Miracle-dz/UniFAR-FTUs huggingface dataset:
The N-myristoylated Rab-GTPase m-Rabmc is involved in post-Golgi trafficking events to the lytic vacuole in plant cells

We report on the sub-cellular localisation and function of m-Rabmc, a N-myristoylated plant-specific Rab-GTPase previously characterised at the molecular level and also by structural analysis in Mesembryanthemum crystallinum. By confocal laser scanning microscopy, we identified m-Rabmc predominantly on the prevacuolar compartment of the lytic vacuole but also on the Golgi apparatus in various plant cell types. Two complementary approaches were used immunocytochemistry and cyan fluorescent protein (CFP)/yellow fluorescent protein (YFP)-fusion proteins. Co-localisation studies of m-Rabmc with a salinity stress modulated integral calcium-ATPase suggest involvement of m-Rabmc in a plant-specific transport pathway to the prevacuolar compartment of the lytic vacuole. This hypothesis was strengthened by the inhibition of the transport of aleurain fused to green fluorescent protein (GFP), a marker of the lytic vacuole, in the presence of the dominant negative mutant m-Rabmc(N147I) in Arabidopsis thaliana protoplasts. The inhibitory effect of m-Rabmc(N147I) was specific for the transport pathway to the lytic vacuole, since the transport of chitinase-YFP, a marker for the neutral vacuole, was not hindered by the mutant.
A Case Based Clustering-Based TSK Fuzzy Rule Systems for Stock Price Forecasting

Stock price predictions suffer from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the huge historic price data. This paper presents a new financial time series-forecasting model by a case based clustering TSK fuzzy rule system for stock price predictions in Taiwan Stock Exchange Corporation (TSEC) . This forecasting model integrates a case based reasoning technique, a TSK Fuzzy Rule based system (TSK), and Simulated Annealing (SA) to construct a perdition-system based on historical data and technical indexes. The model is major based on the idea that the historic price data base can be transformed into a smaller case-base together with a group of TSK fuzzy decision rules. As a result, the model can be more accurately react to the current price of the stock from these smaller case based TSK fuzzy rule system . MAPE is applied as a performance measure and the effectiveness of our proposed CBTSK model is demonstrated by experimentally compared with other approaches on various stocks in TSEC. The average MAPE of CBTSK model is 85% the highest among others.
DETAILED STUDY OF THE RF PROPERTIES OF THE FETS RFQ COLD MODEL

A 324MHz four vane RFQ cold model has been built, as part of the development of a proton driver Front End Test Stand (FETS) at the Rutherford Appleton Laboratory (RAL) in the UK. Previous measurements to determine the electric field profile were made using the bead-pull perturbation method: these measurements have been refined and expanded. New measurements of the electric field profile, Q-value and resonant modes are presented. Measurements of the fundamental frequency and Q-value of the RFQ as a result of modifications to the profile of the end flange inserts are also given. Finally, an experiment is outlined to determine the beam transmission properties of the cold model based on beam transport simulations with the General Particle Tracer package (GPT).
An investigation into noise-bound shadow detection and removal

Noise is an unavoidable contaminant in any non-trivial image. It is usually identified as a limiting factor in the performance of shadow-removal algorithms, but little is done to reduce its negative impact. The typical method to counter noise effects is to employ arbitrary or empirical thresholds somewhere inside the algorithm, with values chosen to maximize the shadow-removal performance. However these thresholds can be objectively calculated from the noise statistics for a particular pixel value. We present a method of shadow-removal whose internal parameters are adaptively set by noise statistics such that the algorithm is free of any empirically set threshold. Experiments indicate that the performance of the new method is approximately equivalent to that with an empirically-fixed threshold, though an area of improvement has been identified that could significantly boost the accuracy of the new method.
Evaluation of Source Code with Item Response Theory

The analysis of source code produced by novice programmers could provide interesting insights into their learning progress, particularly in introductory programming courses. Yet, as the programming ability of a person is assumed to be quite complex, it is not likely that it would be observable directly in its total. Instead, we regard those abilities as latent psychometric constructs and apply the methodology of item response theory (IRT) to assess their manifestations. In preparatory work, we had identified a list of items that represent the central concepts of object-oriented programming. In this paper we propose a methodology that allows the evaluation of coding abilities by analyzing the application of those concepts. We demonstrate this methodology by exemplarily analyzing source code that was produced during programming projects. The results provide interesting information about the difficulty of the concepts' application and the distribution of the respective coding abilities among the students.
Implications for Parkinson ' s Disease

