Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel-Based Least Squares Policy Iteration for Reinforcement Learning

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.
Smart metering demand management programs: challenging the comfort and cleanliness habitus of households

Drawing on Pierre Bourdieu's concepts of habitus, field and capital, this paper outlines how smart metering demand management programs could be redesigned to bring together the competing fields of resource management and domestic life. Comfort and cleanliness expectations, which are ingrained in the habitus of householders and the field of domestic life, are often overlooked in demand management programs, which focus instead on making existing and evolving expectations more efficient. This paper draws on preliminary findings from qualitative research activities with householders who received consumption feedback through an in-home display, and/or variable price signals --- both enabled by smart meters. The paper offers insights for designers of interactive demand management strategies about how to go beyond achieving efficiency benefits in the home in order to fundamentally change expectations and norms ingrained in the habitus.
An Efficient Step-Up Converter with a Low Switch Stress

A novel approach is proposed for a high step-up and high efficiency converter with a low switch stress. The integrated boost-flyback converter uses coupled-inductor techniques to achieve high step-up voltage with low duty ratio, and thus the slope compensation circuit is disregarded. The proposed circuit topology improving high-gain ratio, increasing efficiency, reducing the secondary side of copper loss, and having an active-clamp effect that can reduce the switch stress on power components and have the energy feedback mechanism. Such a method of high efficiency and simple control can reduce cost which makes production size in increasing demand at present. Therefore, designing converters becomes a challenge when stepping up voltage with high efficiency and high power output. This thesis is a design of a high-efficiency step-up converter with low switch stress. The design is simple in that the circuit requires only a pulse width modulation (PWM) signal output. In addition, at cut-off time, the MOSFET generates a spike by way of a circuit sent to the output. This way can achieve the goal of energy recovery, an active-clamp, and can reduce the switch stress of power switch to improve circuit efficiency. Experimental results have confirmed that the proposed converter possesses high step-up, high efficiency, and low switch stress.
Cooperative Institutions and Inequality in the OECD: Bringing the Firm Back In

Objective. To examine the relationship between firm-level cooperation, inequality, and redistribution in 18 advanced industrialized democracies. Methods. The relationships are investigated using multiple regression analyses of institutional, political, and economic variables. Results. Multilevel models reveal that contrary to neocorporatism, firm-level cooperative ties have significant inegalitarian effects, particularly in the distribution of pretax, pretransfer market income. The effects, however, are also felt in the distribution of posttax, posttransfer income. Conclusion. By paying attention to the effect of firm-level cooperation, the study sheds new light on inequality in the OECD as a result of both market-based and nonmarket coordination.
Consumer Complaints and Traffic Fatalities: Insights from the NHTSA Vehicle Owner’s Complaint Database

Driving simulators, crash databases, and more recently, naturalistic studies all help understand how changes to vehicle design affect driving safety. The rapid computerization of cars makes it increasingly important to capitalize on these sources and exploit others. The present study explores a rarely analyzed data source on traffic fatalities: National Highway Traffic Safety Administration’s vehicle owner’s complaint database. The textual data within the event description field of each complaint is extracted and analyzed using a text mining approach that involves the use of latent semantic analysis (LSA) for reducing the dimensionality of the problem. Hierarchical clustering is then employed to identify clusters of complaints that share content. Clusters are described in terms of the most frequent terms and the time trends of the complaints within them. The analysis highlights how text mining analysis can help unlock the wealth of information contained in consumer complaint databases.
Unsupervised Multiple Kernel Learning

Traditional multiple kernel learning (MKL) algorithms are essentially supervised learning in the sense that the kernel learning task requires the class labels of training data. However, class labels may not always be available prior to the kernel learning task in some real world scenarios, e.g., an early preprocessing step of a classification task or an unsupervised learning task such as dimension reduction. In this paper, we investigate a problem of Unsupervised Multiple Kernel Learning (UMKL), which does not require class labels of training data as needed in a conventional multiple kernel learning task. Since a kernel essentially defines pairwise similarity between any two examples, our unsupervised kernel learning method mainly follows two intuitive principles: (1) a good kernel should allow every example to be well reconstructed from its localized bases weighted by the kernel values; (2) a good kernel should induce kernel values that are coincided with the local geometry of the data. We formulate the unsupervised multiple kernel learning problem as an optimization task and propose an efficient alternating optimization algorithm to solve it. Empirical results on both classification and dimension reductions tasks validate the efficacy of the proposed UMKL algorithm.
Approaches to Theory Use In Interpretive Case Studies - a Critical Realist Perspective

