Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of local Type Ia supernovae with the IRAS PSCz gravity field

We compare the measured peculiar velocities of 98 local (< 150 h -1 Mpc) Type Ia supernovae with predictions derived from the IRAS PSCz survey. There is excellent agreement between the two data sets, with a best-fitting β I (= Ω 0.6 m /b I ) of 0.55 ± 0.06. Subsets of the supernovae data set are further analysed, and the above result is found to be robust with respect to culls by distance, host-galaxy extinction and choice of reference frame in which the analysis is carried out. Alternative methods of determining β I including density-density comparisons, dipole measurements and WMAP-based results are also discussed. We conclude that most recent determinations are consistent with a value of β I = 0.5.
Jointly or Separately: Which is Better for Parsing Heterogeneous Dependencies?

For languages such as English, several constituent-to-dependency conversion schemes are proposed to construct corpora for dependency parsing. It is hard to determine which scheme is better because they reflect different views of dependency analysis. We usually obtain dependency parsers of different schemes by training with the specific corpus separately. It neglects the correlations between these schemes, which can potentially benefit the parsers. In this paper, we study how these correlations influence final dependency parsing performances, by proposing a joint model which can make full use of the correlations between heterogeneous dependencies, and finally we can answer the following question: parsing heterogeneous dependencies jointly or separately, which is better? We conduct experiments with two different schemes on the Penn Treebank and the Chinese Penn Treebank respectively, arriving at the same conclusion that jointly parsing heterogeneous dependencies can give improved performances for both schemes over the individual models.
The probe type CNT network junctions for real-time detection of neurotransmitter

In order to detect L-glutamate, we immobilized L-glutamate oxidase (GLOD) on the probe-type carbon nanotube (CNT) network junctions by a non-covalent functionalized method to preserve their electronic characteristics. After immobilizing GLOD on the CNT network, the excess reactive groups of linker molecule remaining on its surface were deactivated and blocked by ethanolamine. The electrical property of the GLOD-immobilized CNT network transistor was characterized as source-drain current that depended on liquid gate voltage. The real time electronic response of the GLOD-coated CNT network junctions was conducted with a glutamate standard solution in vitro and the 11 vessel occlusion (11VO) rat model in vivo. The ultrahigh sensitivity, selectivity, and fast response time of GLOD-immobilized CNT-FET (field-effect transistor) could provide great potential for the real time electronic detection of extracellular glutamate levels in the brain.
Model based testing of data constraints: testing the business logic of a Mnesia application with Quviq QuickCheck

Correct implementation of data constraints, such as referential integrity constraints and business rules is an essential precondition for data consistency. Though most modern commercial DBMSs support data constraints, the latter are often implemented in the business logic of the applications. This is especially true for non relational DBMS like Mnesia, which do not provide constraints enforcement mechanisms. This case study examines a database application which uses Mnesia as data storage in order to determine, express and test data constraints with Quviq QuickCheck, adopting a model-based testing approach. Some of the important stages of the study described in the article are: reverse engineering of the database, analysis of the obtained database structure diagrams and extraction of data constraint, validation of constraints, formulating the test specifications and finally running the generated test suits. As a result of running the test suits randomly generated by QuickCheck, we have detected several violations of the identified and validated business rules. We have found that the applied methodology is suitable for applications using non relational, unnormalized databases. It is important to note the methodology applied within the case study is not bound to a specific application or DBMS, and can be applied to other database applications.
Rhythmic Auditory Stimulation reinstates natural 1 / f structure in gait of Parkinson ’ s patients

Parkinson’s disease (PD) and basal ganglia dysfunction impair movement timing, which leads to gait instability and falls. Parkinsonian gait consists of random, disconnected stride times— rather than the 1/f structure observed in healthy gait—and this low fractal scaling of stride times is a strong predictor of falling. Walking with fixed-tempo Rhythmic Auditory Stimulation (RAS) improves many aspects of gait timing; however it requires attention and lowers fractal scaling away from healthy 1/f structure. In this experiment, PD patients and healthy participants walked with a) no auditory stimulation; b) fixed-tempo RAS; and c) an interactive rhythmic auditory stimulation system that used foot sensors and nonlinear oscillators to track and interact with the human’s timing. Patients effortlessly synchronized with the interactive system, their gait felt more stable, and their fractal scaling returned to levels of healthy participants. With the fixed-tempo RAS, patients and healthy participants rarely synchronized, and when they did their fractal scaling declined away from healthy 1/f levels. After removing the interactive rhythmic stimulation, the PD patients’ boost in fractal scaling persisted, indicating that the interaction stabilized the internal rhythm generating system and reintegrated timing networks. Interactive rhythmic auditory stimulation offers a flexible, portable, low-cost, non-invasive rehabilitation system that can improve the mobility, safety, and quality of life of Parkinson’s Disease patients.
Analyzing Socio-economic and Geographical factors for Crime Incidents using Heat maps and Hotspots

