Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained MMSE Approach To Equalizer Design

When applied to decision feed back equahzation (DFE), the conventional minimum mean square error (MMSE) design approach is shown to be deficient in performance, making a number of recent contributions in the literature questionable. By taking into account the adverse effects of the nonlinear recursive character of the DFE which makes its presence felt in the error propagation phenomenon, a modified MMSE criterion is developed. Our modified design approach is based on reducing the damaging effect of large taps in the feedback path by combining two MMSE designs so as to reduce symbol error rate.
Extending Fine-Grained Semantic Relation Classification to Presupposition Relations between Verbs

contrast to typical semantic relations between verbs, such as antonymy, synonymy or hyponymy, presupposition is a lexical relation that is not very well covered in existing lexical resources. It is also understudied in the field of corpus-based methods of learnin gs emantic relations. But presupposition is very important for the quality of automatic semantic and discourse analysis tasks. In this paper we present a corpus-based method for learning presupposition relations between verbs, embedded in a discriminative classification approach for fine-grained semantic relations. The focus of the present paper is to discuss methodological aspects of our approach including the choice of resources and data sets, the selection of features for classification, and design decisions regarding the annotation of fine-grained semantic relations between verbs.
O Error Bounds for Approximate Policy Iteration

In Dynamic Programming, convergence of algorithms such as Value Iteration or Policy Iteration results -in discounted problemsfrom a contraction property of the back-up operator, guaranteeing convergence to its fixedpoint. When approximation is considered, known results in Approximate Policy Iteration provide bounds on the closeness to optimality of the approximate value function obtained by successive policy improvement steps as a function of the maximum norm of value determination errors during policy evaluation steps. Unfortunately, such results have limited practical range since most function approximators (such as linear regression) select the best fit in a given class of parameterized functions by minimizing some (weighted) quadratic norm. In this paper, we provide error bounds for Approximate Policy Iteration using quadratic norms, and illustrate those results in the case of feature-based linear function approximation.
Objective identification of cloud regimes in the Tropical Western Pacific

Identifying cloud regimes and their role in the climate system can serve a multitude of purposes, ranging from a better understanding of clouds to guiding field experiments to improving the representation of clouds in models. This study describes early results in identifying cloud regimes from ISCCP data using cluster analysis. A simple algorithm for cloud regime identification is introduced and applied to data in the Tropical Western Pacific region. Four major cloud regimes, namely a shallow cumulus regime, a transparent isolated cirrus regime, thick cirrus with convection and a deep and probably organized convective regime are identified and their frequency of occurrence is quantified. The use of the regime information for various applications is discussed and the use of regime classifications for representativeness studies is presented using the ARM TWP sites as an example.
Throughput enhancement to the Cognitive Radio networks under the precaution is better than the cure (PBC) approach

In this paper, we have proposed a Cognitive Radio system where primary users uses the precautionary measures against the possible interferences that may disturb their quality of service (QOS), these interferences may be originated from any other primary user itself, possibly due to the effect of the hidden terminal problem or may be received from the secondary user end. The proposed approach is carried with the cooperation of the primary users where they perform the task of data transmission and spectrum sensing simultaneously, using our proposed receiver and frame structures. The proposed cognitive radio system works beyond the sensing-throughput tradeoff, overcomes the hidden terminal problem of the secondary as well as the primary users and increases awareness of the primary users against the interferences that can harm their performance, hence facilitates them with the maximum quality of service. More specifically, the probability of detection of the secondary users is increased dramatically even for the low signal to noise ratio (SNR) of the primary user received at the secondary user end. In other words, the licensed spectrum can be best exploited in an unlicensed manner even for the low SNR region, while allowing maximum protection to the primary users.
A Data Transfer in Wireless Sensor Networks Using AODV Protocol

Sensor networks present unique opportunities for a broad spectrum of applications such as industrial automation, situation awareness, tactical surveillance for military application and environmental monitoring, chemical/biological detection etc. Sensor Network can monitor ambient condition such as temperature, sound, light and others. Information is collected from many sensor devices for further consumer application in the Sensor Network. For selecting a cluster head, k-means algorithm will be used to find the cluster center. For hop to hop packet forwarding, AODV protocol has been used at network layer. All the simulations of the proposed idea will be simulated on Berkeley’s ns2 network simulator and the performance of the proposed scheme has been evaluated.
MPPT of Solar Energy Generating System with Fuzzy Control and Artificial Neural Network

