Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation research of induction motor based on SVM-DTC with three-level inverter

Direct Torque Control has become a more and more important control technique in the control of Induction Motor for higher performance. The conventional two-level inverter has a problem of high torque ripple and large harmonics. For solving this problem, a model which is a combination of space vector modulation and a three-level inverter is proposed in this paper. On the platform of Matlab/Simulink, a 2-level DTC and a 3-level SVM-DTC are modeled and simulated. The simulation results show that 3-level DTC can reduce torque ripples and harmonics effectively.
A Semester Course for Teaching Three-Dimensional System-in-Package Design Automation

In recent years, three-dimensional (3D) system-in-package (SiP) has been widely recognized as a cost-effective alternative to system-on-chip (SoC). However, successful adoption of 3D SiP requires modifications to existing electronic design automation (EDA) tools to enable 3D SiP designs. In our department, our previous courses focus on SoC design flow. To meet the trend of 3D SiP, we are motivated to develop a new course to offer students the concepts and experiences for 3D SiP design automation. Our teaching materials cover the overall 3D SiP design flow from front-end to back-end within a semester (18 weeks). During the course, students need to implement four computer-aided-design (CAD) tools for 3D SiP design automation. At the end of the semester, the evaluation of the course by students is very positive. Moreover, our students used the topic of 3D SiP design automation to participate in Taiwan IC/CAD contest, which is a national contest hosted by Ministry of Education of Taiwan, and won the first prize awards in year 2009 and year 2010.
Life-long Map Learning for Graph-based Simultaneous Localization and Mapping Diplomarbeit

Maps of the environment are needed for a wide range of robotic applications, such as delivery, surveillance, and pursuit-evasion. The ability to generate a map of the environment is therefore a prerequisite for any truly autonomous mobile robot. A stateof-the-art approach to simultaneous localization and mapping (SLAM) uses a graph to model the belief of the robot. The nodes in the graph correspond to positions of the robot, while the edges in the graph represent spatial constraints between the nodes which arise from the measurements obtained by the robot. While mapping, this graph grows constantly as the robot travels, resulting in a computational complexity and memory requirements which impede long-term mapping applications. This thesis formulates an approach for graph-based simultaneous localization and mapping using grid maps that addresses the problem of scalability in long-term applications. We describe an information-theoretic algorithm that achieves significant gains in computational efficiency for robots that frequently re-traverse previously visited areas. In addition to that, our method produces grid maps of higher quality compared to standard grid-based mapping approaches. The less nodes in the graph used to model the belief of the robot, the faster the computations are. Furthermore, a sparse graph structure is desirable for efficient graph optimization. Our algorithm removes redundant information from the belief of the robot and thus prevents the graph from growing as long as the robot is moving through already visited territory. Exact elimination of information, however, introduces a large number of new constraints into the belief, ceasing the sparsity of the graph and thus the efficiency of the algorithm. We therefore describe an approximation technique to remove nodes from the graph while maintaining its sparsity and minimizing the involved loss of information, culminating in a highly efficient approach to graph sparsification. The experimental evaluation using real robot data show that our method yields grid maps which exhibit less blur compared to standard grid-based approaches while requiring significantly less computational resources.
A fast algorithm for estimating overcomplete ICA bases for image windows

We introduce a very fast method for estimating over-complete bases of independent components from image data. This is based on the concept of quasi-orthogonality, which means that in a very high-dimensional space, there can be a large, over-complete set of vectors that are almost orthogonal to each other. Thus we may estimate an over-complete basis by using one-unit ICA algorithms and forcing only partial decorrelation between the different independent components. The method can be implemented using a modification of the FastICA algorithm, which leads to a computationally highly efficient method.
Integrating Epigenomics into the Understanding of Biomedical Insight

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.
Incorporating Security Features in Service-Oriented Architecture using Security Patterns

Service-Oriented Architecture is an architectural style where different heterogeneous components share information with each other by using special types of messages based on the protocol known as Simple Object Access Protocol. Various technologies, such as Common Object Request Broker Architecture, Java 2 Platform, Enterprise Edition, Java Message Service etc. are applied to realize Service-Oriented Architecture for different applications. Besides these approaches, two other techniques, REpresentational State Transfer, and web services are applied for the realization of Service-Oriented Architecture. Web services provide a platform independent communication scheme between applications. The security preservation among the composition of services is an important task for Service-Oriented Architecture. In this study, an attempt is made to incorporate security features in Service- Oriented Architecture with the help of software security patterns. This scheme is described by developing an architectural model integrated with security goals and security patterns. The structural and behavioral aspects of composition of web services incorporated with security features are presented using a Unified Modeling Language class diagram and a sequence diagram respectively. At the end of this study, an evaluation is performed between identified security patterns and critical security properties along with Service-Oriented Architecture design principles. A case study of an online banking system is considered to explain the use of security patterns.
Gradient Calculations for Factor Analysis and Selected Variations

