Miracle-dz/UniFAR-FTUs huggingface dataset:
GRAPHICAL PERSON-MACHINE INTERACTIVE APPROACH FOR BUS SCHEDULING. (ABRIDGMENT)

A highly informative graphical technique for the problem of finding the least number of buses required to service a given schedule of trips is described. The purpose is to develop the methodology for variable bus scheduling in which trip departure times can vary within acceptable tolerances. This is a continuation of a research project concerned with the problem of fixed scheduling where the timetable of trips and length of trip times are fixed. The motivation for this study comes from the Israel National Bus Carrier, Egged, which is responsible for scheduling an average of 54,400 daily trips by about 5200 buses. Consequently, the research takes on a practical nature. The approach used is based on the deficit-function theory where the deficit function at time t defines the total number of trips that have departed from a given terminal k less the number of trips that have arrived at k up to an including time t. The method developed is capable of aiding the scheduler to perform his or her tasks through a person-machine conversational mode. It allows the scheduler to interject his or her own practical suggestions and to see immediately their effects on the final schedule. The method is based on the deficit-function graphical multiterminal display, which guides and advises the schedulers of possible directions in reducing fleet size. In addition, this methodology aids schedulers in evaluating the results of their own suggestions. The potential of this person-machine interactive bus-scheduling procedure is presented by using several practical examples.
Mandatory alcohol and drug treatment: what is it and does it work?

Mandatory (or compulsory) treatment is a tool often reached for by governments during moments of alcohol and/or illicit drug crises. It has come to the fore as a potential policy panacea in recent times in response to ongoing concern about the rise of crystal methamphetamine (“ice”) use, regional concerns over alcohol abuse, and the desire to address alcohol and other drug (AOD) problems in young people. However, in public debate there is a significant lack of clarity about what mandatory treatment is, who benefits from it, and how it might be implemented. In addition, there is confusion about the existing evidence-base and the extent to which mandatory treatment is an effective and cost-effective approach for people with AOD problems. The aim of this bulletin is to: 1. Provide an overview of the various models of mandatory treatment currently used in Australia and internationally 2. Summarise the evidence base for each of these models 3. Assess the relative merits of the different types of mandatory treatment models, especially their effectiveness in addressing AOD problems.
Optimizing Neural Networks that Generate Iimages

Optimizing Neural Networks that Generate Images Tijmen Tieleman Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2014 Image recognition, also known as computer vision, is one of the most prominent applications of neural networks. The image recognition methods presented in this thesis are based on the reverse process: generating images. Generating images is easier than recognizing them, for the computer systems that we have today. This work leverages the ability to generate images, for the purpose of recognizing other
Simulation Based Analysis of AODV, DSR, ZRP Routing Protocols

A Mobile Ad hoc Network (MANETs) is a kind of wireless ad-hoc network which allows mobile computer users for wireless communication to set up a short lived network for communication needs during movement. The mobile nodes are free to move randomly, thus making the network topology dynamic. Each node operates as a router to forward packets and also acts as an end system. Therefore routing is one of the most concerned areas in these networks. Normal routing protocols which work well in fixed networks do not show the same performance in Mobile Ad Hoc Networks because the requirements differ in the two scenarios. In wireless networks, routing protocols should be more dynamic so that they quickly respond to topological changes which occur frequently in these networks. Various routing protocols have been designed which aims at establishment of correct and efficient routes between a pair of mobile nodes. This study is a comparison of three routing protocols proposed for mobile ad-hoc networks. The protocols are: Ad-hoc On demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Zone Routing Protocol (ZRP). The performances of the protocols are analyzed in terms of throughput, average end-to-end delay and normalized routing load, which shall provide an insight about the sensitivity of the protocols. Simulation based analysis of the protocols have been carried out by using NS-2, an open source simulator. Index Terms—AODV, DSR, MANET, NS-2, ZRP.
Tea polyphenol-assisted green synthesis of Ag-nanodiamond hybrid and its catalytic activity towards 4-nitrophenol reduction

ABSTRACT Herein, we describe an environmentally friendly strategy to synthesize Ag nanoparticles on Nanodiamond (ND) by utilizing natural adhering and reduction properties of tea polyphenols (TPs). Firstly, the surface of ND was modified by TPs through their natural adhering ability. Then, the modified TPs functioned as reducing agents for reducing Ag precursor in situ. The synthesis process, structure and its physicochemical properties of the nanohybrid were tracked and characterized by UV-Vis, FTIR, TGA, HRTEM, XRD and XPS. It has been shown that Ag nanoparticles with uniform size and distribution were anchored successfully on the surface of ND. The ND supported Ag nanoparticles displayed highly water dispersibility due to the hydrophilicity of TPs, as is very important in heterogeneous catalysis. The catalytic performance of this nanohybrid was evaluated comprehensively by catalyzing 4-nitrophenol reduction by sodium borohydride, which is often used as a model reaction, showing excellent catalytic activity and recycling stability. GRAPHICAL ABSTRACT
Representation and selection of time-varying signals by single cortical neurons

