Set Of Prototypes Research Articles

Weighted Reward–Punishment Editing

The Nearest Neighbor classifier is a popular nonparametric classification method that has been successfully applied to many pattern recognition problems. Its usefulness has been limited, however, because of its computational complexity and sensitivity to outliers in the training set. Computational complexity is becoming less of an issue thanks to the availability of inexpensive memory and high processing speeds. To overcome the second limitation, sensitivity to outliers in the training set, researchers have developed editing and condensing techniques that are aimed at selecting a proper set of prototypes from the training set. In this work, we propose a new editing technique based on the idea of rewarding those patterns that make a contribution to a correct classification while punishing those patterns that provide an incorrect classification. This criterion is coupled with an approach for selecting the edited patterns based on the minimization of a criteria index related to the distances in the training patterns and on the calculation of edge and border patterns for determining the number of edited patterns. Extensive experiments were conducted across several classification problems that evaluated both the efficacy of the proposed technique with respect to other editing approaches and the advantages of using our proposed editing technique either in combination with condensing techniques or as a method utilized in the preprocessing stage. Moreover, the proposed editing approach is shown to be particularly effective when combined with a support vector machine (SVM) classifier. The MATLAB code used in the proposed paper will be available at https://www.dei.unipd.it/node/2357.

Microstrip Patch versus Dielectric Resonator Antenna Bearing All Commonly Used Feeds: An experimental study to choose the right element

Microstrip patches and dielectric resonators (DRs) are two low-profile variants of modern microwave and wireless antennas. However, the DR antenna (DRA) is relatively new and still passing through the stages of development. Both variants are quite similar in terms of performance and characteristics. This article focuses on a meaningful comparative study where we have considered all commonly used feed mechanisms such as coaxial probe, microstrip line, and rectangular aperture for both antennas operating near the same frequency. Circular geometry, i.e., cylindrical DRA (CDRA) and circular microstrip patch antenna (CMPA), have been chosen, and a systematic investigation based on thorough experiments has been executed. Multiple sets of prototypes have been fabricated and measured at 4 GHz. All available data have been furnished and compared, indicating relative advantages and disadvantages. This comparative study should provide qualitative and quantitative instructions to a designer for choosing the right element and corresponding feed based on design requirement and feasibility.

A Cluster-Based Method for Hydrometeor Classification Using Polarimetric Variables. Part II: Classification

AbstractTwo new algorithms for hydrometeor classification using polarimetric radar observations are developed based on prototypes derived by applying clustering techniques (Part I of this two-part paper). Each prototype is defined as a probability distribution of the polarimetric variables and ambient temperature corresponding to a hydrometeor type. The first algorithm is a maximum prototype likelihood classifier that uses all prototypes attributed to the different hydrometeor types in Part I. The hydrometeor type is assigned as the prototype with the highest likelihood when comparing the polarimetric variables and temperature with each prototype. The second algorithm is a Bayesian classifier that uses the probability density functions (PDFs) as derived from the prototype set associated with the identical hydrometeor type. The posteriori probability in the Bayesian method is calculated from a combination of the PDFs and the prior probability, the maximum of which corresponds to the most likely hydrometeor type. The respective merits of the two techniques are discussed. The two classifiers are applied to CP-2 S-band radar observations of two hailstorms that occurred between 16 and 20 November 2008, including the so-called Gap storm, which produced a devastating microburst and large hail at the ground. Results from the classifiers are compared with those derived using the well-established National Center for Atmospheric Research fuzzy logic classifier. In general, good agreement is found, yielding overall confidence in the robustness of the new classifiers. However, large differences are found for the melting ice and ice crystal categories, which will need to be studied further.

