Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering to Find Exemplar Terms for Keyword Extraction

— This paper addresses the problem of keyword extraction from conversations, with the target of utilizing these watchwords to recover, for each short discussion piece, somewhat range of conceivably pertinent reports, which may be prescribed to members. In any case, even a brief piece contains a miscellanea of words, that square measure conceivably known with some themes; additionally, utilizing a programmed discourse acknowledgment (ASR) framework presents slips among them. On these lines, it's laborious to surmise properly the info wants of the discussion members. we have a tendency to initial propose a calculation to get rid of decisive words from the yield of Associate in Nursing ASR framework (or a manual transcript for testing), that makes utilization of theme demonstrating ways and of a sub standard prize capability that supports differing qualities within the magic word set, to coordinate the potential differing qualities of subjects and reduce ASR commotion. At that time, we have a tendency to propose a way to infer varied locally isolated inquiries from this decisive word set, keeping in mind the tip goal to amplify the probabilities of creating at any rate one pertinent proposal once utilizing these inquiries to look for over land Wikipedia. The projected systems square measure assessed as way as significance as for discussion items from the Fisher, AMI, and ELEA colloquial corpora, appraised by some human judges. The scores demonstrate that our proposition moves forward over past systems that think about simply word return or theme closeness, and speaks to a promising declare a report recommender framework to be used as a section of discussions.
Diagnosis of Large Prediction Errors on Recurvature of Typhoon Fengshen (2008) in the NCEP-GFS Model

The steering-flow analysis based on potential vorticity (PV) diagnosis is used to examine the reasons why the National Centers for Environmental Prediction Global Forecast System (NCEP-GFS) model showed large track forecast errors with over-recurving movement in Typhoon Fengshen (2008). In particular, two forecasts initialized at 0000 UTC 19 and 20 June 2008 are demonstrated in this study. The deep-layer-mean (DLM) steering flow between 925 and 300 hPa with tropical cyclone components filtered out is directed to the west or northwest in the analysis field, which can account for the continuous westward and northwestward movement in the best track. However, the DLM steering flow is shown more toward the north in the forecast fields. Four distinct PV features associated with the corresponding subtropical high, monsoon trough, continental high, and midlatitude trough are identified to diagnose their balanced steering flows around the storm. The result based on PV analysis indicates that the reduced westward steering flow in the forecast field is mainly attributed to the subtropical high which is over-predicted to extend southwestward, as well as the continental high with underestimated coverage, as characterized by the geopotential height at 500 hPa. The steering flow associated with the monsoon trough plays an essential role while Typhoon Fengshen (2008) experiences northward recurvature in both analysis and forecast fields. Therefore, the associated reduced westward steering flow in the NCEP-GFS model leads to the over-recurvature of Fengshen.
Mass Flow Rate Scaling of the Continuum-Based Equations Using Information Preservation Method

Kinetic theory based numerical scheme such as direct simulation Monte Carlo (DSMC) and information preservation (IP) schemes properly solve micro-nano flow problems in transition and free molecular regimes. However, the high computational cost of these methods encourages the researchers toward extending the applicability of the continuumbased equations beyond the slip flow regime. In addition to correct velocity profile, the continuum-based equations should predict accurate mass flow rate magnitude. The secondorder velocity slip models derived from the kinetic theory provide accurate velocity profiles up to Kn=0.5; however, they yield erroneous mass flow rate magnitudes because the basic Navier-Stokes equations are invalid for transition regime calculation. To remedy this shortcoming, the rarefaction effects must be considered on dynamic viscosity, i.e., µ=µ(Kn), in order to achieve correct mass flow rate magnitude despite using the kinetic-based slip model. Using shear stress distribution of IP simulations, we develop an analytical formula for dynamic viscosity for the early transition regime and use it to modify the continuumbased equations to improve more accurate mass flowrate magnitudes. Before using the IP results, we compare the accuracy of IP solution with the standard DSMC, the linearized Boltzmann and the continuum-based analytical solutions. Using the viscosity coefficient obtained from IP, the analytical expression for the mass flow rate is derived. We show that it provide accurate solutions for mass flow rate for the region of 0.1
Study on Process Parameters and Control Technology of One-Step Shaping for Thin Layer Paving of Polymer Concrete