JONATHAN VINCENT TOUPS: Representation and selection of time-varying signals by single cortical neurons. (Under the direction of Paul Tiesinga.) I present a theoretical effort to develop tools and statistical analysis of neural responses to repeated presentations of identical time varying stimuli. Such experiments may produce responses characterized by regions of elevated firing separated by longer periods of low firing rate referred to as “events. ” Unlike previous methods, which find events based on a firing rate threshold, I present a four parameter, reproducible method which first discovers “spike patterns” (subsets of trials with similar spike timing) using unsupervised clustering and a spike train metric, followed by the use of an interspike interval threshold to detect events. I present results from in vitro data showing that the precision of the resulting events is higher than that estimated using a firing rate threshold technique; events within a single spike pattern may be very precise, even if they overlap in time across spike patterns. This analysis provides a model of neural activity which preserves information about spike patterns which can be used to generalize single unit recordings to multi-unit activity. I also present a statistical test to characterize whether events are correlated with one another. A new insight provided is that the choice of time scale for the metric space analysis should maximize the information in the distances between spike trains, regardless of specific timescales in the data. The event finding procedure works well for data sets with more than 20 trials, and with events which are well separated within spike patterns. I also present a comparison of two methods for selecting one of two stimuli present in the receptive field of a single cortical neuron. In the first, clustering of excitatory
Mechanically Verified Hardware Implementing an 8-Bit Parallel IO Byzantine Agreement Processor

Consider a network of four processors that use the Oral Messages (Byzantine Generals) algorithm of Pease, Shostak and Lamport to achieve agreement in the presence of faults. Bevier and Young have published a functional description of a single processor that, when interconnected appropriately with three identical others, implements this network under the assumption that the four processors step in synchrony. By formalizing the original Pease, etal work, Bevier and Young mechanically proved that such a network achieves fault tolerance. In this paper we develop, formalize and discuss a hardware design that has been mechanically proved to implement their processor. In particular, we formally define mapping functions from the abstract state space of the Bevier-Young processor to a concrete state space of a hardware module and state a theorem that expresses the claim that the hardware correctly implements the processor. We briefly discuss the Brock-Hunt Formal Hardware Description Language which permits designs both to be proved correct with the Boyer-Moore theorem prover and to be expressed in a commercially supported hardware description language for additional electrical analysis and layout. We briefly describe our implementation.
Learning Faster by Discovering and Exploiting Object Similarities

In this paper we explore the question: “Is it possible to speed up the learning process of an autonomous agent by performing experiments in a more complex environment (i.e., an environment with a greater number of different objects)? ” To this end, we use a simple robotic domain, where the robot has to learn a qualitative model predicting the change in the robot's distance to an object. To quantify the environment's complexity, we defined cardinal complexity as the number of objects in the robot's world, and behavioural complexity as the number of objects' distinct behaviours. We propose Error reduction merging (ERM), a new learning method that automatically discovers similarities in the structure of the agent's environment. ERM identifies different types of objects solely from the data measured and merges the observations of objects that behave in the same or similar way in order to speed up the agent's learning. We performed a series of experiments in worlds of increasing complexity. The results in our simple domain indicate that ERM was capable of discovering structural similarities in the data which indeed made the learning faster, clearly superior to conventional learning. This observed trend occurred with various machine learning algorithms used inside the ERM method.
A Pragmatic Media-Sharing Device for ATSC Mobile DTV Broadcasting

The recent proliferation of smart and portable devices has made multimedia streaming services available in mobile communication networks, which sometimes causes heavy traffic and network congestion problems. In contrast, there are no network congestion issues in mobile broadcasting networks. Due to the unidirectional property of mobile broadcasting networks, it is possible to provide multimedia streaming services efficiently. This paper, therefore, proposes a pragmatic mediasharing device capable of receiving ATSC Mobile DTV broadcast and relaying it to several smart and portable devices through Wi-Fi. The proposed device provides both multimedia streaming and data services of ATSC Mobile DTV broadcast and works well with smart devices based on different mobile operating systems such as Android and iOS.
Multi-carrier parallel combinatory DS/CDMA system: bandwidth efficient multiple access scheme for indoor wireless communication

