Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of the Rotor Pole Number on Optimal Parameters in Flux-Switching PM Brushless AC Machines by the Lumped-Parameter Magnetic Circuit Model

The investigation on flux-switching permanent-magnet (FSPM) machines in existing papers has been restricted to a specific combination of stator and rotor pole numbers, viz., 12/10 stator/rotor poles. In this paper, the influence of stator and rotor pole numbers on the optimal parameters of the FSPM machines are investigated by the finite-element analysis and the lumped-parameter magnetic circuit model, respectively. It shows that, although the optimal rotor pole width for maximum torque varies with the rotor pole number, the optimized ratio of rotor pole width to the rotor pole pitch almost maintains constant, viz., ~1/3. It also shows that the 12/13 and 12/14 stator/rotor pole FSPM machines exhibit ~10% larger electromagnetic torque than the conventional 12/10 stator/rotor pole FSPM machine. The analyses are validated by an experiment made on a 12/14 stator/rotor pole FSPM machine.
Learning to Be Calm in the Storm

When the level of anxiety goes up in a congregation and the clergy leader becomes anxious, that leader tends to revert to old family patterns and ways of functioning. Clergy persons without an awareness of their habitual patterns and reactivity often exercise their leadership in unhealthy ways that are damaging to their congregations. This qualitative case study described eight positive changes in the leadership attitudes and practices of clergy who took a Clergy Family Systems training which included theoretical content and experiential learning exercises. This article includes one of the three composite narratives that were used to reveal the real-life experiences of the 14 clergy persons who applied these concepts personally and professionally. Three years after the training these clergy were interviewed and found to be stronger leaders who were not only less reactive, less blaming and more calm themselves, but were also more calming to others even in anxious circumstances.
Knowledge Flow in the World of Cooperation : A Study on the Efficacy of Accredited Clusters in Hungary

Several high standard studies analyzing the effects of knowledge flow of organizations have appeared since the fact that the creation of applicable novel knowledge is fundamental to economic growth was acknowledged (ROMER, 1986). According to PORTER (2000) the regional alliance of small and medium-sized enterprises as well as supporting institutions could provide these businesses such competitive advantages that enable them to keep up with multinational companies, even in a globalised market environment. Accredited clusters, subsidized by the EU, are specific kind of alliance formations, which facilitate competence and innovation. At present (February 2010), there are 18 accredited clusters operating in Hungary – a dozen and a half company associations which met the economic and cooperative criteria defined by the EU and thus reached all the important stages of accreditation in the process of cluster development. These clusters are the subject of the present study and analysis. The first part of this paper introduces different theoretical approaches to the cluster phenomenon, including the observation and analysis of intra-cluster knowledge flow characteristics. The main purpose of the present study is to describe and assess the actual cluster formation process of accredited Hungarian clusters in order to compare them to each other and to measure them in terms of efficacy. The contrastive analysis is based on quantifiable public data and interviews carried out with cluster management leaders. We also describe the theoretical framework of an alternative methodology for monitoring cluster knowledge processes and measuring cluster performance. Knowledge transfer between cluster members is essential for innovations. The new methodology reviews and induces the measurement of emerging knowledge processes in Knowledge Flow in the World of Cooperation: a Study on the Efficacy of Accredited Clusters in Hungary 1 order to assess intra-cluster knowledge flow sector-specifically. This novel, applied methodology differs from conventional analytical practices on several points and makes use of innovative solutions in order to be more in line with the aims stated above, i.e. to capture “volatile” knowledge flow in the innovation-oriented clusters.
Control Methods in Data-Storage Systems

The recording performance of data-storage devices, in which write/read elements move relative to a storage medium to reliably store and retrieve information, depends on the capability of servo mechanisms to provide the necessary positioning accuracy. The desired characteristics of servo mechanisms for data-storage systems include robustness against variations of environmental parameters, high resolution, accuracy, stability, and fast response. This paper presents a comprehensive overview of advanced servo-control methods for data storage. The applications are to well-established recording technologies, including magnetic tape and magnetic disk systems as well as CD/DVD/Blue-Ray optical data-storage systems. Moreover, newer holographic and near-field optical systems and the emerging probe-storage technology are also addressed. Emphasis is given to the potential exhibited by the technologies considered for achieving ultra-high storage capacity, as required by the exploding demand in data-storage capacity for archival systems and massive multimedia data storage.
A highly-underactuated robotic hand with force and joint angle sensors

This paper describes a novel underactuated robotic hand design. The hand is highly underactuated as it contains three fingers with three joints each controlled by a single motor. One of the fingers (“thumb”) can also be rotated about the base of the hand, yielding a total of two controllable degrees-of-freedom. A key component of the design is the addition of position and tactile sensors which provide precise angle feedback and binary force feedback. Our mechanical design can be analyzed theoretically to predict contact forces as well as hand position given a particular object shape
A Continuous-Time Version of the Principal-Agent