Abstract It is argued that interpretive case studies can be classified as either describing or explaininga particular research phenomena and as such the researcher firstly needs to finalise whichapproach they are going to adopt. The article then discusses a number of basic approaches tocase study research and suggests an alternative context dependent approach that more fullyrecognises the “reality” of the research situation. Keywords IS Research Frameworks, Research Methodology, Case Study, Epistemology, InterpretivePerspective, Critical Realism DECIDING THE PURPOSE OF CASE STUDY RESEARCH Yin (1994, p. 13) defines a case study as: an empirical enquiry that investigates a contemporary phenomena within itsreal life context especially when the boundaries between phenomenon andcontext are not clearly evident. The intention of case study research is generally proposed as to gain an “in-depth”understanding of the concerned phenomena in a “real-life” setting.Stake (1994) describes two types of case study – the intrinsic case study and the instrumentalcase study. An intrinsic case study is:
A Comparative Study of Clustering Methods for Urban Areas Segmentation from High Resolution Remote Sensing Image

This paper focuses on evaluating and comparing a number of clustering methods used in color image segmentation of high resolution remote sensing images. Despite the enormous progress in the analysis of remote sensing imagery over the past three decades, there is a lack of guidance on how to select an image segmentation method suitable for the image type and size. Clustering has been widely used as a segmentation approach therefore, choosing an appropriate clustering method is very critical to achieve better results. In this paper we compare five clustering methods that have been suggested for segmentation of images. We focus on segmentation of urban areas in high resolution remote sensing images. Effective clustering extracts regions which correspond to land uses in urban areas. Ground truth images are used to evaluate the performance of clustering methods. The comparison shows that the average accuracy of road extraction is above 75%. The results show the potential of clustering high resolution aerial images starting from the three RGB bands only. The comparison gives some guidance and tradeoffs involved in using each.
Getting doctored: a proposed model of marketing doctoral student socialization

Addresses the socialization process among marketing doctoral students. Four modes of doctoral student socialization are provided from depth interviews conducted with 28 purposively selected individuals. These four modes are based upon two characteristics: degree of program structure, or formal socialization; and degree of student‐faculty interaction, or informal socialization. Reveals five factors that informants identified as contributors toward the professional success of a marketing doctoral student: inner desire, communitas, practicality in research, networking, and brand equity.
Diagnosis of neonatal sepsis: a clinical and laboratory challenge.

The evaluation of tests for neonatal sepsis is important because the infection may present a very serious threat to the baby. There is an urgent need to know whether the baby has sepsis to institute treatment as quickly as possible. Confirmation of the diagnosis may take time, and diagnostic tests are used to obtain a rapid indication of the infection status. These tests are not perfect. Some real cases of infection will produce negative test results, whereas some babies without infection will test positive. The potential usefulness of the test will depend, above all, on the clinical condition of the baby. If the baby is really very sick, the test will not give very much additional information. Similarly, if the baby is evidently well, a clinical examination will be sufficient and a positive test result would not dramatically increase the probability that the baby is infected. It is in situations in which the clinical picture leaves the physician in doubt about the infection status that a diagnostic test is likely to be most useful. Thus, the result of a diagnostic test must be evaluated in the light of the clinical condition of the baby. Extensive literature exists on single laboratory tests or combinations of tests, as well as tests used together with risk factors and/or clinical signs, to diagnose neonatal sepsis. In many instances, the results of the evaluations have been conflicting. There are several possible explanations for the divergent results, and the purpose of this review is to update readers on the topic and raise issues that should be addressed in the future. Throughout pregnancy and until the membranes rupture, the fetus is relatively protected from the microbial flora of the mother by the chorioamniotic membranes, the placenta, and poorly understood antibacterial factors in amniotic fluid (1). However, there …
ConfSeer: Leveraging Customer Support Knowledge Bases for Automated Misconfiguration Detection

