Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable, secure, and highly available distributed file access

A summary of and historical perspective on work done to implement easy-to-share distributed file systems based on the Unix model are presented. Andrew and Coda are distributed Unix file systems that embody many of the recent advances in solving the problem of data sharing in large, physically dispersed workstation environments. The Andrew architecture is presented, the scalability and security of the system are discussed. The Coda system is examined, with emphasis on its high availability. <<ETX>>
Full-duplex MIMO relaying powered by wireless energy transfer

We consider a full-duplex decode-and-forward system, where the wirelessly powered relay employs the time-switching protocol to receive power from the source and then transmit information to the destination. It is assumed that the relay node is equipped with two sets of antennas to enable full-duplex communications. Three different interference mitigation schemes are studied, namely, 1) optimal 2) zero-forcing and 3) maximum ratio combining/maximum ratio transmission. We develop new outage probability expressions to investigate delay-constrained transmission throughput of these schemes. Our analysis show interesting performance comparisons of the considered precoding schemes for different system and link parameters.
Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning

Fault detection and isolation (FDI) is very difficult for electronics-rich analog systems due to its sophisticated mechanism and variable operational conditions. Traditionally, FDI in such systems is done through the monitoring of deviation of output signals in voltage or current at system level, which commonly arises from the degradation of one or more critical components. Therefore, FDI can be transformed to a multiclass classification task given the extracted features of the output signals in voltage or current of the circuit. Traditional feature extraction on the circuit output is mostly based on time-domain, frequency-domain, or time-frequency signal processing, which collapse high-dimensional raw signals into a lower dimensional feature set. Such low-dimensional feature set usually suffers from information loss so as to affect the accuracy of the later fault diagnosis. In order to retain as much information as possible, deep learning is proposed which employs a hierarchical structure to capture the different levels of semantic representations of the signals. In this paper, a novel fault diagnostic application of Gaussian–Bernoulli deep belief network (GB-DBN) for electronics-rich analog systems is developed which can more effectively capture the high-order semantic features within the raw output signals. The novel fault diagnosis is validated experimentally on two typical analog filter circuits. Experimental results show the fault diagnosis based on GB-DBN is with superior diagnostic performance than the traditional feature extraction methods.
A Visual Approach for External Cluster Validation

Visualization can be very powerful in revealing cluster structures. However, directly using visualization techniques to verify the validity of clustering results is still a challenge. This is due to the fact that visual representation lacks precision in contrasting clustering results. To remedy this problem, in this paper we propose a novel approach, which employs a visualization technique called HOV (hypothesis oriented verification and validation by visualization) which offers a tunable measure mechanism to project clustered subsets and non-clustered subsets from a multidimensional space to a 2D plane. By comparing the data distributions of the subsets, users not only have an intuitive visual evaluation but also have a precise evaluation on the consistency of cluster structure by calculating geometrical information of their data distributions
In-homogeneous Virus Spread in Networks

Our $N$-intertwined model (now called NIMFA) for virus spread in any network with $N$ nodes is extended to a full heterogeneous setting. The metastable steady-state nodal infection probabilities are specified in terms of a generalized Laplacian, that possesses analogous properties as the classical Laplacian in graph theory. The critical threshold that separates global network infection from global network health is characterized via an $N$ dimensional vector that makes the largest eigenvalue of a modified adjacency matrix equal to unity. Finally, the steady-state infection probability of node $i$ is convex in the own curing rate $\delta_{i}$, but concave in the curing rates $\delta_{j}$ of the other nodes $1\leq j\neq i\leq N$ in the network.
Efficient method of establishing words tone dictionary for Korean TTS system

In this paper, we propose an efficient method to establish Word Tone Dictionary(WTD). Vector qantization(VQ) is applied for compressing word tones for compressing word tones, and a phonetic-syntactic distance is adopted for searching the word tone dictionary. Because word tone is a sequence of syllable tones, VQ is used in encoding the syllable tones. As word tones in utterances are specified by their syntactic roles and phonetic features, we propose an adequate distance function to search the appropriate word tone in WTD. It is a combined distance function of syntactic distance and phonetic distance. We tested on a 100-utterance corpus. Preliminary experiments showed that the proposed method could lead to the natural pitch-controlled speech.
Proceedings of Ihe 1986 Winter Simulation

In this paper, we introduce two convergent Monte Carlo algorithms for optimizing complex stochastic systems. The first algorithm, which is applicable to regenerative processes, operates by estimating finite differences. The second method is of Robbins-Monro type and is applicable to Markov chains. The algorithm is driven by derivative estimates obtained via a likelihood ratio argument.
Spectral and spatial classification of hyperspectral data using SVMs and Gabor textures