Abstract We derive the math (objective and gradients) for three closely-related Gaussian-based data models. All three are based on Factor Analysis (FA). The first is a simplification of FA; the second is FA itself; the third is our extension of FA to use a smooth trace norm prior on the factor loading matrix (in place of the hard rank constraint used by FA). See [3] for some background on Factor Analysis (FA). See [1] for help with matrix calculus.
The water balance of Lake Victoria

Abstract This paper presents new calculations of Lake Victoria's water balance. Evaporation is estimated using both the Penman formula and the energy balance approach, and sensitivity studies are performed to determine the influence of input data on the estimates. Rainfall over the lake is estimated from catchment rainfall using a relationship between the two that was derived using satellite data. The results, using the reference period 1956–1978, indicate that mean annual rainfall over the lake is 1791 mm, compared to mean annual evaporation of 1551 mm. When compared with lake level changes, tributary inflow, and discharge during this period, there is a resultant imbalance of 19 mm. Adding this amount to the calculated evaporation, we are able to reproduce with great accuracy the lake level changes during the period 1956–1978 utilizing precipitation estimates of this study plus measured inflow and discharge. Sensitivity studies show that the discrepancy in the balance of 19 mm is considerably smaller tha.
Hypothyroidism Patient : Dose ? Guideline Determination Optimized Dose Range of Dose Adaptation by Calculation Sequential Retrieval Similar Former Courses Check : Best Therapy Results Best Similar Courses Compositional Adaptation

So far, Case-based Reasoning has not become as successful in medicine as in some other application domains. One, probably the main reason is that on the one side in Case-based Reasoning the adaptation problem could not be solved in a general way. On the other side in medicine adaptation is often more difficult than in other domains, because usually more and complex features have to be considered. Recently we have developed we have developed some programs for endocrinology therapy support, especially for hypothyroidism. In this paper, we do not present them in detail, but focus on adaptation. We do not only summarise experiences with adaptation in medicine, but we want to elaborate typical medical adaptation problems and hope to indicate possibilities how to solve them.
Active Machine Learning for Consideration Heuristics

We develop and test an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a “configurator. ” Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy). To update posteriors after each question, we approximate the posterior with a variational distribution and use belief propagation (iterative loops of Bayes updating). The method runs sufficiently fast to select new queries in under a second and provides significantly and substantially more information per question than existing methods based on random, market-based, or orthogonal-design questions. Synthetic data experiments demonstrate that adaptive questions provide close-to-optimal information and outperform existing methods even when there are response errors or “bad” priors. The basic algorithm focuses on conjunctive or disjunctive rules, but we demonstrate generalizations to more complex heuristics and to the use of previous-respondent data to improve consumer-specific priors. We illustrate the algorithm empirically in a Web-based survey conducted by an American automotive manufacturer to study vehicle consideration (872 respondents, 53 feature levels). Adaptive questions outperform market-based questions when estimating heuristic decision rules. Heuristic decision rules predict validation decisions better than compensatory rules.
Heritability and quantitative genetic divergence of serotiny, a fire-persistence plant trait.

BACKGROUND AND AIMS Although it is well known that fire acts as a selective pressure shaping plant phenotypes, there are no quantitative estimates of the heritability of any trait related to plant persistence under recurrent fires, such as serotiny. In this study, the heritability of serotiny in Pinus halepensis is calculated, and an evaluation is made as to whether fire has left a selection signature on the level of serotiny among populations by comparing the genetic divergence of serotiny with the expected divergence of neutral molecular markers (QST-FST comparison). METHODS A common garden of P. halepensis was used, located in inland Spain and composed of 145 open-pollinated families from 29 provenances covering the entire natural range of P. halepensis in the Iberian Peninsula and Balearic Islands. Narrow-sense heritability (h(2)) and quantitative genetic differentiation among populations for serotiny (QST) were estimated by means of an 'animal model' fitted by Bayesian inference. In order to determine whether genetic differentiation for serotiny is the result of differential natural selection, QST estimates for serotiny were compared with FST estimates obtained from allozyme data. Finally, a test was made of whether levels of serotiny in the different provenances were related to different fire regimes, using summer rainfall as a proxy for fire regime in each provenance. KEY RESULTS Serotiny showed a significant narrow-sense heritability (h(2)) of 0·20 (credible interval 0·09-0·40). Quantitative genetic differentiation among provenances for serotiny (QST = 0·44) was significantly higher than expected under a neutral process (FST = 0·12), suggesting adaptive differentiation. A significant negative relationship was found between the serotiny level of trees in the common garden and summer rainfall of their provenance sites. CONCLUSIONS Serotiny is a heritable trait in P. halepensis, and selection acts on it, giving rise to contrasting serotiny levels among populations depending on the fire regime, and supporting the role of fire in generating genetic divergence for adaptive traits.
Designing Fuzzy Ensemble Classifiers by Evolutionary Multiobjective Optimization with an Entropy-Based Diversity Criterion