We introduce ConfSeer, an automated system that detects potential configuration issues or deviations from identified best practices by leveraging a knowledge base (KB) of technical solutions. The intuition is that these KB articles describe the configuration problems and their fixes so if the system can accurately understand them, it can automatically pinpoint both the errors and their resolution. Unfortunately, finding an accurate match is difficult because (a) the KB articles are written in natural language text, and (b) configuration files typically contain a large number of parameters with a high value range. Thus, expert-driven manual troubleshooting is not scalable. While there are several state-of-the-art techniques proposed for individual tasks such as keyword matching, concept determination and entity resolution, none offer a practical end-to-end solution to detect problems in machine configurations. In this paper, we describe our experiences building ConfSeer using a novel combinations of ideas from natural language processing, information retrieval and interactive learning. ConfSeer powers the recommendation engine behind Microsoft Operations Management Suite that proposes fixes for software configuration errors. The system has been running in production for about a year to proactively find misconfigurations on tens of thousands of servers. Our evaluation of ConfSeer against an expert-defined rule-based commercial system, an expert survey and web search engines shows that it achieves 80%-97.5% accuracy and incurs low runtime overheads.
Assessing the Role of GLUK5 and GLUK6 at Hippocampal Mossy Fiber Synapses

It has been suggested recently that presynaptic kainate receptors (KARs) are involved in short-term and long-term synaptic plasticity at hippocampal mossy fiber synapses. Using genetic deletion and pharmacology, we here assess the role of GLUK5 and GLUK6 in synaptic plasticity at hippocampal mossy fiber synapses. We found that the kainate-induced facilitation was completely abolished in the GLUK6-/- mice, whereas it was unaffected in the GLUK5-/-. Consistent with this finding, synaptic facilitation was reduced in the GLUK6-/- and was normal in the GLUK5-/-. In agreement with these results and ruling out any compensatory effects in the genetic deletion models, application of the GLUK5-specific antagonist LY382884 [(3S,4aR,6S,8aR)-6-(4-carboxyphenyl)methyl-1,2,3,4,4a,5,6,7,8,8a-decahydroisoquinoline-3-carboxylic acid] did not affect short-term and long-term synaptic plasticity at the hippocampal mossy fiber synapses. We therefore conclude that the facilitatory effects of kainate on mossy fiber synaptic transmission are mediated by GLUK6-containing KARs.
Methods and Techniques to Protect the Privacy Information in Privacy Preservation Data Mining

- Due to the explosive development of Internet browser’s can extract desired data from large amount of database. Data mining is a collection of technique helps the miner can retrieve exact knowledge from it. Privacy Preservation has been one of the greater concerns in data mining; there are many methods and techniques for privacy Preservation data mining. This paper discussed very keen points of various Privacy Preservation data mining algorithms and analyzing techniques and concludes the advantages and disadvantages, and a way to direct the problems.
20 MS/s, 1.6 mW, 50dB SFDR switched-capacitor DAC

This paper presents a 1.6mW, 20 MS/s DAC with 0.33 pJ/conversion-step figure-of-merit, effective number of bits exceeding 7.9 and 0.40mm2 total chip area. A new variety of the non-binary series allowed design of the switched-capacitor charge pump array, with switched capacitance 7.3 pF, maximum to minimum capacitance ratio 7.2 and 7 matched capacitors, resulting in compact DAC layout. The proposed device was implemented in the 0.18um TSMC CMOS technology.
A role for the Speedy gene family in the early stages of mammalian meiosis

Meiosis is the process by which a diploid cell undergoes two sequential rounds of division without an intervening round of DNA replication. The result is the formation of haploid gametes. The genes and signals that regulate the decision to enter meiosis are not entirely elucidated in mammals. I hypothesize that the Speedy/RINGO gene family functions endogenously in the early stages of meiosis including: meiotic initiation, premeiotic DNA replication, or meiotic prophase. In order to validate this function for the Speedy/RINGO genes in vivo, I categorized Speedy/RINGO genes, chromosomal locations, sequences, expression patterns, and identified regulators. In mouse, I identified four Speedy/RINGO genes denoted: SpeedyA, SpeedyBi, SpeedyB2, and SpeedyB3. I detected mouse SpeedyA, SpeedyB2, and SpeedyB3 mRNA in spermatocytes, the meiotic cells of the testis. Additionally, I found SpeedyA to be expressed in the embryonic ovary and its expression to be dependent on Stra8. Thesis Supervisor: David C. Page Title: Professor of Biology
Direct Anonymous Attestation for Next Generation TPM

Trusted computing platforms have been proposed as a promising approach to enhance the security of general-purpose computing systems. Direct Anonymous Attestation(DAA) is a scheme that allows a Trusted Platform Module (TPM) which is the core component of the trusted computing platform to remotely convince a communication partner that it is indeed a Trusted Platform Module while preserving the user’s privacy. The first DAA scheme developed by Brickell which is relatively complex and time-consuming was adopted by the current TPM specification.As the ECC cryptosystem is more efficient compared to the RSA cryptosystem, more and more cryptographic device is based on the ECC cryptosystem, so it is anticipated that the TPM will be based on the ECC in near future. In this paper, we propose a new direct anonymous attestation which is suitable for the ECC-based TPM. This paper presents an efficient construction that implements all anonymous authentication features specified in DAA. The proposed scheme has the best computational performance of all the DAA schemes up to now. The new DAA scheme is provably secure in the random oracle model under the q- SDH and the decisional Diffie-Hellman assumption.
An Approach to Choosing Gaussian Kernel Parameter for One-Class SVMs via Tightness Detecting