High spectral and spatial resolution images are widely used in urban mapping. In order to increase conventional spectral classification accuracy, morphological profiles based spatial information were widely studied. Some improvements were obtained in terms of classification accuracy. However, texture is is a very useful structure information in high spatial resolution images. In this paper, Gabor filters based textures are investigated to increase conventional spectral classification. Gabor textures are extracted from the first three PCs of the hyperspectral bands. Then Gabor textures and hyperspectral bands are concatenated, and classified in one support vector machines. The results show that Gabor textures are useful in extracting spatial information and increasing the classification accuracy compared to conventional spectral classification.
An investigation into the functional and psychosocial impact of living organ donation

First and foremost I would like to thank my thesis supervisors, Prof Ronan O’Carroll and Dr Vivien Swanson for the valuable advice and guidance they have offered throughout my thesis. I consider myself very fortunate to have had Ronan as my principal supervisor, and I take this opportunity to not only apologise to him for frequently trying his patience but to offer him my sincerest gratitude for the support, compassion and understanding he has shown me over the years. Further thanks are offered to the staff at the Royal Infirmary of Edinburgh, particularly within the Scottish Liver Transplant Unit, who have contributed to the completion of this thesis in many ways. I would particularly like to thank Professor Peter Hayes for his continued support, and Jen, Sarah and Maureen for their assistance in participant recruitment. Of course, I am eternally grateful to all patients and their families who accepted the invitation to participate in this thesis, especially Scotland’s first LDLT donor and recipient: a truly inspiring couple. In addition, I would like to thank Professor Eamonn Ferguson and Dr Adele Dickson for their expert advice and support. To my family and friends whose love, support and encouragement has never faltered, I am forever grateful. I could not have completed this thesis without you, particularly Lindsey and Malcolm who got me to the finishing line!
Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging

Photoacoustic imaging (PAI) is an emerging medical imaging modality capable of providing high spatial resolution of Ultrasound (US) imaging and high contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in PAI. However, using DAS beamformer leads to low resolution images and considerable contribution of off-axis signals. A new paradigm namely delay-multiply-and-sum (DMAS), which was originally used as a reconstruction algorithm in confocal microwave imaging, was introduced to overcome the challenges in DAS. DMAS was used in PAI systems and it was shown that this algorithm results in resolution improvement and sidelobe degrading. However, DMAS is still sensitive to high levels of noise, and resolution improvement is not satisfying. Here, we propose a novel algorithm based on DAS algebra inside DMAS formula expansion, double stage DMAS (DS-DMAS), which improves the image resolution and levels of sidelobe, and is much less sensitive to high level of noise compared to DMAS. The performance of DS-DMAS algorithm is evaluated numerically and experimentally. The resulted images are evaluated qualitatively and quantitatively using established quality metrics including signal-to-noise ratio (SNR), full-width-half-maximum (FWHM) and contrast ratio (CR). It is shown that DS-DMAS outperforms DAS and DMAS at the expense of higher computational load. DS-DMAS reduces the lateral valley for about 15 dB and improves the SNR and FWHM better than 13% and 30%, respectively. Moreover, the levels of sidelobe are reduced for about 10 dB in comparison with those in DMAS.
Aggressive actions and anger detection from multiple modalities using Kinect

Prison facilities, mental correctional institutions, sports bars and places of public protest are prone to sudden violence and conflicts. Surveillance systems play an important role in mitigation of hostile behavior and improvement of security by detecting such provocative and aggressive activities. This research proposed using automatic aggressive behavior and anger detection to improve the effectiveness of the surveillance systems. An emotion and aggression aware component will make the surveillance system highly responsive and capable of alerting the security guards in real time. This research proposed facial expression, head, hand and body movement and speech tracking for detecting anger and aggressive actions. Recognition was achieved using support vector machines and rule based features. The multimodal affect recognition precision rate for anger improved by 15.2% and recall rate improved by 11.7% when behavioral rule based features were used in aggressive action detection.
A series of yeast/Escherichia coli λ expression vectors designed for directional cloning of cDNAs and cre/lox‐mediated plasmid excision