In order to make thin layer paving one-step shaping during polymer concrete pavement construction, the relationship model between vibration parameters, structure parameters and the degree of compaction is established in this paper. Based on the resonance theory, this model is integrated with a mathematical model to study the dynamic properties and the interaction mechanism between vibration mechanism and paving material during compaction. The model represents the structure parameters, vibration parameters, driving speed, degree of compaction and the interaction between them. Combining real engineering with adequate quality control technology, the pavement is finished by only one step paving. The results show that, when the vibrating frequency is greater than the optimal vibration frequency and driving speed the driving speed is less than the speed threshold as well as between 2.5~3m/min, the initial compaction degree of the paving is more than 96%. Thereby improving construction efficiency and guaranteeing the construction quality. Thus, the optimal range about vibrating frequency and speed can serve as a valuable reference for one-step shaping of thin layer paving and the dynamic model can also be used to study the compaction process of pavements during the construction.
Hydrodynamical chemistry simulations of the Sunyaev–Zel'dovich effect and the impacts from primordial non-Gaussianities

The impacts of Compton scattering of hot cosmic gas with the cosmic microwave background radiation (Sunyaev-Zel’dovich effect, SZ) are consistent ly quantified in Gaussian and nonGaussian scenarios, by means of 3D numerical, N-body, hydrodynamic simulations, including cooling, star formation, stellar evolution and metal pollu tion (He, C, O, Si, Fe, S, Mg, etc.) from different stellar phases, according to proper yields f or individual metal species and massdependent stellar lifetimes. Light cones are built through the simulation outputs and samples of one hundred maps for the resulting temperature fluctuatio ns are derived for both Gaussian and non-Gaussian primordial perturbations. From them, we estimate the possible changes due to early non-Gaussianities on: SZ maps, probability distribution functions, angular power spectra and corresponding bispectra. We find that the differ ent growth of structures in the different cases induces significant spectral distortions only in models with large non-Gaussian parameters, fNL . In general, the overall trends are covered by the non-linea r, baryonic evolution, whose feedback mechanisms tend to randomize the gas behaviour and homogenize its statistical features, quite independently from the backgr ound matter distribution. Deviations due to non-Gaussianity are almost undistinguishable for fNL . 100, remaining always at few-per-cent level, within the error bars of the Gaussian sc enario. Rather extreme models with fNL � 1000 present more substantial deviations from the Gaussian case, overcoming baryon contaminations and showing discrepancies up to a factor of a few in the spectral properties.
Integrated Readout Circuit for Cross-Correlation Based Ultrasonic Ranging

Distance measurement using ultrasonic waves is employed in a wide range of industrial applications. In this thesis our main goal is to investigate the possibility of processing ultrasonic signals with a ? -modulator and to process its bitstream output signal with the cross-correlation technique, while being independent of the transducer that is used. A compact chip has been designed in a 0.5 ? m CMOS process for the readout of the transducer. We present simulation results that show that the system is able to accurately estimate the distance to a target when low-Q transducers are used. Furthermore, we present measurement results in which we show that the system is able to determine the zero-crossings of the received echo with a resolution of 0.169 mm.
Multiclass Sequential Feature Selection and Classification Method for Genomic Data

This paper presents an efficient multiclass sequential feature selection and classification (mk-SS) method using gene expression signatures. The development of this method employs 10-fold cross-validation to ensure stability. The efficiency of this method is assessed through the misclassification error rate and some other performance measures. The performances of the mk-SS were compared with the classification results of the Support Vector Machines (SVM) over five published multiclass microarray datasets. The results showed that the mk-SS method efficiently selects the informative gene biomarkers for proper classification of the biological groups of the tissue samples. This method competes favourably with SVM in terms of prediction accuracy while it outperforms the SVM in 80% of cases considered. The quality of the features selected by mk-SS algorithm was validated by hybridizing the feature selection scheme of the mk-SS into the standard SVM algorithm which significantly improves the predictive power of the standard SVM method. This work has shown that classification of various cancer type using gene expression profiles is feasible especially when the endpoints are of multi-category. Keywords: k-SS, mk-SS, Support Vector Machines, Microarray, Misclassification error rate
Isolation and characterization of developmentally regulated novel target site from embryonic chick heart

Differential gene expression is the primary determinant of numerous biological processes such as cell differentiation, proliferation, organogenesis, tumor progression and apoptosis. In that context, regulatory proteins play pivotal roles and determine cell fate in all physiological conditions of differentiation, development and disease. As these regulatory proteins are present in extremely small amount in cells, their isolation and identification from tissues is impracticable. These regulatory proteins bind to the target sites (DNA elements) located in the promoter, enhancer and other regulatory region of the genome. These target sites control the developmental expression of genes. In the present paper we have isolated and characterized one novel target site (GTGTT) which is developmentally expressed during chick heart development.
Nature and physicochemical conditions of crystallization in the South Dehgolan intrusion, NW Iran: mineral-chemical evidence