Spatio-temporal data mining techniques are used for crime analysis for their knowledge oriented and meaningful visual representation of crime incidents. Visual representation of crime patterns assist analysts with in-depth understanding of crime behavior with time and location. The representation can be made more knowledgeable and perceptible by incorporating details of socio-economic factor and areafis geographical information providing insights to features that actually play role in certain crime pattern. To analyze the impact of these factors, two of the best density calculation clustering techniques i.e. Heat Maps and Hot Spots analysis are performed for Crime Against Person and Crime Against Property. The analysis demonstrated that Crimes Against Persons are more frequent in rural and sub-urban areas with mostly low socio-economic conditions; whereas, Crimes Against Property are mostly in commercial areas with mix socio-economic conditions.
A SYMMETRIC VERSION SPACE ALGORITHM FOR LEARNING DISJUNCTIVESTRING CONCEPTS

The symmetric version space algorithm (SVS) learns disjunctions of string patterns by example. The learnable string concepts are a subset of regular expressions. The running time of the algorithm is reduced, because the system learns a top{down description of the string concepts. Di erent parts of the algorithm learn descriptions at di erent levels of the concept independently. This technique is similar to factoring the version space, in order to restrict the search space. The problem of fragmentation of the G{set is overcome by using a symmetric version space approach.
Serotonergic modulation of cognition

This work was jointly sponsored by the University of Cambridge and Cambridgeshire and Peterborough NHS Foundation Trust and approved by the NHS East of England - Cambridge Central Research Ethics Committee (REC reference: 15/EE/0004). It was was supported by a Wellcome Trust Senior Investigator Award to TW Robbins (104631/Z/14/Z) and by the NIHR Cambridge Biomedical Research Centre (Mental Health theme). A Medical Research Council (MRC) Doctoral grant studentship was awarded to Dr N Skandali (1432057).
I-Vectors and Structured Neural Networks for Rapid Adaptation of Acoustic Models

A lot of interest has been risen in the last years on the adaptation of deep neural network (DNN) acoustic models, as the latter become the state-of-art in automatic speech recognition. This work focuses on approaches that allow for rapid and robust adaptation of such models. First, i-vectors are added to the DNN input as speaker-informed features. An informative prior is introduced to i-vector estimation to improve the robustness to limited adaptation data. I-vectors are then combined with a structured adaptive DNN, the multibasis adaptive neural network (MBANN), and the complementarity of these adaptation techniques is investigated. Moreover, i-vectors are used to predict the MBANN transforms, avoiding the initial decoding pass and alignment. These approaches are evaluated on a U.S. English Broadcast News (BN) transcription task with two distinct sets of test data. The first, from the BN task and BN-style Youtube videos, yields test data acoustically matched to the training data, while the second set is from acoustically mismatched Youtube videos of diverse context. The performance gains from these schemes are found to be sensitive to the level of mismatch between training and test sets. The MBANN system combined with i-vector input achieves best performance for BN test sets. The i-vector-based predictive MBANN scheme is proven to be more robust to acoustically mismatched conditions and outperforms the other adaptation schemes in such scenarios.
Likelihood inference in nearest‐neighbour classification models

Traditionally the neighbourhood size k in the k-nearest-neighbour algorithm is either fixed at the first nearest neighbour or is selected on the basis of a crossvalidation study. In this paper we present an alternative approach that develops the k-nearest-neighbour algorithm using likelihood-based inference. Our method takes the form of a generalised linear regression on a set of k-nearest-neighbour autocovariates. By defining the k-nearest-neighbour algorithm in this way we are able to extend the method to accommodate the original predictor variables as possible linear effects as well as allowing for the inclusion of multiple nearest-neighbour terms. The choice of the final model proceeds via a stepwise regression procedure. It is shown that our method incorporates a conventional generalised linear model and a conventional k-nearest-neighbour algorithm as special cases. Empirical results suggest that the method out-performs the standard k-nearest-neighbour method in terms of misclassification rate on a wide variety of datasets. Copyright Biometrika Trust 2003, Oxford University Press.
Using data flow analysis for the reliability assessment of safety-critical software systems

Reliability analysis for safety-critical software systems often needs additional expert knowledge, because of the small data-sets available. A Bayesian approach is used to develop a reliability model based on expert knowledge and small data-sets. The expert knowledge is obtained with data flow analysis. Certain variables in the program code are examined to calculate their probability of causing a failure. These additional information are incorporated into a suitable distribution function to be able calculate reliability characteristics with greater precision.
Comparing SAS® PROC MI and IVEware Callable Software

