend by using Radon transform to find direction and fuzzy set concepts to find its extend. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on a wide range of different type of standard images that were degraded with different directions (between 0deg and 180deg) and different motion lengths (between 10 to 50 pixel). Experimental results showed in average SNR > 22 db that is highly satisfactory
Parallel FFT Libraries

The focus of this project is the area of the fast Fourier transform (FFT) libraries (FFTW 3.3, P3DFFT 2.4 and 2DECOMP&FFT 1.1.319). During this project we benchmark the libraries on Hector phase 2b supercomputer. The main goal of this project is to examine the performance of the libraries which are built on the top of the FFTW’s latest version and compare their results with the FFTW itself. Moreover, the ability of the P3DFFT and the 2DECOMP&FFT to implement the 2D decomposition, let us experiment with the scaling of these libraries in thousands of cores. The findings from this experiment provide evidence of the FFTW library’s excellent scaling by using the 1D decomposition and the ability of the other two libraries to minimise the time required for the Fourier transform computation, by using the 2D decomposition. The main conclusions drawn from this study were the ability of the P3DFFT and the 2DECOMP&FFT libraries to scale up to thousands of cores and the affects of the Gemini Interconnect on their performance.
Surfactant Protein D Increases Fusion of Mycobacterium tuberculosis- Containing Phagosomes with Lysosomes in Human Macrophages

ABSTRACT Lung surfactant protein D (SP-D) binds to Mycobacterium tuberculosis surface lipoarabinomannan and results in bacterial agglutination, reduced uptake, and inhibition of growth in human macrophages. Here we show that SP-D limits the intracellular growth of bacilli in macrophages by increasing phagosome-lysosome fusion but not by generating a respiratory burst.
Creating Value Through Divestiture of Assets : The Case of Thorn EMI

This is a study of the restructuring programme at Thorn EMI during 1985-1996, which transformed a diversified conglomerate, trading at a diversification discount, to a narrowly focused firm, while creating considerable value for shareholders. The firm restructured following action by the outside directors and differed in corporate governance from other UK diversified firms. A smaller board of directors majority-controlled by non-executive directors and managerial compensation in the form of stock options providing long-term incentives ensured adequate monitoring and alignment of managerial incentives with shareholder interests. These changes were sufficient to eliminate agency costs of managerial discretion without the need to assume debt, which enabled the firm to reinvest the proceeds from asset sales to positive net present value projects. Furthermore, the case allows us to examine the costs and benefits associated with gradual restructurings over a long time horizon and provides insights on the sources of value gains associated with spin-offs.
Distribution expansion problem: formulation and practicality for a multistage globally optimal solution

Despite numerous research efforts of the past 40 years in the area of distribution expansion, a clear definition of the problem, and a truly multistage formulation that addresses practical concerns is yet to be developed. In this paper, the problem is clearly defined and analyzed from a practical point of view. A directed graph minimum edge cost network flow modeling of the problem for a truly multistage formulation using mathematical programming that guarantees global optimality and addresses the noted deficiencies is proposed. The proposed formulation is implemented on small case studies under varying assumptions. Comparative analysis indicates the importance of improved expansion planning.
A Study and Categorization of Pervasive Systems Architectures Towards Specifying a Software Product Line

Abstract In this work, we identify the importance of building a reference architecture for pervasive systems that could be used as part of a specification of a software product line for this domain. In an attempt to eventually specify such reference architecture, we studied over fifty published architectures for pervasive systems. We then extracted the major architectural features present in the studied architectures, and categorized the architectures according to the type and the environment they are targeting. We present our findings herein this article.
Bioinformatics Analysis of UL41 Protein Encoded by UL41 Gene of Duck Plague Virus

This report showed some physico-chemical properties and structural features about UL41 protein predicted by some software and on-line tools. The analysis showed both the signal peptide and the trans membrance region are not found. Meanwhile, the protein has twenty-four potential phosphorylation sites. In addition, the protein has more hydrophilic amine acid districts than hydrophobic districts and subcellular localization mainly located at the cytoplasm with 56.5%. The prediction of secondary structure showed that random coils dominate among secondary structure elements followed by alpha helix and extended strand. The prediction of tertiary structure showed the structure of the UL41 protein had a certain level of similarity to the structure-specific nuclease FEN-1(flap endonuclease-1). The study will be a basis for the further functional study of the UL41 protein.
Indirect Radionuclide Coronary Angiography to Evaluate Gradients of Myocardial Blood Flow and Flow Reserve Through Coronary Stenosis Using N-13 Ammonia PET/CT