The South Dehgolan intrusion in the Sanandaj-Sirjan Zone ranges in composition from calc-alkalic monzogabbro to syenite to alkalic granite. This suite is composed mainly of variable proportions of quartz, K-feldspar, plagioclase, biotite, hornblende, and pyroxene. The plagioclase composition varies between albite to labradorite. The biotites are Mg- to Fe-rich in the monzogabbro to granite, respectively. The amphiboles are calcic and the composition varies from magnesiohornblende to actinolite. Clinopyroxene compositions fall in the diopside-augite field. The average calculated near-solidus crystallization temperatures are 774 °C, 655 °C, and 775 °C for the monzogabbro-monzodiorite, syenite, and granite, respectively. Calculated average pressures of emplacement are 1.7, 1.3, and 1.9 kbar for the monzogabbro-monzodiorite, syenite, and granite rocks, respectively, crystallizing at different respective depths of about 6.7, 5.0, and 7.3 km. The interpreted oxygen fugacities in the monzogabbro-monzodiorite and syenite rocks are typical of arc magmas, with oxygen fugacities above the Ni-NiO buffer. Water contents in the monzogabbro-monzodiorite and syenite were calculated to range from 4.7 to 4.4 wt. % and in the granite is 3.8 wt. %. High water and volatile contents in the monzogabbro to syenite may have allowed the magma to reach shallower crustal levels. During the evolution of this magmatic system to higher silica contents, there was an increase in the activity of oxygen and decrease in the temperature and the emplacement depth from the monzogabbro-monzodiorite to syenite rocks; this is consistent with the typical evolution of the granitoid rocks. However, the calculated higher temperature and pressure and low $f$O2 and H2O content in the later granite reflects notable differences in its origin and crystallization conditions. The range in mineral compositions is this intrusive suite is consistent with a relationship to subduction of the Neotethys oceanic crust beneath the Central Iranian microcontinent, although there was a change from calc-alkaline to alkaline magmatism.
A New Electricity Price and Load Uncertainty Prediction Method based on Optimal Neural Networks for Deregulated Electricity Power Markets

In recent years, Short-term load and price forecast has always been a key issue for power system operation. In regulated power systems, Short-term load forecast is an important tool for reliable and economic operation of power systems. Many operating decisions are based on Short-term load forecasting, such as dispatch scheduling of generating capacity, reliability analysis, security assessment and maintenance plan for the generators. On the other hand, electricity price variation is more important and effective factors for all power market participants. Bidding strategy, risk control, investment decisions, demand and supply balancing and power system reliability and other power market applications are highly depended to load and price uncertainty. In this paper a new intelligent hybrid method has been proposed to price and load uncertainty prediction. The proposed method consists of an improved version of particle swarm optimization algorithm to fine tuning the main predictor system’s adjustable parameters. The price and load variation intervals have been predicted by predictor system based on multi-layer neural networks. The proposed method has been examined in some well-known power markets. © 2016 Journal of Energy
Growth-Inequality Relationship. An Analytical Approach and Some Evidence for Latin America

The explanation of economic inequality and its relationship to growth has produced a great amount of research. Kuznets (1955) formulated the “inverted-U” hypothesis according to which inequality increases in the initial levels of development to decrease later on, after a certain point of return. This proposition has been the subject of great attention with many theoretical and empirical contributions. In this paper we present an analytical approach to the growth-inequality relationship, including not only the most common measures, but also new indicators based on the information theory.The work also includes the estimation of the growth-inequality relationship and the contrast of the Kuznets´ hypothesis within Latin America.
Quantum Theory of Geometry II: Volume operators

Abhay Ashtekar, Jerzy Lewandowski 1 Center for Gravitational Physics and Geometry Department of Physics, Penn State, University Park, PA 16802-6300, USA 2 Instytut Fizyki Teoretycznej, Uniwersytet Warszawski ul. Hoza 69, 00-681 Warszawa, Poland 3 Max-Planck-Institut fur Gravitationsphysik, Albert Einstein Institut, Schlaatzweg 1, D-14473 Potsdam, Germany 4 Erwin Schrodinger International Institute for Mathematical Sciences Boltzmanngasse 9, A-1090 Vienna, Austria
The effect of absorptive capacity on innovation performance: Empirical research on the mediating effects of knowledge transfer efficiency

The objective of this study is to analyze the mechanism of absorptive capacity in the course of innovation for the purpose of improving the innovation performance of the firms. This paper set the knowledge transfer efficiency as the intermediate variable based on the theory of capability level, analyzing the path and effect on innovation performance in all dimensions of the absorptive capacity. The research results show that absorptive capacity as a kind of high-level capability, the absorptive capacity can influence the innovation performance through the knowledge transfer efficiency. Each dimension of the absorption capacity produces different effects. Firms should pay attention to and treated differently in the process of technological innovation.
Comparison of Three Analytical Methods for the Precise Calculation of Cogging Torque and Torque Ripple in Axial Flux PM Machines