A series of Saccharomyces cerevisiae/Escherichia coli λ/plasmid expression vectors have been constructed which allow easy excision of the plasmid sequences from λ. Features of six are described, and two designated λPG15 and λAD5, are characterized in detail. Transcription of cloned sequences is controlled by the alternative promoters, ADH2, PGK, GAL10 and SV40 early, and by the CYC1 transcriptional terminator. Unique EcoRI and XhoI restriction sites in the intervening polylinker make these λ vectors compatible for directional cloning of ‘ZAP’‐synthesized cDNAs. Inserted DNAs have been previously shown to have high levels of the genetic activity in both S. cerevisiae and E. coli, allowing these vectors to be used for genetic complementation in both species. Plasmid recovery from the λ vector is mediated by the activity of the cre‐encoded enzyme upon lox sequences flanking the plasmid and adjoining the λ arms. The plasmids contain the yeast 2 μm origin and E. coli pBR322 origin, the URA3 or TRP1 yeast selectable markers, and ampicillin‐resistance marker in E. coli. The usefulness of the λPG15 and the λAD5 cloning vectors was demonstrated by constructing large Neurospora crassa cDNA libraries. The λPG15–N. crassa library was used to infect purE, purC and trpC mutants of E. coli, and complemented and/or suppressed prototrophic colonies were selected. The flexibility and power of this system for cloning of cDNAs is discussed.
Interventions for Healthy Eating and Physical Activity among Obese Elementary Schoolchildren: Observing Changes of the Combined Effects of Behavioral Models

The aim of this experimental research was to examine the effectiveness of the SSII-Healthy Eating Intervention and Physical Activity intervention programs at the end of intervention implementation in term of combined effects. The sample of this study was 21 students in Sawadeewittaya School, aged 9-11 years, who met the inclusion criteria and consented to participate in the study. The dependent variables included knowledge about obesity-related Type 2 diabetes, healthy eating behavior, healthy eating self-efficacy, healthy eating self-control, and BMI. The study interventions were the SSII-Healthy Eating Intervention Program, and the SSII-Physical Activity Intervention Program. Each of the two interventions was created using the self-efficacy, self-control, and implementation intention principles. The sample was first implemented with the SSII-Healthy Eating Intervention Program, followed by the SSII-Physical Activity Intervention. Data analysis was performed using SPSS for Windows. The statistical tests were descriptive statistics and One-way repeated measures ANOVA. Results showed that: 1) after the individual SSII-Healthy Eating Intervention Program; mean scores of knowledge about obesity-related Type 2 diabetes, healthy eating self-efficacy, healthy eating self-control, and healthy eating behavior significantly increased from the baseline and BMI significantly decreased. 2) The combined effect of the SSII-Healthy Eating and Physical Activity Intervention Programs on healthy eating behavior was greater than that of the individual SSII-Healthy Eating Intervention, but not for BMI.
Distributed simulation and profiling of multiprocessor systems on a chip

Embedded systems - multiprocessor systems on a chip with application specific instruction-set processors (ASIPs) - become indivisible part of our everyday lives. They are everywhere. Therefore, powerful and flexible way of design and simulation of these systems is needed. The simulators of ASIPs are created using an architecture description language called ISAC. In this paper, the basic concept of simulation, the communication protocol among ASIPs and the three-state synchronization protocol that is used in the distributed multiprocessor simulation are described. Further, the basic concept of profiling and consequential evaluation of profiling information is described. One needs the results from the simulation itself and the results from the profiling to find bugs in the system and to optimize the system as a whole.
Prognostic Significance of Volume-Based Metabolic Parameters in Uterine Cervical Cancer Determined Using 18F-Fluorodeoxyglucose Positron Emission Tomography

ObjectiveWe compared the prognostic value of volume-based metabolic parameters determined using fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) (18F-FDG PET) (with other prognostic parameters in uterine cervical cancer. MethodsThe subjects were 73 female patients who had an initial diagnosis of uterine cervical cancer and who underwent 18F-FDG PET. Various metabolic or volume-based PET parameters including maximum and average standardized uptake values, metabolic tumor volume, and total lesion glycolysis (TLG) were measured in primary cervical tumors. Survival analysis for disease-free survival or progression-free survival was performed with a Kaplan-Meier method using PET parameters and other clinical variables. For determining independent prognostic factors, Cox regression analysis was performed. ResultsRecurrence or disease progression occurred in 23 patients (31.5%). In univariate analysis, patient age (cutoff, 57 years, P < 0.05), International Federation of Gynecology and Obstetrics stage (P = 0.07), primary tumor size (cutoff, 6.7 cm; P < 0.05), lymph node status on PET (P < 0.005), treatment method (P < 0.01), metabolic tumor volume (cutoff, 82 cm3; P = 0.001), and TLG (cutoff, 7600; P = 0.005) were significant predictors of recurrence or progression. In multivariate analysis, both lymph node status on PET (hazard ratio, 1.042 [negative vs intrapelvic metastasis only], 7.008 [negative vs extrapelvic metastasis]; P < 0.001) and TLG (cutoff, 7600; hazard ratio, 2.981; P < 0.05) were independent prognostic factors for predicting recurrence. ConclusionsIn uterine cervical cancer, TLG, a volume-based metabolic parameter, and lymph node status on PET may be significant independent prognostic factors for event-free survival.
Private Link Prediction in Social Networks

