Miracle-dz/UniFAR-FTUs huggingface dataset:
12CO(3-2) Emission in Spiral Galaxies: Warm Molecular Gas in Action?

Using the APEX submillimeter telescope we have investigated the 12CO(3-2) emission in five face-on nearby barred spiral galaxies, where three of them are high surface brightness galaxies (HSBs) lying at the Freeman limit, and two are low surface brightness galaxies (LSBs). We have positive detections for two of three HSB spirals and nondetections for the LSBs. For the galaxies with positive detection (NGC 0521 and PGC 070519), the emission is confined to their bulges, with velocity dispersions of ~90 and ~73 km s-1 and integrated intensities of 1.20 and 0.76 K km s-1, respectively. For the nondetections, the estimated upper limit for the integrated intensity is ~0.54 K km s-1. With these figures we estimate the H2 masses as well as the atomic-to-molecular mass ratios. Although all the galaxies are barred, we observe 12CO(3-2) emission only for galaxies with prominent bars. We speculate that bars could dynamically favor the 12CO(3-2) emission, as a second parameter after surface brightness. Therefore, secular evolution could play a major role in boosting collisional transitions of molecular gas, such as 12CO(3-2), especially in LSBs.
Relevance feedback for image retrieval in structured multi-feature spaces

An approach for content-based image retrieval with relevance feedback based on a structured multi-feature space is proposed. It uses a novel kernel for merging multiple feature subspaces into a complementary space. The kernel exploits nature of the data by assigning appropriate weights for each feature set. The weights are dynamically adapted to user preferences in a relevance feedback scenario.
Motion Estimation to a Failed Satellite on Orbit using Stereo Vision and 3D Model Matching

An algorithm is developed for estimating the motion (relative attitude and relative position) of large pieces of space debris, such as failed satellites. The algorithm is designed to be used by a debris removal system which would perform various operations on space debris such as observation, investigation, capture, repair, refuel and de-orbit. During these operations, the debris removal system must control its position and attitude simultaneously. Such six degree-of-freedom control becomes more difficult if the failed satellite is changing attitude, such as by nutation. The information required as feedback signals for such a controller is relative - position, velocity, attitude and angular velocity - and these are expected to be measured or estimated from image data. The algorithm uses a combination of stereo vision and 3D model matching, applying the ICP (iterative closest point) algorithm, and uses time series of images to increase the reliability of the relative attitude and position estimates. To evaluate the algorithm, a visual simulator is prepared to simulate the on-orbit optical environment in terrestrial experiments, and the motion of a miniature satellite model is estimated using images obtained from the simulator
The role of inhibition in older and younger adults’ lexical competition

With age, uncertainty becomes more pronounced in speech perception; it becomes harder to recognize words in noise (Helfer & Freyman, 2014) and to inhibit similar sounding high-frequency lexical competitors (Revill & Spieler, 2012). Similar to McMurray, Munson, and Tomblin (2014), we contrast lexical competition and speech perception, but in older and younger adults, because older adults have weaker encoding of some phonetic contrasts (Anderson, Parbery-Clark, White-Schwoch, & Kraus, 2012; Bidelman, Villafuerte, Moreno, & Alain, 2014) and greater lexical effects than younger adults (Mattys & Scharenborg, 2014; Revill & Spieler, 2012). The lexical bias found in older adults (Mattys & Scharenborg, 2014) could be indicative of decreased lexical inhibition, which would result in increased activation among lexical competitors. Indeed, Sommers and Danielson (1999) found that older adults had greater difficulty than younger adults recognizing words with many semantic neighbours, suggesting that older adults exhibit difficulty inhibiting competitors. Using the visual world paradigm, we investigated the role of inhibition, lexical competition and phonetic sensitivity for word recognition in younger (n=28) and older adults (n=27). We manipulated voice onset time and included a phonological competitor to the target to contrast the effects of lexical competition and phonetic sensitivity. To measure domain-general inhibition, participants completed a Simon task. Results from a mixed-effects linear regression on discrimination score (difference between looks to the target image and to the competitor) suggest that all listeners have better discrimination for less ambiguous stimuli, but individuals with poorer inhibition were more affected by stimulus ambiguity (Continuum step x Inhibition: Beta=0.12, t(54)=2.4, p=0.02). Additionally, younger adults with poorer inhibition are less distracted by weak competitors, but more distracted by strong competitors than those with better inhibitory abilities (Continuum step x Inhibition x Age Group: Beta=0.28, t(54)=2.77, p=0.008). These results suggest that older and younger adults perform similarly when distinguishing between competitors, and that individual ability to inhibit competitors informs word recognition behaviour as speech ambiguity increases. This provides evidence that older and younger adults employ different strategies when resolving lexical competition, as inhibitory ability plays a different role across the two age groups. [Full references included in supplementary material]
Designing an Adaptive Automation System for Human Supervision of Unmanned Vehicles: A Bridge from Theory to Practice