A thermodynamics-inspired feature for anomaly detection on crowd motions in surveillance videos

Identification of abnormal behaviors in surveillance videos of crowds plays an important role in public security monitoring. However, detecting abnormal crowd behaviors is challenging in that movements of individuals are usually random and unpredictable, and the occlusions caused by over-crowding make the task more difficult. In this paper, we introduce thermodynamic micro-statistics theory to detect and localize abnormal behaviors in crowded scenes based on Boltzmann Entropy. For this purpose, the scene of interest is modeled as moving particles turned out from a general optical flow algorithm. The particles are grouped into a set of prototypes according to their speeds and directions of moving, and a histogram is established to figure out how the particles distribute over the prototypes. Here, Boltzmann Entropy is computed from the histogram for each video clip to characterize the chaos degree of crowd motion. By means of such feature extraction, the crowd motion patterns can be represented as a time series. We find that when most people behave anomaly in an area under surveillance, the corresponding entropy value will increase remarkably in comparison with those of normal cases. This motives us to make use of Boltzmann Entropy to distinguish the collective behaviors of people under emergent circumstances from their normal behaviors by evaluating how significantly the current feature value fits into the Gaussian model of normal cases. We validate our method extensively for anomaly detection and localization. The experimental results show promising performance compared with the state of the art methods.

Instance reduction for supervised learning using input-output clustering method

A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed. The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data. Then, a set of prototypes are selected from the clustered input data. The inessential data can be ultimately discarded from the data set. The proposed method can reduce the effect from outliers because only the prototypes are used. This method is applied to reduce the data set in regression problems. Two standard synthetic data sets and three standard real-world data sets are used for evaluation. The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets. From the experiments, the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets. The numbers of instances of the synthetic data sets are decreased by 25%-69%. The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%, respectively. The reduction rate of 96% is very good for the electrocardiogram (ECG) data set because of the redundant and periodic nature of ECG signals. For all of the data sets, the regression results are similar to those from the corresponding original data sets. Therefore, the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.

Global behavior of the Douglas–Rachford method for a nonconvex feasibility problem

In recent times the Douglas-Rachford algorithm has been observed empirically to solve a variety of nonconvex feasibility problems including those of a combinatorial nature. For many of these problems current theory is not sufficient to explain this observed success and is mainly concerned with questions of local convergence. In this paper we analyze global behavior of the method for finding a point in the intersection of a half-space and a potentially non-convex set which is assumed to satisfy a well-quasi-ordering property or a property weaker than compactness. In particular, the special case in which the second set is finite is covered by our framework and provides a prototypical setting for combinatorial optimization problems.

Prototype Generation Using Multiobjective Particle Swarm Optimization for Nearest Neighbor Classification.

The nearest neighbor (NN) classifier suffers from high time complexity when classifying a test instance since the need of searching the whole training set. Prototype generation is a widely used approach to reduce the classification time, which generates a small set of prototypes to classify a test instance instead of using the whole training set. In this paper, particle swarm optimization is applied to prototype generation and two novel methods for improving the classification performance are presented: 1) a fitness function named error rank and 2) the multiobjective (MO) optimization strategy. Error rank is proposed to enhance the generation ability of the NN classifier, which takes the ranks of misclassified instances into consideration when designing the fitness function. The MO optimization strategy pursues the performance on multiple subsets of data simultaneously, in order to keep the classifier from overfitting the training set. Experimental results over 31 UCI data sets and 59 additional data sets show that the proposed algorithm outperforms nearly 30 existing prototype generation algorithms.

Control of Chaotic Resonance Phenomena using Prototypes in Manifold Forms

This paper presents a modeling strategy using prototypes in manifold forms for unknown chaotic behaviors, observed possibly in power electronics, robot manipulators, lasers, etc.; it models from only a finite number of one dimensional time series observables in order to simplify both the modeling and the controller design. With the intention of dividing the operating space into a set of smaller regions, the Self-Organizing Map (SOM) is employed as a modeling groundwork and prototypes in manifold forms attached to the SOM are created in the least square sense for each region. Once a set of prototypes representing the operating space is established, the regional controllers associated with the prototypes are designed with a traditional PID control law. Switching of the controllers is done synchronously by the SOM, which chooses the regional operating space, linked with an active regional prototype valid in a certain operating regime. Simulations on the chaotic oscillator regulating the unknown chaos to a fixed point or a stable periodic orbit illustrate the efficiency of the proposed modeling and control architecture.