This paper describes a new continuous-time principal-agent model, in which the output is a diffusion process with drift determined by the agentâ€™s unobserved effort. The risk-averse agent receives consumption continuously. An optimal contract, based on the agentâ€™s continuation value as a state variable, is computed by a new method using a differential equation. During employment the output path stochastically drives the agentâ€™s continuation value until it hits a low retirement point or a high retirement point. Unlike in related discrete-time models, one can use calculus to derive comparative statics and evaluate inefficiency
Human Action Recognition Using Accelerated Variational Learning of Infinite Dirichlet Mixture Models

Exploiting Dirichlet process mixture models (also known as infinite mixture models) to model visual and textual data is now standard weapon in the arsenal of machine learning. This paper proposes a new accelerated variational inference approach to learn Dirichlet process mixture models with Dirichlet distributions. The choice of using Dirichlet distribution as the basic distribution is mainly due to its flexibility for modeling proportional data. Indeed, this kind of data is naturally generated by several applications involving the representation of texts, images and videos using the bag-of-words (or "visual words" in the case of images and videos) approach. The potential of the developed learning framework is shown using a challenging real application namely human action recognition in videos.
Distributional vectors encode referential attributes

Distributional methods have proven to excel at capturing fuzzy, graded aspects of meaning (Italy is more similar to Spain than to Germany). In contrast, it is difficult to extract the values of more specific attributes of word referents from distributional representations, attributes of the kind typically found in structured knowledge bases (Italy has 60 million inhabitants). In this paper, we pursue the hypothesis that distributional vectors also implicitly encode referential attributes. We show that a standard supervised regression model is in fact sufficient to retrieve such attributes to a reasonable degree of accuracy: When evaluated on the prediction of both categorical and numeric attributes of countries and cities, the model consistently reduces baseline error by 30%, and is not far from the upper bound. Further analysis suggests that our model is able to “objectify” distributional representations for entities, anchoring them more firmly in the external world in measurable ways.
Deep learning based automatic liver tumor segmentation in CT with shape-based post-processing

Accurate automatic liver tumor segmentation would have a big impact on liver therapy planning procedures and follow-up reporting, thanks to automation, standardization and incorporation of full volumetric information. In this work, we propose a fully automatic method for liver tumor segmentation in CT images based on a 2D convolutional deep neural network with a shape-based post-processing. We ran our experiments on the LiTS dataset and evaluated detection and segmentation performance. Our proposed method achieves segmentation quality for detected lesions comparable to a human expert and is able to detect 77% of potentially measurable tumor lesions according to the RECIST 1.1 guidelines. We submitted our results to the LiTS challenge achieving state-of-the-art performance.
Evaluation of ERG and SPINK1 by Immunohistochemical Staining and Clinicopathological Outcomes in a Multi-Institutional Radical Prostatectomy Cohort of 1067 Patients

Distinguishing between patients with early stage, screen detected prostate cancer who must be treated from those that can be safely watched has become a major issue in prostate cancer care. Identification of molecular subtypes of prostate cancer has opened the opportunity for testing whether biomarkers that characterize these subtypes can be used as biomarkers of prognosis. Two established molecular subtypes are identified by high expression of the ERG oncoprotein, due to structural DNA alterations that encode for fusion transcripts in approximately ½ of prostate cancers, and over-expression of SPINK1, which is purportedly found only in ERG-negative tumors. We used a multi-institutional prostate cancer tissue microarray constructed from radical prostatectomy samples with associated detailed clinical data and with rigorous selection of recurrent and non-recurrent cases to test the prognostic value of immunohistochemistry staining results for the ERG and SPINK1 proteins. In univariate analysis, ERG positive cases (419/1067; 39%) were associated with lower patient age, pre-operative serum PSA levels, lower Gleason scores (≤3+4=7) and improved recurrence free survival (RFS). On multivariate analysis, ERG status was not correlated with RFS, disease specific survival (DSS) or overall survival (OS). High-level SPINK1 protein expression (33/1067 cases; 3%) was associated with improved RFS on univariate and multivariate Cox regression analysis. Over-expression of either protein was not associated with clinical outcome. While expression of ERG and SPINK1 proteins was inversely correlated, it was not mutually exclusive since 3 (0.28%) cases showed high expression of both. While ERG and SPINK1 appear to identify discrete molecular subtypes of prostate cancer, only high expression of SPINK1 was associated with improved clinical outcome. However, by themselves, neither ERG nor SPINK1 appear to be useful biomarkers for prognostication of early stage prostate cancer.
Space-time joint channel estimation in filtered multitone based multicarrier multibranch systems