Parkinson's disease occurs in approximately one percent of individuals over the age of 55. It is characterized by the presence oftremor, rigidity, and bradykinesia. Pathologically, the hallmark of Parkinson's disease is the progressive degeneration of the dopamine (DA)-containing neurons of the nigrostriatal projection. Neurological deficits associated with the loss of DA neurons do not appear until the degeneration is extensive, presumably due to compensatory properties of the remaining DA neurons and their targets (Bernheimer et ai., 1973; Zigmond et aI., 1984). The mechanism responsible for the degenerative process is not known, although factors such as genetic predisposition and environmental toxins have been proposed to playa role (Agid et aI., 1993). There is also growing evidence that endogenous factors such as oxidative stress and DA itself may contribute to the neurodegenerative process (Cohen, 1983; Agid et aI., 1993; Zigmond et ai., 1992). A role for DA-induced toxicity to DA neurons is supported by several lines of evidence. First, like the neurotoxin 6-hydroxydopamine, DA is an unstable molecule that will oxidize to form reactive oxygen species and quinones (Graham, 1978). Second, the accumulation of the dark pigment neuromelanin in DA-containing cells of the substantia nigra is positively correlated with the susceptibility of these neurons to degradation in Parkinson's disease (Hirsch et ai., 1988; Kastner et aI., 1992). Neuromelanin is the polymerization product of oxidized DA, suggesting that oxidation is occurring within DA-containing neurons. Likewise, free cysteinyl-DA, the cysteine conjugated form of oxidized DA, has been found in DA-rich regions of the brain in a variety of species and used as an index of the rate of DA autoxidation (Fornstedt et aI., 1990a). Levels of free cysteinyl-catechols are increased under conditions known to promote oxidative stress, such as aging (Fornstedt et ai., 1990b) or ascorbate deficiency (Fornstedt and Carlsson, 1991). Third, the exposure of cells to DA, either in vivo or in vitro, has neurotoxic consequences (Graham et aI., 1978; Michel and Hefti, 1990; Filloux and Townsend, 1993). Finally, pharmacological depletion
Leveraging Knowledge From Physiological Data: On-Body Heat Stress Risk Prediction With Sensor Networks

The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1 ± 2.9% and 94.4 ± 2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.
Empirical Likelihood Methods for Sample Survey Data: An Overview

The use of empirical likelihood (EL) in sample surveys dates back to Hartley and Rao (1968). In this paper, an overview of the developments in empirical likelihood methods for sample survey data is presented. Topics covered include EL estimation using auxiliary population information and EL confidence intervals. Issues related to pseudo-EL estimation for general sampling designs are also discussed.
Physiology on a Landscape Scale: Plant-Animal Interactions1

Abstract We explore in this paper how animals can be affected by variation in climate, topography, vegetation characteristics, and body size. We utilize new spatially explicit state-of-the-art models that incorporate principles from heat and mass transfer engineering, physiology, morphology, and behavior that have been modified to provide spatially explicit hypotheses using GIS. We demonstrate how temporal and spatial changes in microclimate resulting from differences in topography and vegetation cover alter animal energetics, and behavior. We explore the impacts of these energetic predictions on elk energetics in burned and unburned stands of conifer in winter in Yellowstone National Park, chuckwalla lizard distribution limits in North America, California Beechey Ground squirrel and Dusky Footed woodrat mass and energy requirements and activity patterns on the landscape, their predator prey interactions with a rattlesnake, Crotalus viridis, and shifts in that food web structure due to topographic and vegetative variation. We illustrate how different scales of data/observation provide different pieces of information that may collectively define the real distributions of a species. We then use sensitivity analyses of energetic models to evaluate hypotheses about the effects of changes in core temperature (fever) global climate (increased air temperature under a global warming scenario) and vegetation cover (deforestation) on winter survival of elk, the geographic distribution of chuckwallas and the activity overlap of predator and prey species within a subset of commonly observed species in a terrestrial food web. Variation in slope and aspect affect the spatial variance in solar radiation incident on the ground, hence ground surface temperature, at the same elevation, same hourly 2 m air temperatures, and wind speeds. We illustrate visually how spatial effects and landscape heterogeneity make statistical descriptions of animal responses problematic, since multiple distributions of their responses to climate, topography, and vegetation on the landscape can yield the same descriptive statistics, especially at high (30 m) resolution. This preliminary analysis suggests that the model has far-reaching implications for hypothesis testing in ecology at a variety of spatial and temporal scales.
Personalized music emotion classification via active learning