Control of Chaotic Resonance Phenomena using Prototypes in Manifold forms

This paper presents a modeling strategy using prototypes in manifold forms for unknown chaotic behaviors, observed possibly in power electronics, robot manipulators, lasers, etc.; it models from only a finite number of one dimensional time series observables in order to simplify both the modeling and the controller design. With the intention of dividing the operating space into a set of smaller regions, the Self-Organizing Map (SOM) is employed as a modeling groundwork and prototypes in manifold forms attached to the SOM are created in the least square sense for each region. Once a set of prototypes representing the operating space is established, the regional controllers associated with the prototypes are designed with a traditional PID control law. Switching of the controllers is done synchronously by the SOM, which chooses the regional operating space, linked with an active regional prototype valid in a certain operating regime. Simulations on the chaotic oscillator regulating the unknown chaos to a fixed point or a stable periodic orbit illustrate the efficiency of the proposed modeling and control architecture.Keywords: Chaotic Resonance, Modeling, PID Control, Self-Organizing Map

Kondo destruction in heavy fermion quantum criticality and the photoemission spectrum of YbRh2Si2

Heavy fermion metals provide a prototype setting to study quantum criticality. Experimentally, quantum critical points have been identified and studied in a growing list of heavy fermion compounds. Theoretically, Kondo destruction has provided a means to characterize a class of unconventional quantum critical points that goes beyond the Landau framework of order-parameter fluctuations. Among the prominent evidence for such local quantum criticality have been measurements in YbRh2Si2. A rapid crossover is observed at finite temperatures in the isothermal field dependence of the Hall coefficient and other transport and thermodynamic quantities, which specifies a T*B line in the temperature (T)–magnetic field (B) phase diagram. Here, we discuss what happens when temperature is raised, by analyzing the ratio of the crossover width to the crossover position. With this ratio approaching unity at T≳0.5K, YbRh2Si2 at zero magnetic field belongs to the quantum-critical fluctuation regime, where the single-particle spectral function has significant weight at both the small and large Fermi surfaces. This implies that, in this temperature range, any measurements sensitive to the Fermi surface will also see a significant spectral weight at the large Fermi surface. The angle-resolved photoemission spectroscopy experiments recently reported for YbRh2Si2 at T>1K are consistent with this expectation, and therefore support the association of the T*B line with the physics of Kondo destruction.

Cost-effective evolution of research prototypes into end-user tools: The MACH case study

Much of Software Engineering research needs to provide an implementation as proof-of-concept. Often such implementations are created as exploratory prototypes without polished user interfaces, making it difficult to (1) run user studies to validate the tool's contribution, (2) validate the author's claim by fellow scientists, and (3) demonstrate the utility and value of the research contribution to any interested parties. However, turning an exploratory prototype into a “proper” tool for end-users often entails great effort. Heavyweight mainstream frameworks such as Eclipse do not address this issue; their steep learning curves constitute substantial entry barriers to such ecosystems.In this paper, we present the Model Analyzer/Checker (MACH), a stand-alone tool with a command-line interpreter. MACH integrates a set of research prototypes for analyzing UML models. By choosing a simple command line interpreter rather than (costly) graphical user interface, we achieved the core goal of quickly deploying research results to a broader audience while keeping the required effort to an absolute minimum. We analyze MACH as a case study of how requirements and constraints in an academic environment influence design decisions in software tool development. We argue that our approach while perhaps unconventional, serves its purpose with a remarkable cost-benefit ratio.

