Miracle-dz/UniFAR-FTUs huggingface dataset:
Isotone Recursive Methods for Stationary Markovian Equilibrium in Overlapping Generations Models with Stochastic Nonclassical Production

Based on an order-theoretic approach, we derive su¢ cient conditions for the existence, characterization, and computation of minimal state space Markovian equilibrium decision processes (MEDPs) and stationary Markov equilibrium (SME) for a large class of stochastic overlapping generations models. In contrast to previous work, our focus is exclusively on constructive xed point methods. In addition, we extend results obtained in earlier work to the case of more general reduced-form stochastic production technologies that allow for a broad set of equilibrium distortions such as public policy distortions, social security, monetary equilibrium, and production nonconvexities. Our order-based methods provide monotone iterative algorithms the uniformly converge to extremal Markovian equilibrium decision proceesses. Further, those methods can be tied to the computation of extremal equilibrium invariant distributions. Our methods select equilibrium that avoid many of the problems associated with the existence of indeterminacies that have been well-documented in previous work. We study cases where MEDPs are unique continuous functions, as well as provide the rst results in the literature on the existence and computation of minimal state space MEDPS and SME for the case where capital income is not monotone. We show that our results on minimal state space MEDPs extend to the case of n-period stochastic OLG models. Finally, we conclude with examples common in macroeconomics such The authors would like to thank Elena Antoniadou, Robert Becker, Manjira Datta, Seppo Heikkilä, Len Mirman, Jayaram Sethuraman, Yiannis Vailakis, Itzhak Zilcha, and especially John Stachurski for very helpful discussions on topics related to this paper. All mistakes are our own.
Towards Modelling the Intended Purpose of Ontologies : A Case Study in Geography

As the Semantic Web technologies gain popularity, more organisations are providing semantic resources related to the geographical domain but are doing this based on ontologies that represent their own conceptualisations and needs. As a result: finding, understanding and evaluating relevant semantic resources is still very time consuming, cognitively demanding and often require a deep understanding of the domain and the used semantic technologies. To help address this problem, we are investigating how ontology purposes can be captured and used to facilitate ontology engineering. The paper presents an exploratory study which examines whether it is possible to identify generic categories to model the purpose of geography-related ontologies. As an output, we present an initial categorisation of ontology purposes and discuss how these categories can help us understand and reuse ontologies.
Expiratory flow limitation detected by forced oscillation and negative expiratory pressure

The within-breath change in reactance (ΔX̄rs) measured by forced oscillation technique (FOT) at 5 Hz reliably detects expiratory flow limitation in chronic obstructive pulmonary disease (COPD). The present study compared this approach to the standard negative expiratory pressure (NEP) method. In total, 21 COPD patients were studied by applying both techniques to the same breath and in 15 patients the measurements were repeated after bronchodilator. For each patient and condition five NEP tests were performed and independently scored by three operators unaware of the FOT results. In 180 tests, FOT classified 53.3% as flow limited. On average, the operators scored 27.6% of tests flow limited and 47.6% non-flow limited, but could not score 24.8%. The methods disagreed in 7.9% of cases; in 78% of these the NEP scores differed between operators. Bronchodilation reduced NEP and ΔX̄rs scores, with only the latter achieving significance. Averaging the operators′ NEP scores, a threshold between 24.6–30.8% of tidal volume being flow limited by NEP produced 94% agreement between methods. In conclusion, when negative expiratory pressure and forced oscillation technique were both available they showed good agreement. As forced oscillation technique is automatic and can measure multiple breaths over long periods, it is suitable for monitoring expiratory flow limitation continuously and identifying patients′ breathing close to the onset of expiratory flow limitation, where intermittent sampling may be unrepresentative.
Evaluation of Various Interference Models for Joint Routing and Scheduling in Wireless Mesh Networks

We explore radio resource allocation in wireless mesh networks, namely we study how to determine a space- and time-division pattern of transmissions, to deliver traffic to gateway nodes. We highlight link scheduling and routing issues, which we investigate in a cross-layer framework. To this end, an integer linear program is presented, where diverse constraints affect resource allocation; in particular, we focus in this paper on the key role played by the characterization of radio interference. Finally, we obtain numerical results through a genetic algorithm, and we are able to show that the wireless interference model adopted strongly affects the performance of the radio resource allocation; thus, an accurate representation of the wireless channel is necessary to obtain meaningful results.
Effect of hand jitter on video compression algorithms for mobile devices