We propose using active learning in a personalized music emotion classification framework to solve subjectivity, one of the most challenging issues in music emotion recognition (MER). Personalization is the most direct method to tackle subjectivity in MER. However, almost all of the state-of-the-art personalized MER systems require a huge amount user participation, which is a non-neglegible problem in real systems. Active learning seeks to reduce human annotation efforts, by automatically selecting the most informative instances for human relabeling to train the classifier. Experimental results on a Chinese music dataset demonstrate that our method can effectively reduce as much as 80% of the requirement of human annotation without decreasing F-measure. Different query selection criteria of active learning were also investigated, and we found that informativeness criterion which selects the most uncertain instances performed best in general. We finally show the condition of successful active learning in personalized MER is that label consistency from the same user.
2 Evidence-Based Complementary and Alternative Medicine review of the current evidence for the effects of Oral Chinese Herbal Medicine

Dilated cardiomyopathy (DCM) is one of the main causes of heart failure and could increase death, hospitalization, and rehospitalization rate. The effect of conventional medicine treatment (CMT) is limited; meanwhile, the combination of CMT and Oral ChineseHerbalMedicine (OCHM) represents exciting adjunctive therapies. In this study, we ascertained the therapeutic effect of OCHM in combination with CMT for dilated cardiomyopathy by using meta-analysis methods for controlled clinical trials. We searched studies from five databases and extracted data from these studies. We also assessed the methodological quality of the included studies. We evaluated the following outcome measures to estimate the prognosis in patients with DCM: left ventricular ejection fraction (LVEF), left ventricular end-diastolic dimension (LVEDD), stroke volume (SV), brain natriuretic peptide (BNP), 6-minute walk test (6MWT), and overall efficacy. The result showed that OCHM combined with CMT for the improvement of therapeutic effect in DCM patients. However, the evidence remains weak due to the small sample size, high clinical heterogeneity, and poor methodological quality of the included trials. Further, large sample size and well-designed trials are needed.
Spatial Text Visualization Using Automatic Typographic Maps

We present a method for automatically building typographic maps that merge text and spatial data into a visual representation where text alone forms the graphical features. We further show how to use this approach to visualize spatial data such as traffic density, crime rate, or demographic data. The technique accepts a vector representation of a geographic map and spatializes the textual labels in the space onto polylines and polygons based on user-defined visual attributes and constraints. Our sample implementation runs as a Web service, spatializing shape files from the OpenStreetMap project into typographic maps for any region.
A Pattern-Based Machine Translation System - Yakushite Net MT Engine

This paper introduces our pattern-based machine translation system. Our system enables us to register translation patterns including more complicated sentence structures rather than just simple words and limited types of expressions. Having registered in the Japanese to English section of IWSLT05, we aimed to input as many expressions as possible to our system in order to achieve successful translation of travel expression into English within a limited period of time.
Towards Brain-based Sensor Fusion for Navigating Robots

Current state of the art robot mapping and navigation systems produce impressive performance under a narrow range of robot platform, sensor and environmental conditions, in contrast to animals such as rats that produce “good enough” maps that enable them to function under an incredible range of situations. In this paper we present a rat-inspired featureless sensor-fusion system that assesses the usefulness of multiple sensor modalities based on their utility and coherence for place recognition during a navigation task, without knowledge as to the type of sensor. We demonstrate the system on a Pioneer robot in indoor and outdoor environments with abrupt lighting changes. Through dynamic weighting of the sensors, the system is able to perform correct place recognition and mapping where the static sensor weighting approach fails.
Memory is Deceiving: a Typical Size Induces the Judgment Bias in the Ebbinghaus IIlusion

Grounded cognition theories state that conceptual knowledge is closely linked to the current situation and embodied in sensory dimensions. Alongside the interaction with the environment, knowledge related to our environment is continually recovered from memory. Thus, the perceptual situation is closely linked to the reactivated traces in memory. Visual illusions correspond to a situation in which the perceived image differs from the objective image. In order to explore the link between conceptual processes and perceptual processes, we used the Ebbinghaus illusion and replaced the perceptual size difference of the inducers by a typical size difference simulated in memory (animals with a typically large or small size were used as inducers). Results showed a bias in judgments of the size of the test elements even when the inducers did not have different physical sizes but only reactivated different sizes in memory.
On the evolution and utility of annual citation indices