In this paper, we propose a multi-classifier coding scheme and an entropy-based diversity criterion in evolutionary multiobjective optimization algorithms for the design of fuzzy ensemble classifiers. In a multi-classifier coding scheme, an ensemble classifier is coded as an integer string. Each string is evaluated by using its accuracy and diversity. We use two accuracy criteria. One is the overall classification rate of the string as an ensemble classifier. The other is the average classification rate of component classifiers in the ensemble classifier. As a diversity criterion, we use the entropy of outputs from component classifiers in the ensemble classifier. We examine four formulations based on the above criteria through computational experiments on benchmark data sets in the UCI machine learning repository. The experimental results show the effectiveness of the multi-classifier coding scheme and the entropy-based diversity criterion.
Transformer based dual-power-mode CMOS power amplifier for handset applications

A fully integrated transformer based dual-power-mode CMOS power amplifier (PA) is implemented. The proposed architecture provides enhanced back-off efficiency with simple circuit topology. With broadband characteristic of the transmission line transformer, the PA delivers good performance across the 1.7-2.0 GHz frequency band. The PA with a 3.5 V supply voltage has gains of 14.5-16.2 dB and 10-10.5 dB in high power mode (HPM) and low power mode (LPM), respectively. The linear average output power for long-term evolution signals having 10MHz bandwidth and 7.5 dB peak-to-average power ratio is 26/20 dBm with a PAE of 31.6%-33.8%/20.8%-26.5% across the 1.7-2.0 GHz frequency in the HPM/LPM.
The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices

The extracellular matrix (ECM) is a complex meshwork of cross-linked proteins providing both biophysical and biochemical cues that are important regulators of cell proliferation, survival, differentiation, and migration. We present here a proteomic strategy developed to characterize the in vivo ECM composition of normal tissues and tumors using enrichment of protein extracts for ECM components and subsequent analysis by mass spectrometry. In parallel, we have developed a bioinformatic approach to predict the in silico “matrisome” defined as the ensemble of ECM proteins and associated factors. We report the characterization of the extracellular matrices of murine lung and colon, each comprising more than 100 ECM proteins and each presenting a characteristic signature. Moreover, using human tumor xenografts in mice, we show that both tumor cells and stromal cells contribute to the production of the tumor matrix and that tumors of differing metastatic potential differ in both the tumor- and the stroma-derived ECM components. The strategy we describe and illustrate here can be broadly applied and, to facilitate application of these methods by others, we provide resources including laboratory protocols, inventories of ECM domains and proteins, and instructions for bioinformatically deriving the human and mouse matrisome.
Molecular regulation of the PAI‐1 gene by hypoxia: contributions of Egr‐1, HIF‐1 α, and C/EBPα

