Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Capital Development and Innovation: The Case of Training in Small and Medium-Sized Firms

This study examines the characteristics of small and medium-sized firms that perform training. It uses data taken from a recent Statistics Canada survey that permit firms' training decisions to be analyzed within the broader context of their many activities and strategies. The study finds strong evidence for the hypothesis that human capital development facilitated by training is complementary to innovation and technological change. Training incidence is found to be closely related to the importance that a firm gives to research and development, the use of new technologies, and numerous other strategies that are related to innovation. Training is also greater where a firm emphasizes quality and a comprehensive human-resource strategy. The results point to the inherent complementarity of technology and human resources policy.
Emphasizing the role of Wnt5a protein expression to predict favorable outcome after radical prostatectomy in patients with low-grade prostate cancer

Wnt5a, a member of non‐canonical wingless‐related MMTV integration site family is a secreted glycoprotein that plays important roles in development and disease. Recent studies have shown that Wnt5a protein levels are up‐regulated in prostate cancer, but contrasting reports exist on the role of Wnt5a to predict outcome after radical prostatectomy in patients with localized prostate cancer. Our group has recently shown that preserved high protein expression of Wnt5a in prostate cancer is associated with longer relapse‐free time after radical prostatectomy. The present tissue microarray study emphasizes the role of Wnt5a protein expression in a different, well‐defined, and independent cohort consisting of 312 prostate cancer patients. Kaplan–Meier curves plotted between Wnt5a expression and time to biochemical recurrence revealed that in low‐grade prostate cancer, patients with preserved high‐Wnt5a protein levels in their tumor cells have a lower risk of recurrence after radical prostatectomy compared to patients with low‐Wnt5a protein expression. When Wnt5a protein expression was added to a Cox regression multivariate analysis, both Wnt5a protein expression and surgical margin status independently predict biochemical free survival. Herein we confirm Wnt5a positivity as a prognostic factor and show that preserved overexpression of Wnt5a protein is associated with increased time to biochemical recurrence in localized low‐grade prostate cancer patients after radical prostatectomy. Our results emphasize that Wnt5a can be used as a predictive biomarker, and favoring the view of Wnt5a as a future therapeutic target in prostate cancer patients with tumor cells displaying low expression of Wnt5a.
Transitioning from a Goods-dominant to a Service-dominant logic : Visualizing the role of Remote Monitoring Systems

The manufacturing industry is turning their attention from a solely product orientation view, to the creation of new value-adding services in order to increase their after-sales business. In this thesis, a case study was conducted in order to examine a manufacturing industry in their process of developing value-adding services. By exploring their processes in service delivery and studying their introduction of remote monitoring systems in their service distribution, the aim was to visualise the dimensions of a service system and how it changes with the introduction of an specific new technology. This study has shown that the introduction of remote monitoring systems, changes the relationship between the provider and the receiver, through the decrease of physical visits at the clients’ site, with the consequence that the close relationships between the provider and its receivers can vanish. Furthermore this study shows that remote monitoring systems affects the service system, through intertwining the technology and shared information. As the characteristics of the new technology remote monitoring systems enables new ways of information sharing and thereby becoming how the information between the provider and receiver is shared. Additionally, this study shows that in order to co-create value in a service system, the provider has to bear full responsibility in coming up with an acceptable value proposition and for the clearness in presenting the value proposal to the receiver.
Context-Aware Service Discovery and Selection in Decentralized Environments

With the increasing use of web services in everyday tasks, we are entering an era of Internet of Services (IoS). Service discovery and selection has become critical issue in the area of web services. Traditional service discovery approaches are often supported by centralized registries that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, in this paper we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes, previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of the best available web service by taking into account the QoS properties of the web services. Experimental results were based on real world semantic web service dataset, the results showed that the approach achieved better performance and efficiency in both discovery and selection processes.
Galactic dynamo and helicity losses through fountain flow

Aims. Nonlinear behaviour of galactic dynamos is studied, allowing for magnetic helicity removal by the galactic fountain flow. Methods. A suitable advection speed is estimated, and a one-dimensional mean-field dynamo model with dynamic α-effect is explored. Results. It is shown that the galactic fountain flow is efficient in removing magnetic helicity from galactic discs. This alleviates the constraint on the galactic mean-field dynamo resulting from magnetic helicity conservation and thereby allows the mean magnetic field to saturate at a strength comparable to equipartition with the turbulent kinetic energy.
Action recognition in still images using a combination of human pose and context information