We study the statistics of citations made to the top ranked indexed journals for Science and Social Science databases in the Journal Citation Reports using different measures. Total annual citation and impact factor, as well as a third measure called the annual citation rate are used to make the detailed analysis. We observe that the distribution of the annual citation rate has an universal feature - it shows a maximum at the rate scaled by half the average, irrespective of how the journals are ranked, and even across Science and Social Science journals, and fits well to log-Gumbel distribution. Correlations between different quantities are studied and a comparative analysis of the three measures is presented. The newly introduced annual citation rate factor helps in understanding the effect of scaling the number of citation by the total number of publications. The effect of the impact factor on authors contributing to the journals as well as on editorial policies is also discussed.
MRL-0001 A Note on Chain Reactions in Traceability in CryptoNote 2 . 0 12 September 2014

Correspondence: lab@monero.cc Monero Research Lab Abstract This research bulletin describes a plausible attack on a ring-signature based anonymity system. We use as motivation the cryptocurrency protocol CryptoNote 2.0 ostensibly published by Nicolas van Saberhagen in 2012. It has been previously demonstrated that the untraceability obscuring a one-time key pair can be dependent upon the untraceability of all of the keys used in composing that ring signature. This allows for the possibility of chain reactions in traceability between ring signatures, causing a critical loss in untraceability across the whole network if parameters are poorly chosen and if an attacker owns a sufficient percentage of the network. The signatures are still one-time, however, and any such attack will still not necessarily violate the anonymity of users. However, such an attack could plausibly weaken the resistance CryptoNote demonstrates against blockchain analysis. This research bulletin has not undergone peer review, and reflects only the results of internal investigation.
Robustifying visual SLAM using multiple cameras

This master thesis is about modifying a certain monocular visual SLAM algorithm to address some of its limitations. The SLAM algorithm is not robust to quick camera motions and input images in which there are few visible features. A second camera and an inertial measurement unit was added to the hardware. Then a method for selecting the appropriate camera for tracking depending on the estimated number of features was implemented to solve the issue of few features. Experiments and results show that this method works well for slow motions. A gyrometer threshold method along with a motion model to solve the issue of quick motions was implemented and reviewed in this thesis.
Edinburgh Research Explorer Approximate Pcfg Parsing Using Tensor Decomposition Approximate Pcfg Parsing Using Tensor Decomposition

General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac. uk providing details, and we will remove access to the work immediately and investigate your claim. Abstract We provide an approximation algorithm for PCFG parsing, which asymptotically improves time complexity with respect to the input grammar size, and prove upper bounds on the approximation quality. We test our algorithm on two treebanks, and get significant improvements in parsing speed.
Voice quality transformation using an extended source-ﬁlter speech model

In this paper we present a ﬂexible framework for parametric speech analysis and synthesis with high quality. It constitutes an extended source-ﬁlter model. The novelty of the proposed speech processing system lies in its extended means to use a Deterministic plus Stochastic Model (DSM) for the estimation of the unvoiced stochastic component from a speech recording. Further contributions are the ef-ﬁcient and robust means to extract the Vocal Tract Filter (VTF) and the modelling of energy variations. The system is evaluated in the context of two voice quality transformations on natural human speech. The voice quality of a speech phrase is altered by means of re-synthesizing the deterministic component with different pulse shapes of the glottal excitation source. A Gaussian Mixture Model (GMM) is used in one test to predict energies for the re-synthesis of the deterministic and the stochastic component. The subjective listening tests suggests that the speech processing system is able to successfully synthesize and arise to a listener the perceptual sensation of different voice quality characteristics. Additionally, improvements of the speech synthesis quality compared to a baseline method are demonstrated.
Deformation Reduction of a MEMS Sensor by Stress Balancing of Multilayer

Stress balancing of a multilayer is presented to reduce out-of-plane deformation of a MEMS structure for infrared (IR) sensor application. The MEMS structure is designed to have a suspended membrane with two supporting legs, and fabricated by surface micromachining using a sacrificial polyimide. The uncontrolled stress of Si3N4/a-Si:H/Si3N4 multilayer resulted in a large deformation of the membrane, likely causing problems in IR absorption and thermal isolation of the sensor. Flat membrane was successfully achieved by minimizing the residual stress of individual films and adjusting their relative thickness and distribution. Based on simple stress analyses, the deformation of sensor structure is suggested to be dominated by gradient stress rather than mean stress of the multilayer, likewise that of a monitoring cantilever.