Maximum velocity of self-propulsion for an active segment

The motor part of a crawling eukaryotic cell can be represented schematically as an active continuum layer. The main active processes in this layer are protrusion, originating from non-equilibrium polymerization of actin fibers, contraction, induced by myosin molecular motors, and attachment due to active bonding of trans-membrane proteins to a substrate. All three active mechanisms are regulated by complex signaling pathways involving chemical and mechanical feedback loops whose microscopic functioning is still poorly understood. In this situation, it is instructive to consider the problem of finding the spatial organization of standard active elements inside a crawling layer ensuring an optimal cost-performance trade-off. If we assume that (in the range of interest) the energetic cost of self-propulsion is velocity independent, we obtain, as an optimality criterion, the maximization of the overall velocity. We choose a prototypical setting, formulate the corresponding variational problem and obtain a set of bounds suggesting that radically different spatial distributions of adhesive complexes would be optimal depending on the domineering active mechanism of self-propulsion. Thus, for contraction-dominated motility, adhesion has to cooperate with ‘pullers’ which localize at the trailing edge of the cell, while for protrusion-dominated motility it must conspire with ‘pushers’ concentrating at the leading edge of the cell. Both types of crawling mechanisms have been observed experimentally.

Requirements for qualification of manufacture of the ITER Central Solenoid and Correction Coils

The manufacturing line of the ITER Correction Coils (CC) at ASIPP in Hefei (China) was completed in 2013 and the manufacturing line of the ITER Central Solenoid (CS) modules is under installation at General Atomic premises in Poway (USA). In both cases, before starting production of the first coils, qualification of the manufacturing procedures is achieved by the construction of a set of mock-ups and prototypes to demonstrate that design requirements defined by the ITER Organization are effectively met. For each qualification item, the corresponding mock-ups are presented with the tests to be performed and the related acceptance criteria. The first qualification results are discussed.

Formative research and development of innovative tools for “Better Outcomes in Labour Difficulty” (BOLD): study protocol

BackgroundMost complications during labour and childbirth could be averted with timely interventions by skilled healthcare providers. Yet, the quality and outcomes of childbirth care remains suboptimal in many health facilities in low-resource settings. To accelerate the reduction of childbirth-related maternal, fetal and newborn mortality and morbidity, the World Health Organization has initiated the “Better Outcomes in Labour Difficulty” (BOLD) project to address weaknesses in labour care processes and better connect health systems and communities. The project seeks to develop a “Simplified, Effective, Labour Monitoring-to-Action” tool (SELMA) to assist healthcare providers to monitor labour and take decisive actions more efficiently; and by developing an innovative set of service prototypes and/or tools termed “Passport to Safer Birth”, designed with communities and healthcare providers, to promote access to quality care for women during childbirth. This protocol describes the formative research activities to support the development of these tools.Methods/DesignWe will employ qualitative research and service design methodologies in eight health facilities and their catchment communities in Nigeria and Uganda. In the health facilities, focus group discussions (FGD) and in-depth interviews (IDI) will be conducted among different cadres of healthcare providers and facility administrators. In the communities, FGDs and IDIs will be conducted among women who have delivered in a health facility. We will use service design methods to explore women’s journey to access and receive childbirth care in order to innovate and design services around the needs and expectations of women, within the context of the health system.DiscussionThis formative research will serve several roles. First, it will provide an in-depth understanding of healthcare providers and health system issues to be accounted for in the final design and implementation of SELMA. Second, it will help to identify key moments (“touch points”) where women’s experiences of childbirth care are shaped, and where the overall experience of quality care could be improved. The synthesis of findings from the qualitative and service design activities will help identify potential areas for behaviour change related to the provision and experience of childbirth care, and serve as the basis for the development of Passport to Safer Birth.Please see related articles ‘http://dx.doi.org/10.1186/s12978-015-0027-6’ and ‘http://dx.doi.org/10.1186/s12978-015-0029-4’.

SiC Integrated Circuit Control Electronics for High-Temperature Operation

This paper is an important step toward the development of complex integrated circuit (IC) control electronics that have to attend to high-temperature environment power applications. We present in premiere a prototype set of essential mixed-signal ICs on SiC capable of controlling power switches and a lateral power MESFET able to operate at high temperatures, all embedded on the same chip. Also, we report for the first time the functionality of standard Si-CMOS topologies on SiC for the master–slave data flip-flop (FF) and data-reset FF digital building blocks designed with MESFETs. Concretely, we present the complete development of SiC-MESFET IC circuitry, able to integrate gate drivers for SiC power devices. This development is based on the mature and stable Tungsten–Schottky interface technology used for the fabrication of stable SiC Schottky diodes for the European Space Agency Mission BepiColombo.