In this work, a novel method is proposed for recognizing human actions in still images, which incorporates both pose and context information. Poselet-based action classifiers are learned using Poselet Activation Vector as features, which contain pose information for each action. And context-based action classifiers for each action are learned on contextual information, which is obtained by sparse coding on foreground and background. The confidences of an image belonging to each action are obtained through summing up the probability outputs of the poselet-based and the context-based classifiers. The contribution of this work is three folded. Firstly, sparse coding is adopted to find compact patterns of the original features. Secondly, a block coordinate descent algorithm is proposed for sparse coding, which can be performed very fast in practice. Thirdly, both pose and context information are taken into consideration for action recognition. The experimental results show the proposed method achieves the state-of-the-art performance on several benchmarks.
The SNORD115 (H/MBII-52) and SNORD116 (H/MBII-85) gene clusters at the imprinted Prader–Willi locus generate canonical box C/D snoRNAs

The imprinted Snurf–Snrpn chromosomal domain contains two large arrays of tandemly repeated, paternally expressed box C/D small-nucleolar RNA (snoRNA) genes: the SNORD115 (H/MBII-52) and SNORD116 (H/MBII-85) gene clusters believed to play key roles in the fine-tuning of serotonin receptor (5-HT2C) pre-mRNA processing and in the etiology of the Prader–Willi Syndrome (PWS), respectively. SNORD115 and SNORD116 were recently proposed to undergo significant conversion into shorter RNA species, the so-called psnoRNAs. Here, we provide evidence that argues against the existence of abundant psnoRNAs in human or mouse brain. Instead, we characterize a previously unsuspected low-abundance, fibrillarin-associated SNORD115-derived smaller RNA species. Based on these findings, we strongly recommend that PWS-encoded SNORD115 and SNORD116 be considered as bona fide box C/D snoRNAs.
SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics

High-throughput structural proteomics is expected to generate considerable amounts of data on the progress of structure determination for many proteins. For each protein this includes information about cloning, expression, purification, biophysical characterization and structure determination via NMR spectroscopy or X-ray crystallography. It will be essential to develop specifications and ontologies for standardizing this information to make it amenable to retrospective analysis. To this end we created the SPINE database and analysis system for the Northeast Structural Genomics Consortium. SPINE, which is available at bioinfo.mbb. yale.edu/nesg or nesg.org, is specifically designed to enable distributed scientific collaboration via the Internet. It was designed not just as an information repository but as an active vehicle to standardize proteomics data in a form that would enable systematic data mining. The system features an intuitive user interface for interactive retrieval and modification of expression construct data, query forms designed to track global project progress and external links to many other resources. Currently the database contains experimental data on 985 constructs, of which 740 are drawn from Methanobacterium thermoautotrophicum, 123 from Saccharomyces cerevisiae, 93 from Caenorhabditis elegans and the remainder from other organisms. We developed a comprehensive set of data mining features for each protein, including several related to experimental progress (e.g. expression level, solubility and crystallization) and 42 based on the underlying protein sequence (e.g. amino acid composition, secondary structure and occurrence of low complexity regions). We demonstrate in detail the application of a particular machine learning approach, decision trees, to the tasks of predicting a protein's solubility and propensity to crystallize based on sequence features. We are able to extract a number of key rules from our trees, in particular that soluble proteins tend to have significantly more acidic residues and fewer hydrophobic stretches than insoluble ones. One of the characteristics of proteomics data sets, currently and in the foreseeable future, is their intermediate size ( approximately 500-5000 data points). This creates a number of issues in relation to error estimation. Initially we estimate the overall error in our trees based on standard cross-validation. However, this leaves out a significant fraction of the data in model construction and does not give error estimates on individual rules. Therefore, we present alternative methods to estimate the error in particular rules.
Static single information from a functional perspective

Static single information form is a natural extension of the well-known static single assignment form. It is a program intermediate representation used in optimising compilers for imperative programming languages. In this paper we show how a program expressed in static single information form can be transformed into an equivalent program in functional notation. We also examine the implications of this transformation.
An Integrated Virtualized Strategy for Fault Tolerance in Cloud Computing Environment