Careful consideration must be given to the implementation of automation into complex systems. Much research in adaptive automation has identified challenges for system implementation. A key focus of this research has surrounded the methods of automation invocation including critical events, measurement, and modeling techniques. However, little consideration has been given to selecting and implementing appropriate techniques for a given system as a guide to designers of adaptive automation. This paper proposes such a methodology. We demonstrate the use of this methodology by describing a case study about a system designed to support effective communication and collaboration between the commander and vehicle operator in an unmanned aerial vehicle (UAV) system.
Fast Mode Decision Using Fuzzy Reasoning for Motion Estimation in H.264/AVC Video Coding

To improve the coding efficiency, many optional macroblock encoding modes have been provided in H.264/AVC video coding standard. Exhaustively searching over all possible modes to choose the best coding mode for each macroblock is the most reliable one, but it causes a lot of computational complexity. This paper proposes a fast algorithm to select the coding mode such that the number of candidate modes to be searched in motion estimation is significantly reduced and the coding picture quality is almost remained. Fuzzy logic technique with four inputs, one motion degree and three edge energies distribution of the image is used in the proposed algorithm to select the most appropriate mode for the motion estimation in H.264/AVC video coding. The simulation results show that the encoding time in the proposed algorithm is reduced to 55.7%~70% of the time searching for all coding modes exhaustively and the coded picture quality is not affected almost.
Invasion speeds in uctuating environments

Biological invasions are increasingly frequent and have dramatic ecological and economic consequences. A key to coping with invasive species is our ability to predict their rates of spread. Traditional models of biological invasions assume that the environment is temporally constant. We examine the consequences for invasion speed of periodic and stochastic £uctuations in population growth rates and in dispersal distributions.
Designing the Control Architecture for Algorithm Specific Custom Processors on FPGAs

We consider the implementation of high-level image processing operations using a custom serial processor on an FPGA. When such operations are implemented directly using Handel-C, the algorithm executes serially, controlled by an implicit finite state machine. This architecture is then modified to use an explicit software controller. We demonstrate that these modifications increase design efficiency slightly, because the control logic does not have to be implemented in logic blocks. The architecture is also more flexible, as it is software driven without “hardwired” control. These benefits are at the cost of RAM for the control logic and increased time required to design the controller.
Comparison of several lane marking line recognition methods

This paper introduces the theory of algorithm results of different image processing techniques in lane marking line recognition, including binarize, edge detection and lane extraction. Analyze and compare the accuracy of the each stages of processing algorithms, reliability and real - time performance. And then compare the various combinations of algorithms to detect lane marking lines in several conditions. Finally, an ideal detection algorithm is proposed, which using progressive threshold method to binarize, the contour extraction method, lane edge tracing and curve fitting method.
Database summarization approach based on description logic theory

In this paper, we propose a new approach of database summarization. Our proposal consists in building a set of summaries that gives many levels of granularity. The main contribution of our work consists in giving a generic approach, based on description logic language, which operates on both the schema and the database content. The summarization process leads to building a lattice of summaries where each one gives a certain measure of precision. Our proposal offers a generic setting in which current summarization techniques can be considered as particular cases.
Estimating State Space Parameters

We introduce the problem of estimation of state space parameters, argue that it is an interesting and practically relevant problem, and study several simple estimation techniques. Particularly, we focus on estimation of the number of reachable states. We study techniques based on sampling of the state space and techniques that employ data mining techniques (classification trees, neural networks) over parameters of breadth-first search. We show that even through the studied techniques are not able to produce exact estimates, it is possible to obtain useful information about a state space by sampling and to use this information to automate the verification process.
Human Attribute Recognition by Rich Appearance Dictionary

We present a part-based approach to the problem of human attribute recognition from a single image of a human body. To recognize the attributes of human from the body parts, it is important to reliably detect the parts. This is a challenging task due to the geometric variation such as articulation and view-point changes as well as the appearance variation of the parts arisen from versatile clothing types. The prior works have primarily focused on handling geometric variation by relying on pre-trained part detectors or pose estimators, which require manual part annotation, but the appearance variation has been relatively neglected in these works. This paper explores the importance of the appearance variation, which is directly related to the main task, attribute recognition. To this end, we propose to learn a rich appearance part dictionary of human with significantly less supervision by decomposing image lattice into overlapping windows at multiscale and iteratively refining local appearance templates. We also present quantitative results in which our proposed method outperforms the existing approaches.
REPORT RAPPORT