In this paper, we investigate some space-time joint channel estimation (JCE) schemes in filtered multitone (FMT) based multicarrier multibranch (multiuser and/or multiantenna) systems. Each subcarrier is proposed to be processed separately due to the spectral characteristics of the FMT signals On each subcarrier, the wireless channels experienced by all transmit branches are estimated jointly at the receiver. The least square (LS) space-time JCE and weighted least square (WLS) schemes are derived. The constant amplitude zero auto-correlation (CAZAC) code based pilots are proposed for the aforementioned multicarrier applications to reduce the mean square estimation error. Simulation results are provided to validate the theoretical analysis.
Controversies in cardiovascular medicine Percutaneous transarterial aortic valve implantation : what do we know ?

Transcatheter aortic valve implantation (TAVI) is assuming a major role in the routine management of patients with aortic stenosis. Surgical aortic valve replacement is generally accepted to prolong survival, on the basis of historical comparisons and long experience. However, recently percutaneous transarterial TAVI has assumed the position as the only therapy in any aortic stenosis patient group demonstrated to prolong survival in a randomized trial. Arguably, percutaneous TAVI is now the standard of care in symptomatic patients who are not candidates for conventional surgery. On the basis of almost 10 years of experience TAVI also appears to be a reasonable option for some operable, but high-risk patients. Nevertheless considerable work needs to be done before the indications for TAVI are expanded into lower risk groups. We review what is currently known about percutaneous transarterial implantation of the aortic valve.
The Situation and Influencing Factors of Chinese Older People Taking Care of Their Grandchildren

Based on the Longitudinal Survey Data on the Situation of Urban and Rural Elderly of China in 2006,this paper finds that the older groups with younger age,better physical health,higher educational attainments,middle-level economic income are more likely to help to care their grandchildren.Compared to the older people in urban areas,those living in rural areas face much heavier burden of grandchildren caring.Older people are more likely to help their adult children with lower income to care their children.The behavior is not influenced by the economic support of adult children to them or not.The empirical results show that grandchildren caring could be understood as an unselfish contribution to family members and an altruism behavior in family.
RAHTM: Routing Algorithm Aware Hierarchical Task Mapping

The mapping of MPI processes to compute nodes on a supercomputer can have a significant impact on communication performance. For high performance computing (HPC) applications with iterative communication, rich offline analysis of such communication can improve performance by optimizing the mapping. Unfortunately, current practices for at-scale HPC consider only the communication graph and network topology in solving this problem. We propose Routing Algorithm aware Hierarchical Task Mapping (RAHTM) which leverages the knowledge of the routing algorithm to improve task mapping. RAHTM achieves high quality mappings by combining (1) a divide-and-conquer strategy to achieve scalability, (2) a limited search of mappings, and (3) a linear programming based routing-aware approach to evaluate possible mappings in the search space. RAHTM achieves 20% reduction in the communication time and 9% reduction in the overall execution time for three communication-heavy benchmarks scaled up to 16,384 processes on a Blue Gene/Q platform.
Algorithmic Skeletons within an Embedded Domain Specific Language for the CELL Processor

Efficiently using the hardware capabilities of the Cell processor, a heterogeneous chip multiprocessor that uses several levels of parallelism to deliver high performance, and being able to reuse legacy code are real challenges for application developers. We propose to use Generative Programming and more precisely template meta-programming to design an Domain Specific Embedded Language using algorithmic skeletons to generate applications based on a high-level mapping description. The method is easy to use by developers and delivers performance close to the performance of optimized hand-written code, as shown on various benchmarks ranging from simple BLAS kernels to image processing applications.
Scheduling of microfluidic operations for reconfigurabletwo-dimensional electrowetting arrays

We present an architectural design and optimization methodology for performing biochemical reactions using two-dimensional (2-D) electrowetting arrays. We define a set of basic microfluidic operations and leverage electronic design automation principles for system partitioning, resource allocation, and operation scheduling. Fluidic operations are carried out through the electrostatic configuration of a set of grid points. While concurrency is desirable to minimize processing time, the size of the 2-D array limits the number of concurrent operations of any type. Furthermore, functional dependencies between the operations also limit concurrency. We use integer linear programming to minimize the processing time by automatically extracting parallelism from a biochemical assay. As a case study, we apply our optimization method to the polymerase chain reaction, which is an important step in many lab-on-a-chip biochemical applications.
A Survey of Software-Defined Wireless Networks

