Miracle-dz/UniFAR-FTUs huggingface dataset:
Education, Education, Education: Reforming England's Schools

Tony Blair said his three priorities were Education, Education, Education. Andrew Adonis played a decisive role in turning this slogan into a reform programme. This book describes his quest to transform standards and opportunities in England's schools, and his ambition to make English education truly world class. The reinvention of the comprehensive school was the central Blair project. In 1997 a majority of comprehensives were failing or seriously underperforming, with calamitous consequences for society and the economy. Academies replaced comprehensives by a radically new form of independent state school characterised by strong leadership and an ethos of aspiration, success and social mobility. Adonis tells the story of academies, from the germ of a reform idea in the late 1990s to the national movement for educational transformation they have become today. Adonis argues that wider reform is imperative to make England's schools world-class and sets out a manifesto for change, including higher salaries and better training and selection to make teaching the nation's most prestigious profession, and further radical innovation in the creation of new and better schools to tackle school drop-out and the challenges of the future. Education, Education, Education is optimistic about the power of politics to transform society for the better, and practical about the ways to make it happen.
Agricultural policies and farm structures: agent-based modelling and application to EU-policy reform

The development of competitive and efficient agricultural structures has been one of the central goals of agricultural policy making in addition to ensuring a fair standard of living for farmers. To achieve these goals, the agricultural sector in most industrialised nations has long been the subject of government intervenÂ­tions. However, many agricultural policies have worked counteractively to these goals by creating distortions in the use of resources. Against this background, the emergence of new and innovative modelling methods such as agent- based models, in addition to ever-inÂ­creasing computing capacities has offered new possibilities to model adjustment reactions and to quantify the impact of agriÂ­cultural policies. This thesis takes up these new methodologies and applies them to the modelling and evaluation of agricultural policy impacts on regional strucÂ­tural change. At the centre of the thesis is the development of the spatial and dynamic simulaÂ­tion model of regional agricultural structures AgriPoliS (Agricultural Policy Simulator). The core of AgriPoliS is the understanding of a regional agricultural structure as an agent-based system, i.e., a system of interacting heterogeneous agents. The thesis contributes to a deeper unÂ­derstanding of strucÂ­tural change dynamics and factors causing structural change. It is studied whether and to what extent policy changes can facilitate structural adjustment towards a more efficient and competitive agricultural structure.
Measurability and Computability

The conceptual relation between the measurability of quantum mechanical observables and the computability of numerical functions is re-examined. A new formulation is given for the notion of measurability with finite precision in order to reconcile the conflict alleged by M. A. Nielsen [Phys. Rev. Lett. 79, 2915 (1997)] that the measurability of a certain observable contradicts the Church-Turing thesis. It is argued that any function computable by a quantum algorithm is a recursive function obeying the Church-Turing thesis, whereas any observable can be measured in principle.
Education 14-19: Critical Perspectives.

Education 14-19 - divided and divisive. Part 1 Analysis - policy development: aims, values and the curriculum patterns in development a critique of NVQs and GNVQs teachers' views of 14-19 education competition, choice and hierarchy in a post-16 education and training market the 14-19 curriculum in private schools. Part 2 Examples - 14-19 policy and practice: race and ethnicity in education 14-19 girls in the Sixth Form disruptive behaviour and the educational wasteland transition to what - young pople with special educational needs the ASDAN Award - a celebration of professional practice.
On Weighted vs Unweighted Versions of Combinatorial Optimization Problems

We investigate the approximability properties of several weighted problems, by comparing them with the respective unweighted problems. For an appropriate (and very general) definition of niceness, we show that if a nice weighted problem is hard to approximate within r, then its polynomially bounded weighted version is hard to approximate within r? o(1). Then we turn our attention to specific problems, and we show that the unweighted versions of MIN VERTEX COVER, MIN SAT, MAX CUT, MAX DICUT, MAX 2SAT, and MAX EXACTkSAT are exactly as hard to approximate as their weighted versions. We note in passing that MIN VERTEX COVER is exactly as hard to approximate as MIN SAT. In order to prove the reductions for MAX 2SAT, MAX CUT, MAX DICUT, and MAX E3SAT we introduce the new notion of “mixing” set and we give an explicit construction of such sets. These reductions give new non-approximability results for these problems.
Analysis of Longitudinal Earnings Data: American Scientists 1960-70

The major findings of this study are as follows: (1) Simple cross section estimates grossly underestimate cohort profiles during the period 1960-70. Furthermore the growth in earnings is not uniform across experience groups and more recent vintages tend to have steeper profiles in most fields. Consequently the rate of return or present value comparisons based on cross sections are likely to be misleading even if the standard adjustment for growth is made. (2) For purposes of estimating mean profiles and mean effects of variables estimates based on pooled independent cross sections are quite close to those based on the more expensive longitudinal data. (3) There are important persistent unmeasured individual effects on both the level and growth of earnings. Consequently, individuals with the same observed characteristics will still have a wide variance in their permanent income.
Faster Algorithms for Finding Minimal Consistent DFAs