Multiple imputation has become a common technique for dealing with missing data, to account for the variability involved with imputing data values. Advancements in technology have allowed the development of a number of software and modules/packages that can perform multiple imputation in SAS, R, Stata, and other programs. In this paper we focus on two software packages: SAS PROC MI procedure (SAS Institute 2000) and IVEware. IVEware (Raghunathan et al. 2002) is an imputation and variance estimation software that can be run in SAS (SAS callable). SAS PROC MI provides imputation options including regression imputation, Markov chain Monte Carlo (MCMC) techniques, and fully conditional specification, whereas the IVEware implements a sequential regression imputation technique. Although the basic modeling and prediction in SAS PROC MI and IVEware are comparable; the imputations are developed under different distribution assumptions of variables with missing values. This paper compares the two methods using a data set with count variables having a Poisson distribution. We compare the imputed data with two approaches. The first approach directly imputes the count variable; the second is a modified approach where a binary variable indicating zero or non-zero count is imputed and then the non-zero counts are imputed. We discuss the limitations and issues encountered.
Assessing individual and team readiness

The purpose of this research is to determine if teams can successfully predict their readiness to perform a mission via noninvasive methods. We present a method for assessing individual and team readiness in the form of a questionnaire designed to allow team members to predict the readiness of their team to perform a particular mission. To test the questionnaire's utility, we conducted a field experiment using University of Virginia Army Reserve Officer Training Corps (ROTC) cadets as test subjects because of the fit between our research domain and their field training exercise needs. We use the questionnaire data and scores of actual performance to determine the accuracy of individual and team readiness predictions. The results of this experiment are inconclusive. We recommend further experimentation with experienced subjects to determine if this methodology can be developed into a reliable predictive tool
3D-SYMBOLIZATION USING ADAPTIVE TEMPLATES

: For communication tasks adapted types of information are needed. In navigation, for example, landmarks play an essential role. In order to be able to recognize these landmarks immediately also from larger distances, unimportant details have to be simplified and relevant and characteristic features have to be visible. Thus, these characteristics should be highlighted or enhanced, which is a generalization function. We concentrate on building landmarks. In order to simplify and emphasize 3D buildings, the idea in this research is to use a generic set of templates for typical 3D-buildings and replace the original 3D shape with the most similar of those templates. In this paper, we briefly describe the whole workflow, but concentrate on the adaptation. For this adaptation process we propose optimization techniques. Depending on the target function to optimize, different approaches can be chosen, which will be described in the paper. The results using Least Squares Adjustment are presented.
Space-time adaptive monopulse for RD-STAP parameters estimation

Space-time adaptive processing (STAP) is the key technology of airborne early-warning radar to suppress both clutter and interference, and the extended factored approach (EFA) is the well-established reduced-dimension (RD) STAP approach for engineering implementation. In this paper, the adaptive monopulse technique is introduced into EFA, and an efficient space-time adaptive monopulse to jointly estimate the target velocity and location is presented. Theoretical analysis and simulation results demonstrate that, when the real target Doppler frequency deviates from the central frequency of the Doppler bin, the proposed approach can estimate the velocity and angle parameters more precisely than the conventional adaptive monopulse.
Path Following Control of a Mobile Robot via Line-of-Sight Method

Path tracking control problem of a mobile robot is discussed in this paper. It’s a typical problem in the field of robot locomotion. A control strategy combined with fuzzy control based on the Line of Sight (LOS) is proposed. According to the experience of driver, fuzzy control rule is selected with hierarchical method. With Line-of-sight method, a fuzzy control approach is then constructed. The results of practical experiment show that the designed fuzzy controller is very effective in path tracking.
Validating the Effectiveness of Object-Oriented Metrics over Multiple Releases for Predicting Fault Proneness

In this paper, we empirically investigate the re-lationship of existing class level object-oriented metrics with fault proneness over the multiple releases of the software. Here we first, evaluate each metric for their potential to predict faults independently by performing univariate logistic regression analysis. Next, we perform cross-correlation analysis between the significant metrics to find the subset of these metrics for an improved performance. The obtained metrics subset was then used to predict faults over the subsequent releases of the same project datasets. In this study, we used five publicly available project datasets over their multiple successive releases. Our results reported that the identified subset metrics demonstrated an improved fault prediction with higher accuracy and reduced misclassification errors.
Towards The Development of Virtual Keyboard: An Activity Recognition Approach