We have conducted a sensitivity analysis to determine the PSNR reduction occurring from increased levels of hand jitter while recording video. To conduct this sensitivity analysis we proposed and utilized a hand jitter model. We observe from our analysis that increased hand jitter certainly lowers the PSNR. More importantly, we observe that the magnitude of the reduction in PSNR of increasing hand jitter is increased with less motion in the video. With this insight we developed a decision rule that decides when to activate a video stabilization algorithm. This decision rule requires the distinction between motion created by the scene and motion created by hand jitter as well as a mechanism to measure this motion.
Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading

In this work we tackled the task of Automatic Short Answer Grading (ASAG). While conventional ASAG research makes prediction mainly based on student answers referred as Answer-based, we leveraged the information about questions and student models into consideration. More speciﬁ-cally, we explore the Answer-based, Question, and Student models individually, and subsequently in various combined and composite models through feature engineering. Ad-ditionally, we extend the exploration of machine learning methods by utilizing Deep Belief Networks (DBN) together with other ﬁve classic classiﬁers. Our experimental results show that our proposed feature engineering models signiﬁ-cantly out-performed the conventional Answer-based model and among the six machine learning classiﬁers, DBN is the best followed by SVM, and Naive Bayes is the worst.
String Inference from Longest-Common-Prefix Array

The suffix array, perhaps the most important data structure in modern string processing, is often augmented with the longest common prefix (LCP) array which stores the lengths of the LCPs for lexicographically adjacent suffixes of a string. Together the two arrays are roughly equivalent to the suffix tree with the LCP array representing the tree shape. In order to better understand the combinatorics of LCP arrays, we consider the problem of inferring a string from an LCP array, i.e., determining whether a given array of integers is a valid LCP array, and if it is, reconstructing some string or all strings with that LCP array. There are recent studies of inferring a string from a suffix tree shape but using significantly more information (in the form of suffix links) than is available in the LCP array. We provide two main results. (1) We describe two algorithms for inferring strings from an LCP array when we allow a generalized form of LCP array defined for a multiset of cyclic strings: a linear time algorithm for binary alphabet and a general algorithm with polynomial time complexity for a constant alphabet size. (2) We prove that determining whether a given integer array is a valid LCP array is NP-complete when we require more restricted forms of LCP array defined for a single cyclic or non-cyclic string or a multiset of non-cyclic strings. The result holds whether or not the alphabet is restricted to be binary. In combination, the two results show that the generalized form of LCP array for a multiset of cyclic strings is fundamentally different from the other more restricted forms.
Image denoising via coded aperture photography

We present a novel image denoising method utilizing coded aperture photography. Our approach captures an image that is slightly optically defocused by a coded aperture. This allows us to more effectively reduce noise while high frequency of image structures are protected by the coded aperture image. We analyze the effectiveness of coded aperture in decoupling noise frequency from high frequency of image structures. A novel frequency-aware regularization is proposed to denoise and to restore sharp image from a noisy slightly out-of-focus coded aperture image. The effectiveness of our approach is demonstrated on various challenging examples with quantitative and qualitative comparisons to results of state-of-the-art denoising methods.
Phase diagram of the Anderson transition with atomic matter waves

We realize experimentally a cold atom system equivalent to the 3D Anderson model of disordered solids where the anisotropy can be controlled by adjusting an experimentally accessible parameter. This allows us to study experimentally the disorder vs anisotropy phase diagram of the Anderson metal-insulator transition. Numerical and experimental data compare very well with each other and a theoretical analysis based on the self-consistent theory of localization correctly discribes the observed behavior, illustrating the flexibility of cold atom experiments for the study of transport phenomena in complex quantum systems.
Vehicle path planning for complete field coverage using genetic algorithms

In farming operations, one of the fundamental issues facing farmer is the cost of running the farm. If the equipment the farmer is using can be made more efficient, the cost of farming will be reduced. One way of making agricultural equipment more efficient is to develop automated or autonomous functions for the equipment. One of the fundamental tasks for autonomous equipment is to plan the path for the equipment to travel. This paper reports the research on the feasibility of creating an automated method of path planning for autonomous agricultural equipment. Genetic algorithms were chosen to plan the paths with a primary goal of creating an optimal path guiding the equipment to completely cover a field while avoiding all known obstacles. Two example fields were designed for evaluating the feasibility of this concept on simple problems. While simulation results verified the feasibility of this conceptual path planning method, they also indicated that further development would be required before the algorithm could actually be implemented on agricultural equipment for real-world field applications. Keywords: Automonous equipment, genetic algorithms, off-road vehicle, path planning
Computer Aided Diagnosis of Cardiac Arrhythmias