We describe exbar, a powerful new algorithm for the exact inference of minimal deterministic automata from given training data. This algorithm achieves the highest performance ever on a set of graded benchmark problems that has been posted by Arlindo Oliveira. In addition, we note that the inexact program ed-beam, which resulted from the Abbadingo DFA learning competition, also does well on this benchmark set, despite being designed for larger problems.
A Server-side Approach to Privacy Policy Matching

With the increasing use of online services that require sharing of information there is a need for Privacy Enhancing Technology tailored for personal information control. Commonly, web privacy is handled through matching of privacy policies and user preferences using software agents on the client side. In this paper, we propose a new approach to privacy policy matching we denote server-side matching. By moving the matching logic from the client to the server, the client is alleviated from the resource consuming process of obtaining and matching policies and the service provider is able to adapt services to users' privacy preferences. We describe the architecture of a general solution and a prototype implementation of selected parts. The solution has only been subject to rudimentary testing, but our initial evaluation is promising.
Stochastic recursive inclusion in two timescales with an application to the Lagrangian dual problem

In this paper we present a framework to analyze the asymptotic behavior of two timescale stochastic approximation algorithms including those with set-valued mean fields. This paper builds on the works of Borkar and Perkins & Leslie. The framework presented herein is more general as compared to the synchronous two timescale framework of Perkins & Leslie, however the assumptions involved are easily verifiable. As an application, we use this framework to analyze the two timescale stochastic approximation algorithm corresponding to the Lagrangian dual problem in optimization theory.
The range alignment algorithm for high-resolution range profile of multiple target

Aiming at the problem of the difficulty in the range alignment of HRRP because of more than one target in the window of radar image, this paper propose a alignment method for multiple targets in the target group by using an image segmentation method. First, we accumulate the HRRP of the target, and get the HRRP process diagram. By calculating the correlation coefficient between each frame of HRRP and the reference HRRP, the unaligned HRRP sequence is aligned roughly in frequency domain. After rough alignment, the image segmentation method is used to segment the range image of each target. In the end, the envelope alignment for the HRRP of each target is finished using the least entropy method. The algorithm is simple and fast, and can achieve a better alignment effect on multi object tracking. The effectiveness of the proposed method is verified by the analysis of the simulation data.
Hands-free speaker identification based on spectral subtraction using a multi-channel least mean square approach

A dereverberation method based on generalized spectral subtraction (GSS) using a multi-channel least mean square (MCLMS) approach achieved significantly improved results on speech recognition experiments compared with conventional methods. In this study, we employ this method for hands-free speaker identification. The GSS-based dereverberation method using clean speech models degrades speaker identification performance, although it is very effective for speech recognition. One reason may be that the GSS-based dereverberation method causes distortion such as distortion characteristics between clean speech and dereverberant speech. In this study, we address this problem by training speaker models using dereverberant speech, which is obtained by suppressing reverberation from arbitrary artificial reverberant speech. We also propose a method that combines various compensation parameter sets to improve speaker identification and provide an efficient computational method. The speaker identification experiment was performed on large-scale farfield speech, with reverberant environments different to the training environments. The proposed method achieved a relative error reduction of 87.5%, compared with conventional cepstral mean normalization with beamforming using clean speech models, and 44.8% compared with reverberant speech models.
An Efficient Sequential Covering Algorithm for Explaining Subsets of Data

Given a subset of data that differs from the rest, a user often wants an explanation as to why this is the case. For instance, in a database of flights, a user may want to understand why certain flights were very late. This paper presents ESCAPE, a sequential covering algorithm designed to generate explanations of subsets that take the form of disjunctive normal rules describing the characteristics ({attribute, value} pairs) that differentiates the subsets from the rest of the data. Our experiments demonstrate that ESCAPE discovers explanations that are both compact, in that just a few rules cover the subset, and specific, in that the rules cover the subset but not the rest of the data. Our experiments compare ESCAPE to RIPPER, a popular, traditional rule learning algorithm and show that ESCAPE’s rules yield better covering explanations. Further, ESCAPE was designed to be efficient, and we formally demonstrate that ESCAPE runs in loglinear time.
Chapter 1 COOPERATIVE DIVERSITY Models , Algorithms , and Architectures