A comparison between different analytical and finite-element (FE) tools for the computation of cogging torque and torque ripple in axial flux permanent-magnet synchronous machines is made. 2D and 3D FE models are the most accurate for the computation of cogging torque and torque ripple. However, they are too time consuming to be used for optimization studies. Therefore, analytical tools are also used to obtain the cogging torque and torque ripple. In this paper, three types of analytical models are considered. They are all based on dividing the machine into many slices in the radial direction. One model computes the lateral force based on the magnetic field distribution in the air gap area. Another model is based on conformal mapping and uses complex Schwarz Christoffel (SC) transformations. The last model is based on the subdomain technique, which divides the studied geometry into a number of separate domains. The different types of models are compared for different slot openings and permanent-magnet widths. One of the main conclusions is that the subdomain model is best suited to compute the cogging torque and torque ripple with a much higher accuracy than the SC model.
Clustering Based on Node Density in Heterogeneous Under-Water Sensor Network

An underwater sensor network comprise of sensors and vehicles to perform numerous tasks. In underwater ad-hoc sensor network acoustic signals are transmitted through multi-hop sequence so as to save sensors' energy and to achieve longer life time. Re- charging batteries of deep water deployed sensors is practically not feasible. Clustering is the best strategy to achieve efficient multi-hopping, where cluster head is made responsible to collect local data and forward it to the sink. Cluster-head selection is the challenging job in a cluster, as it loses its energy in transmitting its own data and aggregated data, as compared to other sensors. In this paper we have proposed an Under Water Density Based Clustered Sensor Network (UWDBCSN) scheme using heterogeneous sensors. The scheme utilizes two types of sensors: one having high energy capacity, working as cluster head, having small quantity and other are ordinary sensors in huge quantity. Further cluster-head selection is based on node degree i.e. the density of the sensors in a region. The proposed scheme is found to be more energy efficient helps in extending the life time of underwater sensor networks.
Effects of PH 20 Hyaluronidase Inhibition In Vivo Following Cerebral White Matter Injury

Human infants born prematurely between 23-32 weeks have a predisposition toward sustaining white matter injury (WMI). In survivors of preterm birth, hypoxic-ischemic (HI) damage to the cerebral white matter is the leading cause of a spectrum of neurological and motor deficits and in severe cases, cerebral palsy (CP). H-I triggers extracellular matrix (ECM) breakdown leading to production of inhibitory ECM components. H-I also induces vigorous proliferation of oligodendrocyte progenitor cells (OPCs) leading to accumulation of the pre-oligodendrocytes (preOLs). However, these preOLs fail to differentiate and generate myelin producing oligodendrocytes (OLs). Mechanistically, ECM breakdown blocks repair and regeneration of myelin following WMI as hyaluronic acid (HA), a major component of the brain ECM, is digested into bioactive fragments (fHA). Previous studies have shown that following WMI, HA degraded by PH20 hyaluronidase to fHA prevents differentiation of preOLs to mature OLs, but whether fHA plays a role in H-I mediated OPC proliferation is unknown. To determine if hyaluronidase plays a role in vivo to promote OPC proliferation, we utilized a neonatal rodent preterm model of H-I and a new selective PH20 inhibitor, SuBr3. Our hypothesis tests whether SuBr3 mediated inhibition of PH20 activity will also attenuate the OPC proliferation response observed after neonatal WMI. We found a significant decrease in Ki67 cells in WMI lesions in the SuBr3 treated animals relative to all other control groups, suggesting total cell proliferation was attenuated and mediated by PH20 hyaluronidase activity. Future experiments will analyze whether SuBr3 treatment also results in changes in the total pool of OL lineage cells and preOLs in the WMI lesions.
State of the art modulator approaches for continuous time-of-flight range finding

An overview is given of different state of the art modulator approaches useable as demodulating component in Continuous Time-of-Flight range finding. The first is a spatial optical modulator which can be used in conjunction with a conventional image sensor; the second is a single bulk device combining both optical detection and modulation in the substrate. A third performs optical detection and modulation by combining fast conventional photo-detectors with subsequent analog circuitry to achieve modulation and amplification. The different design and implementation challenges are discussed and the properties of the different approaches at both sensor level and 3D-data post processing level are presented.
A brief look at web architecting

We are familiar with the term Web Engineering, but not quit so with Web Architecting. The activities of Web Architecting precede all other activities in a project. Working trend and working habit are tending toward more mobility. Small and light weight computing devices will out sale stationary units by the year 2013. We-based application and Web-based services are used to deploy 'Software as a Service' of Cloud Computing. The Web is becoming more important than ever.