This paper presents an activity recognition based scheme for the development of the virtual keyboard for mobile and portable computing devices. Keystrokes activities are modeled in terms of the joints movements of fingers using kinematic model of hand. Generic activity recognition system is employed for keystroke detection and recognition. Hands activities on a flat surface are captured using mobile's secondary camera. Scheme composed of two steps: first, low-level features are extracted in terms of joints trajectories estimation using finger joints localization and optical flow calculation. Second, trajectories are further interpreted into feature vectors. Feature vectors are trained and classified using Hidden Markov Models leading towards the keystrokes recognition. Real- time implementation covers the accurate detection and recognition of 78 keys of the keyboard, thus providing enriched set of keys to the users of mobile devices. (Yousaf MH, Habib HA, Azhar K, Hussain F, Rizwan M, Asim MM. Towards the Development of Virtual Keyboard: An Activity Recognition Approach. Life Sci J 2013;10(10s):275-282) (ISSN:1097-8135). http://www.lifesciencesite. com. 45
Methodological challenges in utilizing miRNAs as circulating biomarkers

MicroRNAs (miRNAs) have emerged as important regulators in the post‐transcriptional control of gene expression. The discovery of their presence not only in tissues but also in extratissular fluids, including blood, urine and cerebro‐spinal fluid, together with their changes in expression in various pathological conditions, has implicated these extracellular miRNAs as informative biomarkers of disease. However, exploiting miRNAs in this capacity requires methodological rigour. Here, we report several key procedural aspects of miRNA isolation from plasma and serum, as exemplified by research in cardiovascular and pulmonary diseases. We also highlight the advantages and disadvantages of various profiling methods to determine the expression levels of plasma‐ and serum‐derived miRNAs. Attention to such methodological details is critical, as circulating miRNAs become diagnostic tools for various human diseases.
2 Direct perception Propositional attitude Cognition Indirect discourse Conditional Simultaneous states of affairs Sequential states of affairs Unspecified temporal order

1. Introduction Since Foley and Van Valin (1984), lots of advances have been made in RRG, especially in the realm of clause-internal syntax. Recently, some interesting proposals have been made regarding the typology of complex sentences, some of which are incorporated in Van Valin (to appear). In this paper, I wish to give some more
Automating Planning Using Natural Language

We propose PLANL, an approach for PLAnning with Natural Language, to accelerate the development of automated planning systems, enable plan sharing across multiple planners, and facilitate natural language interaction. PLANL uses generative sublanguage ontologies (GSOs) to robustly and accurately translate planning knowledge descriptions into representations such as STRIPS or hierarchical task networks. GSO’s accomplish this through a novel ability for efficiently representing and resolving polysemy. Unlike alternative approaches, PLANL does not have a proprietary plan representation. Instead, it exploits existing plan representations and selects a linguistically motivated conceptual vocabulary for them.
Cycle-true simulation of the ST10 microcontroller including the core and the peripherals

With the rising complexity of electronic systems, containing more and more hardware and software parts, it becomes necessary to simulate simultaneously, hardware and software at whatever abstraction level. These simulation techniques, called cosimulation, require fast and flexible simulators. We introduce the elaboration of a microcontroller simulator including the core and the peripherals for an accurate hardware/software cosimulation at the clock-cycle level. It is our goal to have a simulator which is fast enough to simulate a few minutes of real time execution within a reasonable lapse of time. To be more precise, we deal with the realization of a simulator for the ST10 microcontroller and its integration into a co-simulation environment. We give our global approach and explain our techniques in detail. We also present new tracks to obtain improved performance with this kind of simulator.
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-Gram Language Model

Lexical substitutes have found use in areas such as paraphrasing, text simplification, machine translation, word sense disambiguation, and part of speech induction. However the computational complexity of accurately identifying the most likely substitutes for a word has made large scale experiments difficult. In this letter we introduce a new search algorithm, FASTSUBS, that is guaranteed to find the K most likely lexical substitutes for a given word in a sentence based on an n-gram language model. The computation is sublinear in both K and the vocabulary size V. An implementation of the algorithm and a dataset with the top 100 substitutes of each token in the WSJ section of the Penn Treebank are available at http://goo.gl/jzKH0.
Local versus landscape determinants of butterfly movement behaviors

Large-scale patterns of animal distributions and abundances may be determined by mechanisms that act at local or landscape scales. We studied the movement behaviors of four species of bottomland butterflies in a natural setting to examine the determinants of movement behavior across different scales. We tested the relative importance of three landscape attributes: drainage slope, boundary type, and stream proximity, and local habitat attributes related to food plants and plants that influence habitat structure. Across species, we tested the relative importance of organism size and habitat specificity to explain response variation. In general, butterfly responses to landscape features were more universal than responses to local features. Specifically, results from this study showed that drainage slope did not influence movement behaviors but boundary type, stream proximity, and host plant abundance all influenced movement patterns. Responses to local features varied by species and often complemented landscape effects on movement. Responses to all features were not related to butterfly size, but did vary in accordance with butterfly host plant specificity. These behaviors help to explain landscape-level variation in population distribution among species.