The early detection of arrhythmia is very important for the cardiac patients. This is done by analyzing the electrocardiogram (ECG) signals and extracting some features from them. These features can be used in the classification of different types of arrhythmias. In this paper, we present three different algorithms of features extraction: Fourier transform (FFT), autoregressive modeling (AR), and principal component analysis (PCA). The used classifier is artificial neural networks (ANN). We observed that the system that depends on the PCA features give the highest accuracy. The proposed techniques deal with the whole 3 second intervals of the training and testing data. We reached the accuracy of 92.7083% compared to 84.4% for the reference that work on a similar data
Globally Attractive and Positive Invariant Set of the Lorenz System

In this paper, based on a generalized Lyapunov function, a simple proof is given to improve the estimation of globally attractive and positive invariant set of the Lorenz system. In particular, a new estimation is derived for the variable x. On the globally attractive set, the Lorenz system satisfies Lipschitz condition, which is very useful in the study of chaos control and chaos synchronization. Applications are presented for globally, exponentially tracking periodic solutions, stabilizing equilibrium points and synchronizing two Lorenz systems.
On Transductive Classification in Heterogeneous Information Networks

A heterogeneous information network (HIN) is used to model objects of different types and their relationships. Objects are often associated with properties such as labels. In many applications, such as curated knowledge bases for which object labels are manually given, only a small fraction of the objects are labeled. Studies have shown that transductive classification is an effective way to classify and to deduce labels of objects, and a number of transductive classifiers have been put forward to classify objects in an HIN. We study the performance of a few representative transductive classification algorithms on HINs. We identify two fundamental properties, namely, cohesiveness and connectedness, of an HIN that greatly influence the effectiveness of transductive classifiers. We define metrics that measure the two properties. Through experiments, we show that the two properties serve as very effective indicators that predict the accuracy of transductive classifiers. Based on cohesiveness and connectedness we derive (1) a black-box tester that evaluates whether transductive classifiers should be applied for a given classification task and (2) an active learning algorithm that identifies the objects in an HIN whose labels should be sought in order to improve classification accuracy.
Joint chunk, power and bit allocation in OFDMA based LTE systems

Orthogonal frequency division multiplexing (OFDM) is a key modulation technique to provide a high performance physical layer in modern wireless systems. Orthogonal frequency multiple accesses (OFDMA) is one of the best ways to resource allocations in OFDM systems. Resource allocation schemes are required to be optimized to improve performance of OFDMA based systems. Scope of this work is to study the performance of joint chunk, power and bit allocation in OFDMA based long term evolution (LTE) systems.
Stabilization of a Wind Farm Using Static VAR Compensators (SVC) Based Fuzzy Logic Controller

Wind energy is a cost competitive and environmentally clean renewable energy sources. Wind farm capacity connected to power system increasing rapidly worldwide. During transient conditions wind farm drive a large reactive power which in turn causes voltage instability. In this paper, Static VAR Compensator (SVC) based fuzzy logic controllers (FLC) has been implemented to improve transient stability and damping power oscillations of a wind farm connected to power system. Different fault types and different fault durations were considered for the study to investigate the effect of the SVC based FLC on system stability. The suggested fault types are, single line to ground fault, double line fault, and three lines to ground faults. The different duration faults are 50ms, 80ms and 100ms. Different locations are considered for the SVC at the studied system. The proposed controller provides the wind farm system with damping effect during transient condition and provides much smoother and quicker response in the post-fault conditions. The proportional plus integral (PI) controller is used for the comparative study. The studied system consists of wind farm represented by double fed induction generator (DFIG) connected to utility grid.
A visual front-end for a continuous pose-invariant lipreading system

Having an audio-visual automatic speech recognition (AVASR) system which can recognise what a speaker's says regardless of head position (i.e. left profile, front, right profile etc.), would be most useful as it enables this technology to be used in a host of realistic applications such as mobile phone and in-vehicle speech recognition. A major hurdle in achieving this goal is in developing a visual front-end which can effectively locate and track a user's face and facial features from a single camera. In this paper, we describe a visual front-end which incorporates a pose-estimator in conjunction with a parallel series of pose specific face and facial feature classifier based on a boosted cascade of simple classifiers devised by Viola and Jones [6]. Results of our visual front-end are tested on the CUAVE database. We also give lipreading results on the CUAVE database, which shows that AVASR whilst a speaker is moving their head is indeed achievable.
Investigation of chunk selection strategies in peer-assisted video-on-demand systems

The applications for peer-to-peer (P2P) architectures rec ently have evolved from straightforward file download to more demanding services like Videoon-Demand (VoD). Here, not only the known advantages of P2P overlays are essential, but also a timely delivery of video frames for their play back. Since the video is watched during the download process, this poses a major challenge, which has to be mastered to guarante e high service quality to the end user. One mechanism influencing the performance of a P 2P content distribution network is the chunk selection strategy in systems using mul ti-source download. This paper aims at systematically investigating the impact of the c unk selection strategies on the system behavior and its self-organization capabilities, a s well as the user perceived quality. In particular, we answer if just an adaptation of this strate gy suffices to make such systems viable for VoD services.
Real-Time Scheduling of Parallel Tasks on Fewest Processors