Cooperative diveristy allows a collection of radio terminals that relay signals for each other to emulate an antenna array and exploit spatial diversity in wireless fading channels. For a variety of processing algorithms and transmission protocols, performance improvements in terms of transmission rate and reliability have been demonstrated. This chapter summarizes some low-complexity processing algorithms and transmission protocols for cooperative diversity, summarizes performance predictions in terms of information-theoretic outage probability, and how such constructs can be integrated into wireless network architectures, both
Design and control of a 1DOF power assist robot for lifting objects based on human operator's unimanual and bimanual weight discrimination

In this paper, we designed and controlled a 1 DOF power assist robot for lifting objects based on human operator's unimanual and bimanual weight discrimination. At first, we established psychophysical relationships between the actual weights and the power assisted weights for the objects lifted with the power assist robot. We also determined the excess of the load forces that humans applied while lifting objects with the robot. In everyday life, human decides to employ one or two hands to manipulate objects on the basis of object's physical characteristics. Hence, the psychophysical relationships were established and the excess of the load forces were determined for two protocols independently. The protocols were: 1) unimanual lift, and 2) bimanual lift. Then, we modified the control system of the power assist robot based on the psychophysical relationships and the load force characteristics. The modified control system reduced the excessive load forces applied by humans and thus enhanced maneuverability, operability, naturalness, ease of use, stability, safety etc. of the robot system significantly. Then, we suggested using the findings to design power assist robots for carrying heavy objects in various industries.
Financial vulnerability of small business owners

This study examines the financial vulnerability ofsmall business owners using data from the 2001 Survey of Consumer Finances.Financial vulnerability is determined by the extent to which income and wealthare derived from the same source. The findings suggest that business ownersface unique financial vulnerability because of their reliance on the businessas both a source of income and wealth. Business owners may have insufficient diversification when relying on thebusiness as an asset to fund retirement. Among business owners, farmers are themost vulnerable; their proportions of total income and total portfolioattributable to the business are higher than other business types. (PublicationAbstract)
Morphological component decomposition combined with compressed sensing for image compression

Basing on the fact that the cartoon and texture in one image have different morphological characteristics, we propose a new method to compress image. Combining RDWT, the dictionary sparsely representing the cartoon, and WAT, the dictionary sparsely representing the texture, the presented model can effectively obtain the cartoon and texture. Then, we reconstruct the compressed cartoon by the combination of Contourlet Transform and Compressed Sensing (CS) and reconstruct the compressed texture by the combination of single layer discrete wavelet transform (SL-DWT) and Compressed Sensing (CS). The reconstructed image will be obtained by superposing the compressed cartoon and texture. As the experimental results show, the new method has good performances for preserving large scale structure and mainly details under the low sampling rate, and ensuring the cartoon completion and texture clear. Moreover, it has higher compression rates.
EVOLVING APPROACHES TO CONSERVATION: INTEGRAL ECOLOGY AND CANADA'S GREAT BEAR RAINFOREST

Abstract This case study applies Integral Ecology to analyze the broad range of strategies environmentalists have undertaken to create protected areas and change forest practices in the Great Bear Rainforest, British Columbia, Canada. Rainforest conservation efforts in the region promoted holistic, trans-disciplinary solutions and fostered agreement among diverse stakeholders, modeling an Integral Ecology approach. Environmentalists worked locally and globally, engaging with economic, cultural, political, and scientific systems to create change. The campaign involved transformations at personal and cultural levels that have enabled negotiated solutions involving over twenty million acres of rainforest on British Columbia's coast.
A GENERALIZED PERSIDSKII THEOREM AND ITS APPLICATIONS TO NONSMOOTH GRADIENT DYNAMICAL SYSTEMS

Abstract A generalized Persidskii-like theorem is derived and shown to be applicable to the stability analysis of a class of gradient dynamical systems with discontinuous right hand sides. These dynamical systems arise from the steepest descent technique applied to a variety of problems suitably formulated as constrained minimization problems. The problems susceptible to this approach include linear programming problems and specifically the k-winners-take-all problem, the problem of solving underdetermined linear systems arising in least squares support vector machines, and quadratic programming problems associated to the support vector machine approach to classification. The advantage of the proposed analysis is the derivation of simple convergence conditions.
Deep brain stimulation of pallidal versus subthalamic for patients with Parkinson’s disease: a meta-analysis of controlled clinical trials