As a data transmission rate must be increased as required to support in the future high-speed wireless communication systems, the conventional DS/CDMA scheme suffers considerably from an intensive processing requirement for the increasing spreading rate to combat the inter-chip interference due to multipath fading inherent in the wireless channel. A parallel combinatory (PC) CDMA/OFDM scheme is considered as one possible variant access scheme to realize a bandwidth efficient transmission at high data rate while maintaining the high-capacity feature of the DS/CDMA scheme. This scheme combines the parallel combinatory feature of the existing PC/CDMA to achieve the high bandwidth efficiency and the orthogonal carrier multiplexing feature of OFDM to reduce the multiple user interference among sub-carriers. Our analysis of the bit error rate for the asynchronous CDMA system investigates the performance characteristics of the proposed system and shows the performance comparison with other types of multi-carrier DS/CDMA systems.
PCA and Wavelet Packet Decomposition

The correct recognition rate (CRR) and implementation speed are two evaluation criteria for face recognition system. However, it is difficult to boost them when images are taken under different conditions. In this paper, the performance of a recognition method using wavelet packet decomposition (WPD) and two-directional twodimensional principal component analysis ((2D) 2 PCA) is explored. First, plot images are obtained via twolevel WPD on original image. And then, the feature matrixes of these plot images are extracted using (2D) 2 PCA. Finally, the method is constructed by fusing the feature matrixes of ‘successful’ plot images properly chosen. Experiments on images with different illumination, expressions, and poses from PIE, Yale, and UMIST indicate that the proposed method can get a higher correct recognition rate than performing (2D) 2 PCA on original image.
Beyond simple model-free reinforcement learning in human decision making

Over the last two decades, there has been a large scale effort in cognitive neuroscience to understand learning and decision making from the perspective of simple model-free reinforcement learning algorithms. This interest was invigorated in the mid 1990’s, when it was realized that the phasic activity of midbrain dopaminergic neurons resembles reward prediction errors. The algorithms studied formalize the notion of learning from past experiences through trial and error. Although important, there are many aspects of behavior they cannot explain. More recent work has begun to fill in some of these gaps by borrowing yet additional ideas from computational reinforcement learning. One line of inquiry has concentrated on aligning goal-directed behavior, which resembles the common sense notion of “planning”, with model-based reinforcement learning. This work has aimed to understand how the brain is able to learn the world model prescribed by the model-based framework, and to characterize the neural correlates of the value functions it predicts. This thesis adds to this work by offering two separate, but related, algorithmic accounts of how the brain may be able to actually map the world model into a decision. Existing data are examined and new experiments are performed. A second line of inquiry has concentrated on understanding behavior from the perspective of hierarchical reinforcement learning. The thesis makes two contributions to this area as well. First, it is shown that the brain codes pseudo-reward prediction errors, a prediction error in response to a faux reward signal that is used to train skills that are not in themselves useful, but that may be used to achieve other means. Second, an optimality framework is provided for understanding which skills are most beneficial to have when confronted with an ensemble of tasks.
Online Gaming: Real Time Solution of Nonlinear Two-Player Zero-Sum Games Using Synchronous Policy Iteration

Games provide an ideal environment in which to study computational intelligence, offering a range of challenging and engaging problems. Game theory (Tijs, 2003) captures the behavior in which a player’s success in selecting strategies depends on the choices of other players. One goal of game theory techniques is to find (saddle point) equilibria, in which each player has an outcome that cannot be improved by unilaterally changing his strategy (e.g. Nash equilibrium). The H∞ control problem is a minimax optimization problem, and hence a zero-sum game where the controller is a minimizing player and the disturbance a maximizing one. Since the work of George Zames in the early 1980s, H∞ techniques have been used in control systems, for sensitivity reduction and disturbance rejection. This chapter is concerned with 2-player zero-sum games that are related to the H∞ control problem, as formulated by (Basar & Olsder, 1999; Basar & Bernard, 1995; Van Der Shaft, 1992). Game theory and H-infinity solutions rely on solving the Hamilton-Jacobi-Isaacs (HJI) equations, which in the zero-sum linear quadratic case reduce to the generalized game algebraic Riccati equation (GARE). In the nonlinear case the HJI equations are difficult or impossible to solve, and may not have global analytic solutions even in simple cases (e.g. scalar system, bilinear in input and state). Solution methods are generally offline and generate fixed control policies that are then implemented in online controllers in real time. In this chapter we provide methods for online gaming, that is for solution of 2-player zerosum infinite horizon games online, through learning the saddle point strategies in real-time. The dynamics may be nonlinear in continuous-time and are assumed known. A novel neural network adaptive control technique is given that is based on reinforcement learning techniques, whereby the control and disturbance policies are tuned online using data generated in real time along the system trajectories. Also tuned is a ‘critic’ approximator structure whose function is to identify the value or outcome of the current control and disturbance policies. Based on this value estimate, the policies are continuously updated. This is a sort of indirect adaptive control algorithm, yet, due to the direct form dependence of the policies on the learned value, it is affected online as direct (‘optimal’) adaptive control. Reinforcement learning (RL) is a class of methods used in machine learning to methodically modify the actions of an agent based on observed responses from its environment (Doya,
Time Synchronisation Error Correction for BiSAR Using Phase History of Direct-Path Signal