A polynomial-time scheduling algorithm is proposed for the real-time parallel tasks on multiprocessors. The algorithm always finds a feasible schedule using the fewest processors. The tasks have the properties of linear speedup, flexible preemption, bounded parallelism, arbitrary arrivals and sequential deadlines with the order of Last Come First Service(LCFS). The time complexity of the algorithm is (M2 ¿ N2) for M tasks and N processors, in the worst case.
MPR-based flooding with distributed fountain network coding

This paper describes DiFCod, a protocol which is designed to reduce the number of transmissions required for flooding in wireless ad-hoc networks using network coding. We propose a simple distributed coding scheme which can be applied locally at each node. The efficiency of network coding is further enhanced by applying multiple point relays (MPR). The simulation results shows that flooding can be efficiently performed with DiFCode. Using a theoretical analysis, we show that DiFCode ensures that any packet generated at any node is delivered to the entire network with high probability.
Investigating the Readiness of College Students for ICT and Mobile Learning: A Case Study from King Saud University

The purpose of this study was to draw an attention to the findings from a quantitative analysis of questionnaire surveys associated with the use of Information and Communication Technology (ICT) and mobile learning (m-Learning) in Education. We invited 317 undergraduate students studying at King Saud University (KSU) in Saudi Arabia then we reported on their experience for academic and personal intentions. We built on a study of undergraduate students and information technology adopted from the EDUCAUSE Center for Analysis and Research (ECAR). The results indicated that the readiness level of KSU students on using ICT and mobile learning is at a high level and there was a demand for institutional support of the mobile learning technology.
Quantum-inspired immune evolutionary algorithm based parameter optimization for mixtures of kernels and its application to supervised anomaly IDSs

Supervised anomaly intrusion detection systems (IDSs) based on Support Vector Machines (SVMs) classification technique have attracted much more attention today. In these systems, features of instances and the characteristic of kernels have great influence on learning and predict results. However, selecting feasible features and kernel parameters can be time-consuming as the number of features and the parameters of kernel increase. In this paper, a quantum-inspired immune evolutionary algorithm (QIEA) based parameter optimization approach is introduced to solve these problems. The mixtures of kernels are used for improving the learning and predict performance of SVM. At the same time, the real-coded chaotic QIEA is used for optimizing the parameters of mixtures of kernels. The KDDCuppsila99 dataset was used for performance comparison and the experiment results show that the proposed method is efficient competent with the Differential Evolution Algorithm (DEA).
Poriferan Chitin as a Versatile Template for Extreme Biomimetics

In this mini-review, we shall first cover a short history of the discovery of chitin isolated from sponges; as well as its evolutionarily ancient roots. Next, we will delve into the unique structural, mechanical, and thermal properties of this naturally occurring polymer to illuminate how its physicochemical properties may find uses in diverse areas of the material sciences. We show how the unique properties and morphology of sponge chitin renders it quite useful for the new route of “Extreme Biomimetics”; where high temperatures and pressures allow a range of interesting bioinorganic composite materials to be made. These new biomaterials have electrical, chemical, and material properties that have applications in water filtration, medicine, catalysis, and biosensing.
A Neuro-Fuzzy Multi Swarm FastSLAM Framework

FastSLAM is a framework for simultaneous localization using a Rao-Blackwellized particle filter. In FastSLAM, particle filter is used for the mobile robot pose (position and orientation) estimation, and an Extended Kalman Filter (EKF) is used for the feature location's estimation. However, FastSLAM degenerates over time. This degeneracy is due to the fact that a particle set estimating the pose of the robot loses its diversity. One of the main reasons for loosing particle diversity in FastSLAM is sample impoverishment. It occurs when likelihood lies in the tail of the proposal distribution. In this case, most of particle weights are insignificant. Another problem of FastSLAM relates to the design of an extended Kalman filter for landmark position's estimation. The performance of the EKF and the quality of the estimation depends heavily on correct a priori knowledge of the process and measurement noise covariance matrices (Q and R) that are in most applications unknown. On the other hand, an incorrect a priori knowledge of Q and R may seriously degrade the performance of the Kalman filter. This paper presents a Neuro-Fuzzy Multi Swarm FastSLAM Framework. In our proposed method, a Neuro-Fuzzy extended kalman filter for landmark feature estimation, and a particle filter based on particle swarm optimization are presented to overcome the impoverishment of FastSLAM. Experimental results demonstrate the effectiveness of the proposed algorithm.