In order to achieve maximum power of solar cell, we focus on the maximum power point tracking (MPPT) algorithm forming based on fuzzy control. The fuzzy control rules are adopted using artificial neural network with measured data. Compared the fuzzy inference systems (FISs) with the ideal FISs, there is only less than 2% of error of signal output. The simulation conclusions show the performance of MPPT algorithm becomes much precise and active with the help of fuzzy control and artificial neural network.
A Fast Soft Bit Error Rate Estimation Method

We have suggested in a previous publication a method to estimate the Bit Error Rate (BER) of a digital communications system instead of using the famous Monte Carlo (MC) simulation. This method was based on the estimation of the probability density function (pdf) of soft observed samples. The kernel method was used for the pdf estimation. In this paper, we suggest to use a Gaussian Mixture (GM) model. The Expectation Maximisation algorithm is used to estimate the parameters of this mixture. The optimal number of Gaussians is computed by using Mutual Information Theory. The analytical expression of the BER is therefore simply given by using the different estimated parameters of the Gaussian Mixture. Simulation results are presented to compare the three mentioned methods: Monte Carlo, Kernel and Gaussian Mixture. We analyze the performance of the proposed BER estimator in the framework of a multiuser code division multiple access system and show that attractive performance is achieved compared with conventional MC or Kernel aided techniques. The results show that the GM method can drastically reduce the needed number of samples to estimate the BER in order to reduce the required simulation run-time, even at very low BER.
Interval Constraint Solving Using Propositional SAT Solving Techniques

In order to facilitate automated reasoning about large Boolean combinations of non-linear arithmetic constraints involving transcendental functions, we extend the paradigm of lazy theorem proving to intervalbased arithmetic constraint solving. Algorithmically, our approach deviates substantially from “classical” lazy theorem proving approaches in that it directly controls arithmetic constraint propagation from the SAT solver rather than completely delegating arithmetic decisions to a subordinate solver. From the constraint solving perspective, it extends intervalbased constraint solving with all the algorithmic enhancements that were instrumental to the enormous performance gains recently achieved in propositional SAT solving, like conflict-driven learning combined with non-chronological backtracking.
A framework for improvement in homogeneity of fluorescence and bright field live cell images using fractional derivatives

Cell segmentation has been an important area in modern biological image processing applications. The most commonly used cell segmentation algorithms are region based and rely on the homogeneity value of the image intensities in the region of interest to be segmented. But the highly inhomogeneous behavior of cell region and background causes feature overlapping between the two leading to misclassification and poor segmentation results. This paper proposes a method to improve the homogeneity of the cell images. The existing clustering criterion for bias correction has been improved upon by introducing fractional differential in the algorithm. The proposed method has been tested on two different sets of 2D cell images, and improved performance results over the existing method are obtained.
OPTIMIZING THE SCENARIO POSITIONS FOR ROBUST RADIATION THERAPY TREATMENT PLANNING

Complex external beam radiation therapy treatment plans are susceptible to errors. Robust optimization methods that take uncertainties into account during the optimization can alleviate the effects of the errors. For many robust methods, the possible error realizations are discretized into scenarios. The methods then aim at achieving high quality plans in all scenarios simultaneously. The choice of scenarios has high impact on the quality of the plans: when the optimization is aimed at high target coverage in scenarios that are incompatible with healthy tissue sparing in other scenarios, the plan quality in all scenarios may suffer. It can then be beneficial to reduce the range of errors the scenarios represent. To accomplish this, we propose a method that determines how large errors that can be accounted for in robust optimization of intensity-modulated photon and proton therapy. It does so by optimizing the scenario positions along predefined directions simultaneously with the direct machine parameter optimization, under constraints on target coverage and healthy tissue sparing. For both modalities, scenario position optimization was applied to a prostate case and a lung case. The optimization reduced the ranges of errors that were accounted for compared to the a priori ranges. The determined scenario positions were used in robust optimization with fixed scenarios. This resulted in plans that satisfied a substantially larger number of constraints in the determined scenarios and moreover a larger number of constraints in the a priori scenarios than the plans optimized with a priori scenario positions.
ℒ1 adaptive and extremum seeking control applied to roll parametric resonance in ships