Although quantitative evaluation of myocardial blood flow (MBF) and myocardial flow reserve (MFR) has been perceived as an attractive advantage of positron emission tomography (PET) over other cardiac imaging technologies, application of the information to specific coronary lesions is a difficult task for nuclear cardiologists. We hypothesized that changes in MBF and MFR over a coronary lesion could be identified by use of a hybrid technology of CT coronary angiography (CTCA) and N-13 ammonia PET. To evaluate this hypothesis, we measured the gradient of MBF and MFR through coronary stenosis in seven patients (M:F=3:4, median age 56 years) with coronary artery disease who underwent N-13 ammonia PET, CTCA, and interventional coronary angiography. Two patients had proximal left anterior descending (LAD) coronary artery disease and five patients had mid to distal LAD disease. Mean global stress and rest MBF were 2.62±0.58 and 1.03±0.19 ml/min/g, respectively. Mean global MFR was 2.6±0.73. Regional stress and rest MBF in the LAD territory were 2.36±0.75 and 0.96±0.21 ml/min/g, respectively. Regional MFR in the LAD territory was 2.55±0.83 ml/min/g. Stress MBF changed dramatically according to the location of coronary stenosis. It dropped acutely in proximal lesions, whereas it diminished gradually in mid to distal lesions. In conclusion, by use of a hybrid technology of CTCA and PET, it was feasible to make a direct correlation of coronary lesions with the gradient of MFR and CFR through coronary stenosis, which indicated the severity of the coronary lesion. We named this technique indirect radionuclide coronary angiography.
Video Object Discovery and Co-Segmentation with Extremely Weak Supervision

We present a spatio-temporal energy minimization formulation for simultaneous video object discovery and co-segmentation across multiple videos containing irrelevant frames. Our approach overcomes a limitation that most existing video co-segmentation methods possess, i.e., they perform poorly when dealing with practical videos in which the target objects are not present in many frames. Our formulation incorporates a spatio-temporal auto-context model, which is combined with appearance modeling for superpixel labeling. The superpixel-level labels are propagated to the frame level through a multiple instance boosting algorithm with spatial reasoning, based on which frames containing the target object are identified. Our method only needs to be bootstrapped with the frame-level labels for a few video frames (e.g., usually 1 to 3) to indicate if they contain the target objects or not. Extensive experiments on four datasets validate the efficacy of our proposed method: 1) object segmentation from a single video on the SegTrack dataset, 2) object co-segmentation from multiple videos on a video co-segmentation dataset, and 3) joint object discovery and co-segmentation from multiple videos containing irrelevant frames on the MOViCS dataset and XJTU-Stevens, a new dataset that we introduce in this paper. The proposed method compares favorably with the state-of-the-art in all of these experiments.
Born at the Wrong Time: Selection Bias in the NHL Draft

Relative age effects (RAEs) occur when those who are relatively older for their age group are more likely to succeed. RAEs occur reliably in some educational and athletic contexts, yet the causal mechanisms remain unclear. Here we provide the first direct test of one mechanism, selection bias, which can be defined as evaluators granting fewer opportunities to relatively younger individuals than is warranted by their latent ability. Because RAEs are well-established in hockey, we analyzed National Hockey League (NHL) drafts from 1980 to 2006. Compared to those born in the first quarter (i.e., January–March), those born in the third and fourth quarters were drafted more than 40 slots later than their productivity warranted, and they were roughly twice as likely to reach career benchmarks, such as 400 games played or 200 points scored. This selection bias in drafting did not decrease over time, apparently continues to occur, and reduces the playing opportunities of relatively younger players. This bias is remarkable because it is exhibited by professional decision makers evaluating adults in a context where RAEs have been widely publicized. Thus, selection bias based on relative age may be pervasive.
KVM IO profiling

This paper focuses on the evaluation of KVM options related to Input/Output performance. The technology developed by Red Hat offers many choices to the end user for configuring the interaction between the Virtual Machine and the real storage space. This document presents tests and recommendations in a bottom-up approach, starting with the virtual drive interface emulated by hypervisor and covering details up to the way that host's file system acts while flushing guest's data to the disk drive. In this paper we analyze many options provided by KVM, evaluate the performances delivered and extract some relevant “best-practices” conclusions.
Rapid and Reliable Calibration of Laser Beam Deflection System for Microcantilever-Based Sensor Setups

Cantilever array-based sensor devices widely utilise the laser-based optical deflection method for measuring static cantilever deflections mostly with home-built devices with individual geometries. In contrast to scanning probe microscopes, cantilever array devices have no additional positioning device like a piezo stage. As the cantilevers are used in more and more sensitive measurements, it is important to have a simple, rapid, and reliable calibration relating the deflection of the cantilever to the change in position measured by the position-sensitive detector. We present here a simple method for calibrating such systems utilising commercially available AFM cantilevers and the equipartition theorem.
Quantification method of classification processes. Concept of structural a-entropy

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