Software-Defined Networking (SDN) has been applied to many different areas of wireless networking, bringing them greater flexibility, fine-grained control, and unparalleled ease-of-use. However, SDN incurs some overhead, so its potential benefits must be evaluated in each networking setting. Fortunately, the benefits of SDN appear to outweigh the overheads incurred in all wireless network environments tested so far. In infrastructure-dense wireless networks, SDN can provide perflow routing while reducing the distance between the OpenFlow switches and the controllers; this reduces customer latency while providing intuitive controls to the company. In infrastructure-less environments, SDN can provide natural virtualization and traffic isolation, providing security and configurability in networks that are not cooperative with such goals. In all wireless environments, SDN helps meet various traffic engineering goals, e.g. load balancing, with ease. In this paper, we survey applications of SDN to a variety of wireless networks, and note the benefits of each approach.
Detection and Tracking of Objects in Low Contrast Conditions

We present an efficient object detection and tracking technique using still cameras in low contrast conditions. The tracking algorithm involves background subtraction using Gaussian Mixture Model (GMM). Our method involves updating the parameters of the Mixture Model using a combination of an online k-means approximation technique and the ExpectationMaximization (EM) algorithm. We have shown experimentally that our proposed method yields results with higher accuracy and superior performance in situations where foregroundbackground contrast is low, as compared to established techniques involving only either one of k-means or EM algorithm to update mixture parameters.
Robust Stability of Uncertain Cellular Neural Networks with Time-Varying Delays

In this paper, we investigate the problem of global robust asymptotical stability of cellular neural networks with time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded, the activation functions are supposed to be bounded and globally Lipschitz continuous. Based on the Lyapunov-Krasovskii functional approach, a new delay-dependent stability criteria is presented in terms of linear matrix inequalities (LMIs). The stability criteria can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is given to illustrate the effectiveness and less conservativeness of our proposed method.
Functional activation of the left amygdala and hippocampus during associative encoding

The human hippocampus is critical to episodic encoding, but the role of the amygdala in memory is less clear. Animal research suggests a role for the amygdala in associative memory, but this has not been examined systematically in humans. Using fMRI, we compared amygdala and hippocampus activation for seven healthy subjects during two visual encoding tasks: serially presented single faces and faces presented as pairs. Single faces activated bilateral hippocampi, but not the amygdala. Paired faces activated bilateral amygdala, but only the left hippocampus. Subtraction of the two conditions revealed greater activation within the left amygdala and hippocampus during paired face encoding, suggesting that associative encoding activates a left-lateralized limbic network including the hippocampus and amygdala.
Generator for single bubbles of controllable size

A new type of apparatus for the production of single bubbles of adjustable size is presented. A single bubble is generated by injecting a short burst of gas into a liquid channel ﬂow. The radius of the bubble can be varied continuously from 300 m m up to a 3 mm by adjusting the timing of a valve injecting the gas. The device works also in liquids with variable pressure and can be microcomputer controlled. © 2001 American Institute of Physics. @ DOI: 10.1063/1.1329900 #
An Inverted File Cache for Fast Information Retrieval

The inverted file is the most popular indexing mechanism used for document search in an information retrieval system (IRS). However, the disk I/O for accessing the inverted file becomes a bottleneck in an IRS. To avoid using the disk I/O, we propose a caching mechanism for accessing the inverted file, called the inverted file cache (IF cache). In this cache, a proposed hashing scheme using a linked list structure to handle collisions in the hash table speeds up entry indexing. Furthermore, the replacement and storage mechanisms of this cache are designed specifically for the inverted file structure. We experimentally verify our design, based on documents collected from the TREC (Text REtrieval Conference) and search requests generated by the Zipf-like distribution. Simulation results show that the IF cache can improve the performance of a test IRS by about 60% in terms of the average searching response time.
Efficient layers-based schedules for iterative decoding of LDPC codes

Efficient serial decoding schedules for LDPC codes are described. The schedules are based on dividing the Tanner graph to sub-graphs. This yields an improvement in complexity and performance over the standard schedules. An application of the introduced schedules to decoding codes based on lifted graphs is described. An analysis based on density evolution is presented and is used to predict the behavior of different schedules.
Comparing the combinational complexities of arithmetic functions

Methods are presented for finding reductions between the computations of certain arithmetic functions that preserve asymptotic Boolean complexities (circuit depth or size). They can be used to show, for example, that all nonlinear algebraic functions are as difficult as integer multiplication with respect to circuit size. As a consequence, any lower or upper bound (e.g., <italic>O</italic>(<italic>n</italic> log <italic>n</italic> log log <italic>n</italic>)) for one of them applies to the whole class. It is also shown that, with respect to depth and size simultaneously, multiplication is reducible to any nonlinear and division to any nonpolynomial algebraic function.