Long Intergenic Non-Coding RNAs: Novel Drivers of Human Lymphocyte Differentiation.

Upon recognition of a foreign antigen, CD4+ naïve T lymphocytes proliferate and differentiate into subsets with distinct functions. This process is fundamental for the effective immune system function, as CD4+ T cells orchestrate both the innate and adaptive immune response. Traditionally, this differentiation event has been regarded as the acquisition of an irreversible cell fate so that memory and effector CD4+ T subsets were considered terminally differentiated cells or lineages. Consequently, these lineages are conventionally defined thanks to their prototypical set of cytokines and transcription factors. However, recent findings suggest that CD4+ T lymphocytes possess a remarkable phenotypic plasticity, as they can often re-direct their functional program depending on the milieu they encounter. Therefore, new questions are now compelling such as which are the molecular determinants underlying plasticity and stability and how the balance between these two opposite forces drives the cell fate. As already mentioned, in some cases, the mere expression of cytokines and master regulators could not fully explain lymphocytes plasticity. We should consider other layers of regulation, including epigenetic factors such as the modulation of chromatin state or the transcription of non-coding RNAs, whose high cell-specificity give a hint on their involvement in cell fate determination. In this review, we will focus on the recent advances in understanding CD4+ T lymphocytes subsets specification from an epigenetic point of view. In particular, we will emphasize the emerging importance of non-coding RNAs as key players in these differentiation events. We will also present here new data from our laboratory highlighting the contribution of long non-coding RNAs in driving human CD4+ T lymphocytes differentiation.

Cross-Polarized Radiation in a Cylindrical Dielectric Resonator Antenna: Identification of Source, Experimental Proof, and Its Suppression

In this communication, cross-polarized (XP) radiation of cylindrical dielectric resonator antenna (CDRA) has been investigated with a view to identify the source of the same. The study starts with a conjecture, which has been established through a systematic study using simulated results. The inference has been verified experimentally with the help of a set of prototypes. HEM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21δ</sub> has been identified as the XP generating mode and a small metallic grounded post of circular cross section has been conceived to perturb the same to prove the conjecture. This insight has been extended to demonstrate a practical application indicating a possibility of achieving up to 8-10-dB suppression in XP radiation from a CDRA.

Scaling up graph-based semisupervised learning via prototype vector machines.

When the amount of labeled data are limited, semisupervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via l1 -regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning.

The FC7 AMC for generic DAQ & control applications in CMS

The FC7 is a flexible, μTCA compatible Advanced Mezzanine Card (AMC) for generic data acquisition/control applications. Built around the Xilinx Kintex-7 FPGA, the FC7 provides developers with a platform which has access to a large array of configurable I/O, primarily delivered from on-board FPGA Mezzanine Card (FMC) sockets. Targeting users of high-speed optical links in high energy physics experiments, the board is capable of driving and receiving links up to 10 Gbps. This paper presents test results from the first set of pre-production prototypes and reports on FC7 uses and applications towards upgrades in CMS.

Illusory Feature Slowing

Upright static faces are widely thought to recruit holistic representations, whereby individual features are integrated into nondecomposable wholes for recognition and interpretation. In contrast, little is known about the perceptual integration of dynamic features when viewing moving faces. People are frequently exposed to correlated eye and mouth movements, such as the characteristic changes that accompany facial emotion, yawning, sneezing, and laughter. However, it is unclear whether the visual system is sensitive to these dynamic regularities, encoding facial behavior relative to a set of dynamic global prototypes, or whether it simply forms piecemeal descriptions of feature states over time. To address this question, we sought evidence of perceptual interactions between dynamic facial features. Crucially, we found illusory slowing of feature motion in the presence of another moving feature, but it was limited to upright faces and particular relative-phase relationships. Perceptual interactions between dynamic features suggest that local changes are integrated into models of global facial change.