In recent years, one-class support vector machines (OCSVMs) have received increasing attention, which are one of the methods to solve one-class classification problems. Among all the kernels available to OCSVMs, Gaussian kernel is the most commonly used one with a single parameter S to tune, which influences classifier performance significantly. This paper proposes a novel heuristic approach to choosing this parameter via tightness detecting, that is designed to detect whether the decision boundaries are satisfactory. The approach tunes the parameter to ensure that the decision boundaries have an appropriate tightness, only according to the geometric distribution of positive samples. Experimental results on different datasets show that the proposed approach has a better performance than previous methods.
Robust hand gesture recognition based on enhanced depth projection maps (eDPM)

This paper presents an enhanced version of descriptor DPM-BCF (Depth Projection Maps-based Bag of Contour Fragments). Named as eDPM, it modified the projection method by converting the depth cloud into three grayscale projected maps in three orthogonal planes. Then we extract Bag of Contour Fragments (BCF) descriptor and Histogram of Oriented Gradient (HOG) descriptor from the three grayscale projected maps, both of which are concatenated as the final descriptor of the original depth data. By combining the merits of contour descriptor, i.e. BCF, and texture descriptor, i.e. HOG, the structure information of hand gesture can be represented more accurately. In this method, linear classifier Support Vector Machine is used as the gesture classifier. Experimental results on two public datasets show that, due to the proposed methods' robustness to the rotation and self-occlusion, a greater performance can be achieved compared with other state-of-the art methods.
Numerical Analysis of Filter-Based Stabilization for Evolution Equations

We consider filter-based stabilization for evolution equations (in general) and for the Navier--Stokes equations (in particular). The first method we consider is to advance in time one time step by a given method and then to apply an (uncoupled and modular) filter to get the approximation at the new time level. This filter-based stabilization, although algorithmically appealing, is viewed in the literature as introducing far too much numerical dissipation to achieve a quality approximate solution. We show that this is indeed the case. We then consider a modification: Evolve one time step, filter, deconvolve, then relax to get the approximation at the new time step. We give a precise analysis of the numerical diffusion and error in this process and show it has great promise, confirmed in several numerical experiments.
High Resolution Intravital Imaging of Subcellular Structures of Mouse Abdominal Organs Using a Microstage Device

Intravital imaging of brain and bone marrow cells in the skull with subcellular resolution has revolutionized neurobiology, immunology and hematology. However, the application of this powerful technology in studies of abdominal organs has long been impeded by organ motion caused by breathing and heartbeat. Here we describe for the first time a simple device designated ‘microstage’ that effectively reduces organ motions without causing tissue lesions. Combining this microstage device with an upright intravital laser scanning microscope equipped with a unique stick-type objective lens, the system enables subcellular-level imaging of abdominal organs in live mice. We demonstrate that this technique allows for the quantitative analysis of subcellular structures and gene expressions in cells, the tracking of intracellular processes in real-time as well as three-dimensional image construction in the pancreas and liver of the live mouse. As the aforementioned analyses based on subcellular imaging could be extended to other intraperitoneal organs, the technique should offer great potential for investigation of physiological and disease-specific events of abdominal organs. The microstage approach adds an exciting new technique to the in vivo imaging toolbox.
Antigen-Specific Blockade of Lethal CD8 T-Cell Mediated Autoimmunity in a Mouse Model of Multiple Sclerosis1

Increasing evidence implies CD8 T cells in tissue-specific autoimmune diseases including multiple sclerosis. mAbs specific for MHC class I molecules presenting a dominant autoantigenic peptide may allow selective immunotherapy in such settings. We demonstrate the prophylactic and therapeutic efficacy of such a mAb in a transgenic mouse model of lethal demyelinating disease in which a neo-self Ag expressed by oligodendrocytes is targeted by CD8 T cells with transgenic Ag receptors. Mechanistic studies performed in vitro and in vivo indicate that it is the low expression of MHC class I on oligodendrocytes, which makes this form of Ag-specific intervention possible.