In this paper we show how a user can inﬂuence recovery of Bayesian Networks from a database by specifying prior knowledge. The main novelty of our approach is that the user only has to provide partial prior knowledge, which is then completed to a full prior over all possible network structures. This partial prior knowledge is expressed among variables in an intuitive pairwise way, which embodies the uncertainty of the user about his/her own prior knowledge. Thus, the uncertainty of the model is updated in the normal Bayesian way.
Efficient Query Processing in Large Traffic Networks

We present an original graph embedding to speedup distance-range and k-nearest neighbor queries on static and/or dynamic objects located on a (weighted) graph. Our method is used to compute a lower and upper bounding filter distance which approximates the true shortest path distance significantly better than traditional filters. In addition, we discuss how the computation of the exact shortest path distance in the refinement step can be boosted by using the embedded graph.
Survey on Text Classification ( Spam ) Using Machine Learning

E-mail spam is a very serious problem in today’s life. It has many conséquences like it causes lower productivity, occupy space in mail boxes, extend viruses, Trojans, and materials containing potentially harmful information for a certain category of users, Destroy stability of mail servers, and as a result users spend a lot of time for sorting incoming mail and deleting undesirable correspondence. There are various classifiers used for e-mail spam detection like Naïve Bayes Classifier, KNN Classifiers etc. We will use SVM classifier for e-mail spam detection which has not been used till now for detecting e-mail spam.
Timing Defect Diagnosis in Presence of Crosstalk for Nanometer Technology

With feature sizes shrinking, manufacturing defects and parameter variations often cause design timing failures. Crosstalk coupling is one of such causes. It is essential that timing failures be correctly and quickly diagnosed. The authors present a methodology to diagnose the delay-defect in presence of crosstalk, given the physical information such as crosstalk coupling capacitance, neighborhood information and SDF delay information. The authors provide diagnosis results for 180, 130, 90 and 65 nm technologies
Young Citizens Participation

A growing body of literature from different disciplines addresses concepts and measurement of citizenship. The present article seeks to contribute to this field by examining the issue of youth citizenship from a comparative international perspective and proposing a simplified conceptual model that can be operationalized. This model includes a community dimension, which refers to individual’s relationship with their community associations, and a civic dimension, concerning institutional processes such as voting and/or political activism. The model was tested using multigroup confirmatory factor analysis and measurement equivalence for eighth-grade students (n = 139.875) across 38 countries that participated in the International Civic and Citizenship Study (2009). Our results find support for the proposed conceptual model and its invariance across countries, and we discuss the implications for theory and further research.
Composition of Interstellar Clouds in the Disk and HaloIV

In this paper we continue our investigation of diiuse clouds in the interstellar medium with an analysis of the line of sight towards the star HD 215733, located in the Galactic halo some 1700 pc below the plane. As in our previous papers, we utilize the component tting technique to determine velocities, velocity widths, and column densities for a variety of ions in each of the absorbing regions detected. Our data include a large number of ultraviolet absorption lines observed with the Goddard High Resolution Spectrograph and ground-based observations of Ca II K absorption and H I 21-cm emission. We detect 23 components (absorbing regions) in the low-ionization species towards HD 215733 and 7 components in the highly ionized species (i.e., Si 3+ , C 3+ , and N 4+). The low ion components are predominantly neutral. Gas-phase abundances measured for these components follow the pattern seen for halo stars in our previous studies. These have been interpreted as indicating either that a nearly-indestructible population of interstellar grains is present or that the intrinsic abundances of a number of elements in the ISM are signiicantly subsolar. Kinetic temperatures are estimated for 16 low-ion components. Of these, four (with the highest values of jv LSR j are warm, with T > 1000 K; 6 are cold, with T < 300 K. Extensive diagnostic information includes data on the excited atoms C + and C 0 , showing that in the cold clouds n(H 0) ' 25 cm ? 3 , with n(H 0)T ' 2500 K cm ? 3. The ionization equilibrium of C 0 , Mg 0 , S 0 , and Ca + gives log n e values diiering systematically between these diierent species by up to 1 dex. Correction for these diierences, together with a somewhat uncertain overall calibration with excited C + , gives values of log n e in the range from ? 2:1 to ? 2:7 for the cold clouds in the four best determined cases, and from ? 1:2 to ? 1:8 for the 3 warm clouds with measured n e. The cold cloud values yield n e =n(H 0) ' 2 10 ? 4 , suggesting ionization of the heavier elements only (with all H neutral), but values higher by half a dex would also be consistent with the data. Strong absorption features of Si IV and C IV appear in seven components. For two of these, the ratio of …
Approximation via Correlation Decay when Strong Spatial Mixing Fails