Cloud fault tolerance allows the cloud an ability to keep performing its functions correctly even if faults are occurring in the system. This becomes an important property that enables a complete system to continue functioning in the event of one or more faults for high availability of virtual machines or in life critical systems. A fault-tolerant design may allow the system to possibly function at a reduced level, rather than failing completely. As a major concern in guaranteeing availability, reliability of critical services or application execution in cloud environment, cloud fault tolerance research focuses on detection, recovery strategies. However, in order to minimize impacts, anticipate failures to proactively handle them, a model called an Integrated Virtualized Failover strategy (IVFS) was introduced where fault tolerance was realized using redundancy, checkpoint/replay, fault manager. In this paper, we critically analyze this model, proposed a model that tolerate faults based on the reliability of each computing node or virtual machine, removing these from the availability list if the performance is not optimal. The results of our algorithm presents an increase in pass rates, considers forward/backward recovery using diverse software tools. Our simulation results suggest a good performance compared to current existing models. The results are demonstrated through experimental validation with a critical analysis, laying the foundation for a fully fault tolerant IaaS Cloud environment.
Design and Synthesis of Programmable Logic Block With Mixed LUT and Macrogate

Small gates, such as AND2, XOR2, and MUX2, have been mixed with lookup tables (LUTs) inside programmable logic blocks (PLBs) to reduce area and power and increase performance in FPGAs. However, it is unclear whether incorporating macrogates with wide inputs inside PLBs is beneficial. In this paper, we first develop a methodology to extract a small set logic functions that are able to implement a large portion of functions for given FPGA applications, and propose a heterogeneous PLB with one LUT and one macrogate for the selected logic functions. Furthermore, we develop a synthesis flow for such heterogeneous PLBs, including a cut-based delay and area optimized technology mapping, a mixed binary integer and linear programming-based postmapping area recovery to balance the utilization of macrogates and LUTs, and a SAT-based PLB architecture-aware packing. Experiments using over 70 industrial benchmark applications show that we can extract four six-input logic functions to cover more than 50% functions of these applications, and the proposed synthesis flow reduces area by 5% compared to an alternative flow without the postmapping area recovery when both have the optimal logic depth. Compared to the PLB with mixed LUT-4 and small macrogates (XOR2 and MUX2), the PLB with mixed LUT-4 and four-input macrogate reduces logic depth by 6% (and up to 42%) for the aforementioned applications.
Visualization of Automated Compliance Monitoring and Reporting

Compliance management is a critical financial and legal subject for organizations. It is operationally implemented by embedding internal controls into business processes and their supporting IT systems. Challenges arise from the complexity of real-life processes and systems, their continuous monitoring and the timely communication of thereby detected problems. In order to realize effective and efficient monitoring, the responsible persons must be supported by suitable compliance software. This compliance software should enable the responsible persons to get both high-level information regarding the overall compliance status and low-level information regarding possible problems. Furthermore, it should not be limited to passive reporting components for compliance management, but also allow for interactive user interfaces, which facilitate the proactive supervision of tasks. The aim of this work is to encourage the responsible persons to analyze and explore compliance information through their appropriate visualization. Thus, unique and valuable human strengths, such as lateral thinking, can be used aside from the computational strengths of compliance software during control monitoring.
Frequency-Independent Warning Detection Sequential for Dynamic Voltage and Frequency Scaling in ASICs

In this paper, a metastability immune warning flip-flop (FF) is proposed, which consists of an edge detector, a warning window generator, and a warning detector along with a traditional FF. The delayed data are monitored during the warning window to flag a warning signal before the data enter the erroneous zone. In this scheme, the warning window is independent of input clock frequency and hence is suitable for frequency scaling application. A 16-bit Kogge-stone adder is implemented in 65-nm technology, which uses warning FF for dynamic voltage and frequency scaling (DVFS). The warning FF-based DVFS allows elimination of safety margins and operates till the point of first warning of the adder without any erroneous results. The experiments were conducted with different supply voltages, phase-shifted clocks, and process conditions. The circuit is helpful to determine when to stop further reduction in supply voltage by producing the warning signal with predefined timing slacks in DVFS application. The test chip results demonstrate that the proposed circuit can track the critical path delay of 2.4-7.5 ns at warning voltage of 1.15-0.72 V, respectively. The measured results from 10 different chips show the effectiveness of the proposed concept across process variation.
Research Statement Summary of Current Research

My research addresses the problems associated with the performance, scalability and management of distributed systems. I am particularly interested in developing techniques that enable self-managed, scalable and efficient deployment of large-scale scientific and engineering applications in a highly dynamic and unpredictable distributed environment. Next few sections describe some of my major research accomplishments to date and plans for future work.
Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?