Hypoxia, as occurs during tissue ischemia, tips the natural anticoagulant/procoagulant balance of the endovascular wall to favor activation of coagulation. Plasminogen activator inhibitor‐1 (PAI‐1) is an important factor suppressing fibrinolysis under conditions of low oxygen tension. We previously reported that hypoxia induced PAI‐1 mRNA and antigen expression in murine macrophages secondary to increased de novo transcription as well as increased mRNA stability. We now show in RAW264.7 murine macrophages that the transcription factors early growth response gene‐1 (Egr‐1), hypoxia‐inducible factor‐1α (HIF‐1α), and CCAAT/enhancer binding protein α (C/EBPα) are quickly activated in hypoxia and are responsible for transcription and expression of PAI‐1. Murine PAI‐1 promoter constructs, including Egr, HIF‐1α, and/or C/EBPα binding sites, were transfected into RAW 264.7 murine macrophages. To identify the relative importance of each of these putative hypoxia‐responsive elements, cells were exposed to normobaric hypoxia, and transcriptional activity was recorded. At 16 h of hypoxic exposure, murine PAI‐1 promoter deletion constructs that included Egr, HIF‐1α, and/or C/EBPα binding sites demonstrated increased tran‐scriptional activity. Mutation of each of these three murine PAI‐1 promoter sites (or a combination of them) resulted in a marked reduction in hypoxia sensitivity as detected by transcriptional analysis. Functional data obtained using 32P‐labeled Egr, HIF‐1 α response element (HRE), and C/EBPα oligonucleotides revealed induction of DNA binding activity in nuclear extracts from hypoxic RAW cells, with supershift analysis confirming activation of Egr‐1, HIF‐1 α, or C/EBPα. ChIP analysis confirmed the authenticity of these interactions as each of these transcription factors binds to chromatin under hypoxic conditions. Further, the induction of PAI‐1 by Egr‐1, HIF‐1 α, or C/EBPα was replicated in primary peritoneal macrophages. These data suggest that although HIF‐1 α appears to dominate the PAI‐1 transcriptional response in hyp‐oxia, Egr‐1 and C/EBPα greatly augment this response and can do so independent of HIF‐1α or each other. These studies are relevant to ischemic up‐regulation of the PAI‐1 gene and consequent accrual of micro‐vascular thrombus under ischemic conditions. —Liao, H. , Hyman, M. C. , Lawrence, D. A. , Pinsky, D. J. Molecular regulation of the PAI‐1 gene by hypoxia: contributions of Egr‐1, HIF‐1α, and C/EBPα. FASEB J. 21, 935–949 (2007)
A novel blind deconvolution de-noising scheme in failure prognosis

With increased system complexity, condition-based maintenance (CBM) becomes a promising solution for system safety by detecting faults and scheduling maintenance procedures before faults become severe failures resulting in catastrophic events. For CBM of many mechanical systems, fault diagnosis and failure prognosis based on vibration signal analysis are essential techniques. Noise originating from various sources, however, often corrupts vibration signals and degrades the performance of diagnostic and prognostic routines, and consequently, the performance of CBM. In this paper, a new de-noising structure is proposed and applied to vibration signals collected from a testbed of the main gearbox of a helicopter subjected to a seeded fault. The proposed structure integrates a blind deconvolution algorithm, feature extraction, failure prognosis and vibration modelling into a synergistic system, in which the blind deconvolution algorithm attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes associated with quality of the extracted features and failure prognosis are addressed, before and after de-noising, for validation purposes.
Reputation Based Collaboration among Ethernet Switches

Ethernet is built on the implicit assumption that all data packets are true and all participants honest, which leaves room for security vulnerabilities. Trust and reputation based systems could be used to increase the level of security on unmanaged Ethernet. By having the switches evaluate their neighbors' actions and propagating the information a collective analysis can be used to prevent suspected malicious nodes from taking over the network, while allowing limited network functionality to them.
A front-tracking algorithm for accurate representation of surface tension

We present a front tracking algorithm for the solution of the 2D incompressible Navier-Stokes equations with interfaces and surface forces. More particularly, we focus our attention on the accurate description of the surface tension terms and the associated pressure jump. We consider the stationary Laplace solution for a bubble with surface tension. A careful treatment of the pressure gradient terms at the interface allows us to reduce the spurious currents to the machine precision. Good results are obtained for the oscillation of a capil- lary wave compared with the linear viscous theory. A classical test of Rayleigh-Taylor instability is presented.
Investment, Debt and Risk Management in a Context of Uncertain Returns to Investment

In a context of uncertain returns to investment, a firm may face increasing costs of borrowing and uncertain value of its internal finance. By trading derivatives on the financial markets, the firm can hedge against the fluctuations of its cash flow, in order to better coordinate investment and financing decisions. This work shows how optimal investment, debt and hedging strategy can be strongly dependent on the mechanism linking the firm's cash flow to its returns on investment. Using the framework provided by Froot-Scharfstein-Stein (1993), two alternative mechanisms are compared. In the first one, the firm's cash flow is linked to the shocks to the price of the final product, whereas, in the second one, it is linked to the non neutral shocks to the production technology. The properties of the optimal hedging strategies and the comparison between the models are shown by deriving approximated analytical solutions. Numerical simulations of the first model's non-closed-form optimal solution are also obtained to validate the approximation, which is thus supported by numerical evidence. From the comparison, it turns out that the two models react differently to the same productivity shock: when cash flow is linked to a prospective price change, investment and debt are positively correlated; when cash flow is linked to the productivity shock, investment and debt are either negatively correlated (no hedging) or uncorrelated (hedging).