Abstract To compensate the time synchronisation error, the direct-path signal is used in some bistatic synthetic aperture radar (BiSAR) systems. It turns out that an additional error is added in the translational-variant BiSAR echo data when the direct-path signal is directly used to compensate the time synchronisation error by aligning the direct-path signal in the same row. Since the additional error is modulated on the phase history of the direct-path signal, it can be estimated by unwrapping the phase history. Therefore, a time synchronisation error correction method using phase history of direct-path signal is presented, and it can compensate the additional error correctly.
An Efficient Data Clustering Algorithm over IRIS Dataset

We are proposing an efficient data clustering mechanism with incremental clustering algorithm with genetic feature, Clustering is a mostly unsupervised procedure and the majority of the clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set. In our approach we are performing incremental clustering on IRIS dataset for optimal clusters apart from the traditional approaches.
Feature-based generalisation as a source of gradient acceptability*

Abstract Phonological judgements are often gradient: blick>? bwick>*bnick>**bzick. The mechanisms behind gradient generalisation remain controversial, however. This paper tests the role of phonological features in helping speakers evaluate which novel combinations receive greater lexical support. A model is proposed in which the acceptability of a string is based on the most probable combination of natural classes that it instantiates. The model is tested on its ability to predict acceptability ratings of nonce words, and its predictions are compared against those of models that lack features or economise on feature specifications. The proposed model achieves the best balance of performance on attested and unattested sequences, and is a significant predictor of acceptability even after the other models are factored out. The feature-based model's predictions do not completely subsume those of simpler models, however. This may indicate multiple levels of evaluation, involving segment-based phonotactic probability and feature-based gradient phonological grammaticality.
Modified Subtraction Coronary CT Angiography with a Two-Breathhold Technique: Image Quality and Diagnostic Accuracy in Patients with Coronary Calcifications

Objective To evaluate a modified subtraction coronary computed tomography angiography (CCTA) technique with a two-breathhold approach in terms of image quality and stenosis grading of calcified coronary segments and in the detection of significant coronary stenosis in segments with severe calcification. Materials and Methods The institutional board approved this study, and all subjects provided written consent. A total of 128 patients were recruited into this trial, of which 32 underwent subtraction CCTA scans and invasive coronary angiography (ICA). The average Agatston score was 356 ± 145. In severely calcified coronary segments, the presence of significant (> 50%) stenosis was assessed on both conventional CCTA and subtraction CCTA images, and the results were finally compared with ICA findings as the gold standard. Results For severely calcified segments, the image quality in conventional CCTA significantly improved from 2.51 ± 0.98 to 3.12 ± 0.94 in subtraction CCTA (p < 0.001). In target segments, specificity (70% vs. 87%; p = 0.005) and positive predictive value (61% vs. 79%, p < 0.01) were improved using subtraction CCTA in comparison with conventional CCTA, with no loss in the negative predictive value. The segment-based diagnostic accuracy for detecting significant stenosis was significantly better in subtraction CCTA than in conventional CCTA (area under the receiver operating characteristic curve, 0.94 vs. 0.85; p = 0.03). Conclusion This modified subtraction CCTA method showed lower misregistration and better image quality in patients with limited breathhold capability. In comparison with conventional CCTA, modified subtraction CCTA would allow stenosis regrading and improve the diagnostic accuracy in coronary segments with severe calcification.
Using Decision Tree and Response Surface Methodology to Review Chronic Periodontitis Profile in Pakistan: a Preliminary Case Study

Chronic periodontitis is among the most prevalent oral diseases in the world. Apart from its destructive outcomes in the oral cavity, there is a strong evidence of the potential association between chronic periodontitis and systemic diseases such as cardiovascular disease, diabetes, and preterm low birth weight. In this paper, the potential contributing factors which lead to chronic periodontitis will be determined among the patients attending Islamabad Dental Hospital by response surface methodology (RSM) and decision tree (DT) analysis. The results of this study show that the prevalence of severe chronic periodontitis is high in patients who have not brushed their teeth (23.5%) and for the case without the severity of chronic periodontitis (61.1%). Whereas, from the RSM analysis, it is shown that there is an association between chronic periodontitis, diabetic status, and frequency of smoking. However, using DT analysis, age and frequency of brushing were the best predictors of severe chronic periodontitis. This present study concludes that by using RSM and DT analysis can predict a better forecasting result in future for the decision making among the decision maker.