Much work has been done on understanding and predicting human mobility in time. In this work, we are interested in obtaining a set of users who are spatio-temporally most similar to a query user. We propose an efficient way of user data representation called Spatio-Temporal Signatures to keep track of complete record of user movement. We define a measure called Spatio-Temporal similarity for comparing a given pair of users. Although computing exact pairwise Spatio-Temporal similarities between query user with all users is inefficient, we show that with our hybrid pruning scheme the most similar users can be obtained in logarithmic time with in a (1+\epsilon) factor approximation of the optimal. We are developing a framework to test our models against a real dataset of urban users.
PWO: Peer-To-Peer Worm Evolution Observer

Peer-to-peer based applications are coming, such as instant messaging, file sharing and video streaming. With the maturation of P2P technology there is larger and larger number of users. But P2P network's characteristics make itself a potential vehicle for the active worms to achieve fast worm propagation. So it is important to study the worm's spreading model and relative countermeasure under P2P systems. P2P worm's new characteristics are analyzed and a simple model is proposed in this paper. A novel method P2P worm evolution observer (PWO) is also described, this method introduces some observers in the P2P network to monitor some nodes' infected during the worm's spreading period, and traces the worm's infected origin and attacking paths under the global P2P network topology. Theory and simulation results show that this method can gain nearly 100% probability to trace the worm evolution, even when only less than 1% of the nodes are deployed with observers.
A homotopy-minimization method for parameter estimation in differential equations and its application in unraveling the reaction mechanism of the Min system

A mathematical model of a dynamical process, often in the form of a system of differential equations, serves to elucidate underlying dynamical structure and behavior of the process that may otherwise remain opaque. However, model parameters are often unknown and may need to be estimated from data for a model to be informative. Numerical-integration-based methods, which estimate parameters in a differential equation model by fitting numerical solutions to data, can demand extensive computation, especially for large stiff systems that require implicit methods for stability. Non-numerical integration methods, which estimate parameters in a differential equation model by fitting solution approximations to data, do not provide an impartial measure of how well a model fits data, a measure required for the testability of a model. In this dissertation, I propose a new method that steps back from a numerical-integration-based method, and instead allows an optimal data-fitting numerical solution to emerge as part of an optimization process. This method bypasses the need for implicit solution methods, which can be computationally intensive, seems to be more robust than numerical-integration-based methods, and, interestingly, admits conservation principles and integral representations, which allow me to gauge the accuracy of my optimization. The Escherichia coli Min system is one of the simplest known biological systems that demonstrates diverse complex dynamic behavior or transduces local interactions into a global signal. Various mathematical models of the Min system show behaviors that are qualitatively similar to dynamic behaviors of the Min system that have been observed in experiments, but no model has been quantitatively compared to time-course data. In this dissertation, I extract time-course data for model fitting from experimental measurements of the Min system and fit established and novel biochemistry-based models to the time-course data using my parameter estimation method for differential equations. Comparing models to time-course data allows me to make precise distinctions between biochemical assumptions in the various models. My modeling and fitting supports a novel model, which suggests that a regular, ordered, stability-switching mechanism underlies the emergent, dynamic behavior of the Min system.
Approximation algorithms for the 0-extension problem

In the 0-extension problem, we are given a weighted graph with some nodes marked as terminals and a semi-metric on the set of terminals. Our goal is to assign the rest of the nodes to terminals so as to minimize the sum, over all edges, of the product of the edge's weight and the distance between the terminals to which its endpoints are assigned. This problem generalizes the multiway cut problem of Dahlhaus, Johnson, Papadimitriou, Seymour, and Yannakakis and is closely related to the metric labeling problem introduced by Kleinberg and Tardos. We present approximation algorithms for O-EXTENSION. In arbitrary graphs, we present an &Ogr;(log k)-approximation algorithm, k being the number of terminals. We also give &Ogr;(1)-approximation guarantees for weighted planar graphs. Our results are based on a natural metric relaxation of the problem, previously considered by Karzanov. It is similar in flavor to the linear programming relaxation of Garg, Vazirani, and Yannakakis for the multicut problem and similar to relaxations for other graph partitioning problems. We prove that the integrality ratio of the metric relaxation is at least c√lgk for a positive c for infinitely many k. Our results improve some of the results of Kleinberg and Tardos and they further our understanding on how to use metric relaxations.
New Directions in Video Information Extraction and Summarization

The Informedia Digital Video Library project provided a technological foundation for full content indexing and retrieval of video and audio media. New directions for this research extend to: (1) search and retrieval in multilingual video corpora, (2) analysis and indexing of continuously captured, unstructed and unedited fieldcollected video, and (3) summarization of video-based content across multiple stories based on the user's perspective.