This paper proposes a robust control approach to stabilize roll parametric resonance, a dangerous nonlinear resonance phenomenon for ships. To that matter, we extend a recent extremum seeking speed and heading control strategy to stabilize roll parametric resonance in ships, aiming at robustness. Based on previous results where extremum seeking was shown to be capable of effectively reducing parametrically excited roll motions by a combined variation of the ship's forward speed and heading angle, two major modifications are suggested. Firstly, the speed and heading controllers are formulated in the framework of ℒ1 adaptive control which guarantees robustness while still having fast adaptation. By doing so, robustness is increased with respect to model uncertainty, lack of knowledge, and bounded external disturbances. Secondly, the extremum seeking loop is modified towards limit cycle minimization, which replaces the objective function of the previously presented control approach, thus relaxing the assumption that the frequency range for roll parametric resonance is a priori known. In simulations, the proposed overall robustification approach is shown to effectively stabilize the roll oscillations due to roll parametric resonance.
The role of angiogenesis in implant dentistry part I: Review of titanium alloys, surface characteristics and treatments

Background Angiogenesis plays an important role in osseointegration process by contributing to inflammatory and regenerative phases of surrounding alveolar bone. The present review evaluated the effect of titanium alloys and their surface characteristics including: surface topography (macro, micro, and nano), surface wettability/energy, surface hydrophilicity or hydrophobicity, surface charge, and surface treatments of dental implants on angiogenesis events, which occur during osseointegration period. Material and Methods An electronic search was performed in PubMed, MEDLINE, and EMBASE databases via OVID using the keywords mentioned in the PubMed and MeSH headings regarding the role of angiogenesis in implant dentistry from January 2000-April 2014. Results Of the 2,691 articles identified in our initial search results, only 30 met the inclusion criteria set for this review. The hydrophilicity and topography of dental implants are the most important and effective surface characteristics in angiogenesis and osteogenesis processes. The surface treatments or modifications of dental implants are mainly directed through the enhancement of biological activity and functionalization in order to promote osteogenesis and angiogenesis, and accelerate the osseointegration procedure. Conclusions Angiogenesis is of great importance in implant dentistry in a manner that most of the surface characteristics and treatments of dental implants are directed toward creating a more pro-angiogenic surface on dental implants. A number of studies discussed the effect of titanium alloys, dental implant surface characteristic and treatments on agiogenesis process. However, clinical trials and in-vivo studies delineating the mechanisms of dental implants, and their surface characteristics or treatments, action in angiogenesis processes are lagging. Key words:Angiogenesis, dental implant, osseointergration.
Representation Theory of Analytic Holonomy C* Algebras

Integral calculus on the space of gauge equivalent connections is developed. Loops, knots, links and graphs feature prominently in this description. The framework is well--suited for quantization of diffeomorphism invariant theories of connections. The general setting is provided by the abelian C* algebra of functions on the quotient space of connections generated by Wilson loops (i.e., by the traces of holonomies of connections around closed loops). The representation theory of this algebra leads to an interesting and powerful ``duality'' between gauge--equivalence classes of connections and certain equivalence classes of closed loops. In particular, regular measures on (a suitable completion of) connections/gauges are in 1--1 correspondence with certain functions of loops and diffeomorphism invariant measures correspond to (generalized) knot and link invariants. By carrying out a non--linear extension of the theory of cylindrical measures on topological vector spaces, a faithful, diffeomorphism invariant measure is introduced. This measure can be used to define the Hilbert space of quantum states in theories of connections. The Wilson--loop functionals then serve as the configuration operators in the quantum theory.
Spatial effects of transeuropean networks: Preliminary results from a spatial computable general equilibrium analysis

This paper quantifies regional welfare effects of new transport links, which are going to be established in the framework of Trans-European Networks (TEN). It is confined to the regional welfare effects resulting from the use of the new links for trading goods and services. Effects from the construction phase, from financing and maintenance are not considered. Use of the links for other than trade purposes, such as commuting, tourism, leisure trips et cetera are not considered either. Welfare implications of new transport links are quantified by simulating effects of transport distance reductions in a spatial computable general equilibrium model. We model a static equilibrium for two sectors (local goods and tradables) and many regions. Firms in the tradables sector supply a large number of symmetrical product varieties under monopolistic competition. Trade between regions is costly, with costs depending on transport distances through a given transport network as well as on national trade impediments. The paper explains the formal structure of the model, the calibration procedure, and the data basis for implementing the model to a system of more than 800 regions covering the entire European space. Numerical results for several scenarios regarding regarding the establishment of TEN links are presented.
Spatial Outlier Detection: A Graph-Based Approach