In this work, we investigate the link prediction problem on social networks presented by Liben-Nowell and Kleinberg [8] in the context of diﬀerential privacy. In particular, after running three link prediction algorithms–common neighbors, Jaccard’s coeﬃcient, and preferential attachment–we investigate whether it is possible to answer graph cut queries in a diﬀerentially private manner. The graph cuts that we consider are arbitrary but ﬁxed. We give positive results for privacy guarantee using the notion of restricted sensitivity under the hypothesis that our networks have bounded degree. We also present some experimental results to show the performance and utility of our methods.
Determination of the target nucleosides for members of two families of 16S rRNA methyltransferases that confer resistance to partially overlapping groups of aminoglycoside antibiotics

The 16S ribosomal RNA methyltransferase enzymes that modify nucleosides in the drug binding site to provide self-resistance in aminoglycoside-producing micro-organisms have been proposed to comprise two distinct groups of S-adenosyl-l-methionine (SAM)-dependent RNA enzymes, namely the Kgm and Kam families. Here, the nucleoside methylation sites for three Kgm family methyltransferases, Sgm from Micromonospora zionensis, GrmA from Micromonospora echinospora and Krm from Frankia sp. Ccl3, were experimentally determined as G1405 by MALDI-ToF mass spectrometry. These results significantly extend the list of securely characterized G1405 modifying enzymes and experimentally validate their grouping into a single enzyme family. Heterologous expression of the KamB methyltransferase from Streptoalloteichus tenebrarius experimentally confirmed the requirement for an additional 60 amino acids on the deduced KamB N-terminus to produce an active methyltransferase acting at A1408, as previously suggested by an in silico analysis. Finally, the modifications at G1405 and A1408, were shown to confer partially overlapping but distinct resistance profiles in Escherichia coli. Collectively, these data provide a more secure and systematic basis for classification of new aminoglycoside resistance methyltransferases from producers and pathogenic bacteria on the basis of their sequences and resistance profiles.
Was Frank Knight an Institutionalist?

Abstract This paper critically examines Geoffrey Hodgson's provocative claim that Frank Knight was a member of the American institutionalist school in the interwar years. In the first section of the paper we provide a definition of institutionalism and emphasize its meaning from a historiographic point of view. The second and third sections analyse the two main methodological struggles between Knight and the institutionalists, namely, the debate during the early 1920s over the use of instinct theory as an explanation of economic behaviour, and the subsequent campaign led by Knight in the late 1920s and early 1930s against the behaviourist wing of American institutionalism à la Copeland and Ayres. The fourth section deals with Knight's own brand of institutionalism. Our main conclusions are that, even if Knight's approach to the study of economic behaviour shows significant affinities with American institutionalism, he was not—both sociologically and in terms of his philosophical premises—an institutionalist.
Next Generation Sequencing of Serum Circulating Nucleic Acids from Patients with Invasive Ductal Breast Cancer Reveals Differences to Healthy and Nonmalignant Controls

Circulating nucleic acids (CNA) isolated from serum or plasma are increasingly recognized as biomarkers for cancers. Recently developed next generation sequencing provides high numbers of DNA sequences to detect the trace amounts of unique serum biomarkers associated with breast carcinoma. Serum CNA of 38 women with ductal carcinoma was extracted and sequenced on a 454/Roche high-throughput GS-FLX platform and compared with healthy controls and patients with other medical conditions. Repetitive elements present in CNA were detected and classified, and each repetitive element was normalized based on total sequence count or repeat count. Multivariate regression models were calculated using an information-theoretical approach and multimodel inference. A total of 423,150 and 953,545 sequences for the cancer patients and controls, respectively, were obtained. Data from 26 patients with stages II to IV tumors and from 67 apparently healthy female controls were used as the training data set. Using a bootstrap method to avoid sampling bias, a five-parameter model was developed. When this model was applied to a validation data set consisting of patients with tumor stage I (n = 10) compared with healthy and nonmalignant disease controls (n = 87; 1,261,561 sequences) a sensitivity of 70% at a specificity of 100% was obtained. At a diagnostic specificity level of 95%, a sensitivity of 90% was calculated. Identification of specific breast cancer–related CNA sequences provides the basis for the development of a serum-based routine laboratory test for breast cancer screening and monitoring. Mol Cancer Res; 8(3); 335–42
A tri-band Fabry-Perot cavity for antenna gain enhancement

This paper presents a tri-band Fabry-Perot cavity, which can be applied to design tri-band Fabry-Perot resonator antennas. The cavity is composed of a PEC ground plane and a thin partially reflective surface (PRS). On the proposed PRS, two distinct resonant frequencies are appropriately designed so that the Fabry-Perot cavity is able to resonate at three frequency bands. This can be utilized to enhance the directivity and gain of small antennas at three distinct bands. Simulations confirm the design principle.