Approximate counting via correlation decay is the core algorithmic technique used in the sharp delineation of the computational phase transition that arises in the approximation of the partition function of anti-ferromagnetic two-spin models. Previous analyses of correlation-decay algorithms implicitly depended on the occurrence of strong spatial mixing (SSM). This means that one uses worst-case analysis of the recursive procedure that creates the sub-instances. We develop a new analysis method that is more refined than the worst-case analysis. We take the shape of instances in the computation tree into consideration and amortise against certain "bad" instances that are created as the recursion proceeds. This enables us to show correlation decay and to obtain an FPTAS even when SSM fails. We apply our technique to the problem of approximately counting independent sets in hypergraphs with degree upper-bound Delta and with a lower bound k on the arity of hyperedges. Liu and Lin gave an FPTAS for k>=2 and Delta =3 and Delta =8. Our technique also applies for larger values of k, giving an FPTAS for k>=Delta. This bound is not substantially stronger than existing randomised results in the literature. Nevertheless, it gives the first deterministic approximation scheme in this regime. Moreover, unlike existing results, it leads to an FPTAS for counting dominating sets in regular graphs with sufficiently large degree. We further demonstrate that approximately counting independent sets in hypergraphs is NP-hard even within the uniqueness regime.
Voice synthesis application based on syllable concatenation

This article presents a voice synthesis application based on syllable concatenation. The system is dedicated for Romanian language, so it was need to work on special rules to decompose Romanian text into syllables. Also for preserving initial prosody of text, accentuation of syllables inside word had to be determined. Then we have recorded a vocal database with the most frequent syllables of Romanian language. A unit matching algorithm matches linguistic units from the input text and acoustic units from database. Acoustic units are then concatenated and converted into sound by mean of a synthesizer.
Eulerian variational principles for stratified hydrostatic equations

The aim of this paper is to advocate the use of variational and Hamiltonian formulations of the hydrostatic equations of motion in finding new conservative numerical techniques for forecast models. For that reason, the fundamental conservative structure of the hydrostatic equations of motion is presented. Variational principles and Hamiltonian formulations of various hydrostatic equations, stratified continuously or in layers, are derived systematically from an Eulerian perspective in the horizontal and a Lagrangian or material perspective in the vertical direction. Variational formulations are derived for the hydrostatic incompressible Boussinesq system and the hydrostatic equations in multiple isentropic and isopycnic layers. The various Hamiltonian formulations presented share similar Poisson brackets; that is, the (contribution to the) Poisson bracket in each layer or in the continuously stratified case is the one for the shallow-water equations with the depth replaced by the appropriate pseudodensity, while the potential (and internal) energy in the Hamiltonian differs in each case. For hydrostatic equations in material coordinates, either stratified continuously or in layers, the coupling between layers happens solely through the Hamiltonian, an observation that may aid in searching for conservative numerical discretizations.
Influence of Maternal High Fat Diet, Stress and Cocaine on Neural Mechanisms of Reward and Anxiety in Rat Offspring

Maternal obesity has important health consequences for the mother and her offspring. Experiments presented in this dissertation explored the role of maternal overnutrition with a high fat diet (HFD) on several aspects of offspring phenotype: rewardand stress-related behaviours, the endocrine stress response, and associated neural gene expression. First, I examined maternal HFD effects on offspring phenotype in stressrelated brain regions. Maternal HFD was associated with altered expression of stress-related genes, a heightened endocrine stress response and increased anxiety behaviour in adult offspring. Genes central to stress and drug addiction (TH and CRF) were upregulated in HFD offspring, suggesting that maternal HFD alters neural systems underlying related processes. Second, I investigated the role of maternal HFD on offspring phenotype following chronic cocaine exposure. Maternal HFD increased anxiety in salinetreated control females, reduced anxiety in cocaine-treated females, but did not interact with cocaine-primed locomotor activity or neural gene expression. These
Parsing Manually Detected and Normalized Disfluencies in Spoken Estonian

An experiment with an Estonian Constraint Grammar based syntactic analyzer is conducted, analyzing transcribed speech. In this paper the problems encountered during parsing disfluencies are analyzed. In addition, the amount by which the manual normalization of disfluencies improved the results of recall and precision was compared to non-normalized utterances.
Particle Swarm Optimization based PI controller tuning for Fermentation Process

The activities related to fermentation process are uncertain and nonlinear in nature. It poses many challenging characteristics like multivariable interactions, unmeasured state variables, system and time varying parameters. Generally, PID controllers are used in these processes. Tuning of PID controller is necessary for the satisfactory operation of the system. The tuning of PID controllers depends upon how the process responds to the controllers' corrective effort. The conventional tuning methods like Z-N method and IMC method takes more time, reduces stability and are more sluggish. In this paper, the problem of identifying the PI controller parameters is considered as an optimization problem. An attempt has been made to optimize the values of the PI controller parameters employing particle swarm optimization technique (PSO). From the simulation results it is observed that the PI controller designed with PSO yields better results when compared to the conventional methods