Spatial outliers are the spatial objects whose nonspatial attribute values are quite different from those of their spatial neighbors. Identification of spatial outliers is an important task for data mining researchers and geographers. A number of algorithms have been developed to detect spatial anomalies in meteorological images, transportation systems, and contagious disease data. In this paper, we propose a set of graph-based algorithms to identify spatial outliers. Our method first constructs a graph based on k-nearest neighbor relationship in spatial domain, assigns the nonspatial attribute differences as edge weights, and continuously cuts high- weight edges to identify isolated points or regions that are much dissimilar to their neighboring objects. The proposed algorithms have two major advantages compared with the existing spatial outlier detection methods: accurate in detecting point outliers and capable of identifying region outliers. Experiments conducted on the US Housing data demonstrate the effectiveness of our proposed algorithms.
Dependency-conflict detection in real-time collaborative 3D design systems

One core issue in real-time collaborative 3D design systems is dependency-conflict caused by concurrent access to object replicas in Dependency Graph (DG) states -- the common underlying structure of 3D design systems. Prior work has provided an Operational Transformation (OT) solution to dependency-conflict resolution, assuming dependency-conflict can be detected. But dependency-conflict detection is a complex and challenging issue because dependency relations among targets of concurrent operations -- a necessary condition for dependency-conflict -- is dynamic in nature and the detection must refer to prior DG states that are often unavailable during dependency-conflict detection. Conflict detection was never a difficult issue in prior OT work based on well-defined linear data models because conflict relations are readily derivable from operation position parameters without having to refer to prior document states. This work is the first to recognize and solve the dependency-conflict detection problem, which complements prior work in dependency-conflict resolution to provide a complete solution to dependency-conflict problem in real-time collaborative DG-based systems. Both theoretical verification and real implementation have been applied to validate the proposed dependency-conflict detection solution.
The adjustment speed of debt maturity structures: Evidence from African countries

ABSTRACT This paper aims to examine the adjustment speed of debt maturity structures within the context of African countries. Dynamic adjustment models using system GMM proposed by Blundell and Bond (1998) were employed to analyze data pertaining to 986 non-financial firms drawn from nine African countries. We find evidence that firms in Africa adjust their debt maturity structures to a target. Our results also indicate that the legal protection afforded to investors and the efficiency of the legal system enhance the pace at which firms in Africa rebalance their debt maturity structures. The evidence shows that firms in richer and fast growing economies experience comparatively rapid adjustments to their debt maturity structures than is the case in poorer and slow-growth economies. In addition, the size and growth prospects of firms positively enhance adjustment speed while the distance from optimal maturity structures deters the rebalancing pace. These findings signify the role that agency, bankruptcy and transaction costs, liquidity pressure and financial flexibility play in the maturity structures decision of firms in Africa.
Performance of 4- and 8-state TCM schemes with asymmetric 8-PSK in fading channels

For Rayleigh fading channels, Ricean fading channels with a small parameter and shadowed Rician channels with the code design criteria are to maximize the effective length as well as the minimum product distance of the code. In this paper, we use two different asymmetric 8-PSK signal sets in 4-state and 8-state rate 2/3 trellis-coded modulation (TCM) schemes and show performance gain over TCM schemes with symmetric 8-PSK constellations. The performance gain is due to an increase in the minimum product distance compared to that of the best known TCM schemes of comparable states using symmetric 8-PSK signal sets while the effective length remains same. Simulation is performed over the Rayleigh, Ricean, and shadowed Rician fading channels to demonstrate the performance gain of the asymmetric 8-PSK TCM schemes over the symmetric 8-PSK TCM.
A FRAMEWORK FOR 3D INTERACTION TECHNIQUES

This paper presents a software architecture for 3D interaction techniques (ITs) and an object oriented, toolkit-independent framework that implements such architecture. ITs are composed of basic filters connected in a dataflow, where virtual input devices and objects in the scene are sources of information. An execution model defines the general flow of information between filters. This framework has been designed to be extensible: new information types, new input devices, new execution models, or new interaction techniques can easily be added. Application specific code and application specific ITs are seamlessly integrated into this architecture. Applications that use this framework are designed and written in two standard languages: UML and XML. XML documents are parsed to generate code for a particular VR toolkit or environment.
Bayesian compressive sensing using Monte Carlo methods

The problem of reconstructing a signal from compressively sensed measurements is solved in this work from a Bayesian perspective. The proposed reconstruction solution differs from previous Bayesian methods in that it numerically evaluates the posterior of the sparse solution. This allows the method to utilize any kind of information on the signal without the need to evaluate the posterior in closed form. Specifically, the method uses multi-stage sampling together with a greedy subroutine to efficiently draw information directly from the likelihood and any prior distribution on the signal, including a sparsity prior. The approach is shown to accurately represent the Bayesian belief on the sparse solution based on noisy compressively sensed signals.