Background Parkinson’s disease (PD) is a common neurodegenerative disorder that affects many people every year. Deep brain stimulation (DBS) is an effective nonpharmacological method to treat PD motor symptoms. This meta-analysis was conducted to evaluate the efficacy of subthalamic nucleus (STN)-DBS versus globus pallidus internus (GPi)-DBS in treating advanced PD. Methods Controlled clinical trials that compared STN-DBS to GPi-DBS for short-term treatment of PD in adults were researched up to November 2015. The primary outcomes were the Unified Parkinson’s Disease Rating Scale Section (UPDRS) III score and the levodopa-equivalent dosage (LED) after DBS. The secondary outcomes were the UPDRS II score and the Beck Depression Inventory (BDI) score. Results Totally, 13 studies containing 1,148 PD patients were included in this meta-analysis to compare STN-DBS versus GPi-DBS. During the off-medication state, the pooled weighted mean difference (WMD) of UPDRS III and II scores were −2.18 (95% CI =−5.11 to 0.74) and −1.96 (95% CI =−3.84 to −0.08), respectively. During the on-medication state, the pooled WMD of UPDRS III and II scores were 0.15 (95% CI =−1.14 to 1.44) and 1.01 (95% CI =0.12 to 1.89), respectively. After DBS, the pooled WMD of LED and BDI were −254.48 (95% CI =−341.66) and 2.29 (95% CI =0.83 to 3.75), respectively. Conclusion These results indicate that during the off-medication state, the STN-DBS might be superior to GPi-DBS in improving the motor function and activities of daily living for PD patients; but during the on-medication state, the opposite result is observed. Meanwhile, the STN-DBS is superior at reducing the LED, whereas the GPi-DBS shows a significantly greater reduction in BDI score after DBS.
Practical Tools for Policy Analysis in DSGE Models with Missing Channels

In this paper we analyze the propagation of shocks originating in sectors that are not present in a baseline dynamic stochastic general equilibrium (DSGE) model. Specifically, we proxy the missing sector through a small set of factors, that feed into the structural shocks of the DSGE model to create correlated disturbances. We estimate the factor structure by matching impulse responses of the augmented DSGE model to those generated by an auxiliary model. We apply this methodology to track the effects of oil shocks and housing demand shocks in models without energy and housing sectors.
Un Modello Di Crescita Discontinua Dell’Impresa: Teoria Ed Evidenza Empirica

Typically, firms change their size through a row of discrete leaps over time. A very basic model allowing for discontinuous growth can be based on a couple of assumptions: (a) in the short run, the firm’s equipment and organization provide the maximum profit only for a given production level, and diverging form it is costly; and (b) in the long run, the firm adjusts its size as if the current equipment had to be exploited until overall profits exceed a given threshold and those expected from the new desired plant for the current production level. Combining the latter two hypotheses entails a number of testable consequences, usually regarded as nuisance facts according to the traditional theories. First of all, the profitability should not be a continuous function of the firms’ size, but exhibits a number of peaks, each corresponding to a different locally optimal size. Secondly, when demand is growing, investment are expected to increase just in those firms where profit falls shorter some given threshold. The model has been tested by using a panel of data on the size and performances of Italian manufacturing firms from 1998 to 2007. Indeed, both the non-parametric analysis and a panel estimation confirm the presence of several “peaks” in the distribution of profitability by size. Furthermore, a negative statistical relationship is apparent between investment and profitability, controlling for the size of firms.
Intra-National Protectionism in China: Evidence from the Public Disclosure of ‘Illegal’ Drug Advertising

This paper provides micro-level evidence that drug advertising regulations and inspections in China are used by local governments to discriminate against firms from outside the province. Furthermore, the degree of discrimination varies across firms in that drug manufacturers which have closer ties with rival provinces are more likely to be targeted. These findings demonstrate that giving provincial governments strong incentives to compete with each other may exacerbate the market distortions inherent in a partially reformed economy such as China.
Capacity and resource reservation in Broadband Internet

The aim of this paper is to introduce a model to providing Internet service charging according to a capacity and usage based charging. A charging model is introduced according to the user interests and preferences. The model provides resource optimization to the mobile network through a capacity and resource parameters where it generates a cost-effective method to optimize the resources. Experiments are conducted using a network simulator to evaluate the model and analyze the results. The results will be useful for Telecommunication Networks and accounting policy planning for the Broadband Internet Charging Quality of Service.
Automating the wrapping of software building blocks with test cases

Adaptation has become an important issue in software development as often interface mismatches, defined by different mismatch classes, in the context of object- and component-reuse and -integration occur. For overcoming these mismatches various techniques and approaches have emerged over the last years with the ultimative goal of fully automated tool support. The class of signature mismatches for instance, which is an important challenge for developers, is still tackled by manual code modification or manual adapter generation. It is an error prone and time consuming task, since neither appropriate tool support nor standardized evaluations exist. Thus, the idea of the author's envisaged PhD thesis is to provide theory, experiments and evaluation for an automated adapter solution based on the idea of using test cases for semantic validation during the adapter generation process, thereby satisfying the need for overcoming the class of signature mismatches for objects as well as components.