Miracle-dz/UniFAR-FTUs huggingface dataset:
KE and MDO based Intelligent Conceptual Design method for tactical missile

Multi-Disciplinary Optimization (MDO) has been proven useful for conceptual design of complex and coupled system. Knowledge Engineering (KE) has been used as a mean of providing initial conceptual design scheme. By combining KE and MDO, a missile conceptual design method was developed in which KE can be used to provide baseline for MDO. This method promotes more intelligent and efficient missile conceptual design than traditional “human-centered” mode. Firstly, the design flow with two phases included was described. Then, the approach how to use KE in conceptual design was presented by decomposing system design task into several disciplinary design tasks. Following that, the critical approaches about missile MDO were presented, including disciplinary modeling, system modeling and optimization solving. In the end, an Intelligent Conceptual Design Environment (ICDE) was established and the software prototype was developed.
Appearance based Generic Object Recognition

In this paper we have presented a scheme for categorization of objects based upon appearance information. The appearance information has been encoded using principal component based representations. A new scheme has been suggested for constructing appearance hierarchy through combination of the appearance space of individual objects. A categorization algorithm has been presented which exploits the features of the appearance hierarchy. Experimentations have produced encouraging results.
Pulse Dynamics in Actively Modelocking Fiber Optic Lasers

Compact sources of optical pulses near telecommunication wavelengths are key enabling technologies for high speed fiber optic communication systems and interconnection networks. The generation of stable pulses is accomplished within a laser cavity, a process known as modelocking. In a simple pulse modelocking device, a modelocking element within the fiber laser cavity preferentially attenuates weaker intensity portions of individual pulses. This attenuation is compensated by the gain medium which acts to preserve the total cavity energy. Initially, shaping occurs since the peak of a pulse experiences a higher net gain per round trip than its lower intensity wings. This narrowing (compression) of a propagating pulse is eventually limited by the gain-bandwidth of the amplifying medium. The primary focus of this research is the development of an analytic model describing active modelocking. Active modelocking is achieved by periodically modulating the cavity gain and can be modeled by a nonlinear wave equation. By using Jacobi elliptic functions to capture the periodic forcing to the cavity, we are able to construct exact solutions representing modelocked pulse trains. Two families of pulse train solutions are generated: one in which neighboring pulses are in-phase, and a second in which neighboring pulses are out-of-phase. We show that only out-of-phase solutions allow for stable modelocked pulse trains. Further, pulse-to-pulse interactions can generate instabilities that destroy the pulse train altogether or lead to phenomena known as Q-switching. This Q-switching has been observed in experiments, but has eluded analytic description. However, this model is able to capture this behavior and allows for an analytic description of this particular kind of laser instability. The model predicts the operating regimes of the laser and so can be applied directly to the experimental situation.
Epha4 controls the midline crossing and contralateral axonal projections of inferior olive neurons

The guidance of axonal projections to ipsilateral and contralateral regions is essential for integration of bilateral sensory information and coordination of movement. In the development of olivocerebellar projections, newborn neurons of inferior olivary (IO) nuclei ventrally migrate from the hindbrain rhombic lip to the floor plate (FP). The cell bodies of IO neurons cannot cross the FP but their axons can, and thus IO neurons project their axons only to the contralateral cerebellar cortex. The molecular mechanisms determining the contralateral axonal projections of IO neurons, however, are obscure. The IO neurons and their axons express EphA4, whereas the FP expresses an EphA4 ligand, EphrinB3, from embryonic day 12.5. Therefore, we tested whether EphA4‐deficient mice (EphA4−/−) would show impairment in the development of olivocerebellar projections. We found that, in EphA4−/− embryos, some of the IO neurons projected their axons to the ipsilateral cerebellar cortex because the cell bodies of the IO neurons abnormally crossed the FP. Furthermore, even in adults, EphA4−/− cerebella were bilaterally innervated by unilateral IO subnuclei. These observations indicate that EphA4 is involved in the contralateral axonal projections of IO neurons by preventing their cell bodies from crossing the midline FP. J. Comp. Neurol. 520:1702–1720, 2012. © 2011 Wiley Periodicals, Inc.
Providing Multilingual, Multimodal Answers to Lexical Database Queries

Language users are increasingly turning to electronic resources to address their lexical information needs, due to their convenience and their ability to simultaneously capture different facets of lexical knowledge in a single interface. In this paper, we discuss techniques to respond to a user's lexical queries by providing multilingual and multimodal information, and facilitating navigating along different types of links. To this end, structured information from sources like WordNet, Wikipedia, Wiktionary, as well as Web services is linked and integrated to provide a multi-faceted yet consistent response to user queries. The meanings of words in many different languages are characterized by mapping them to appropriate WordNet sense identifiers and adding multilingual gloss descriptions as well as example sentences. Relationships are derived from WordNet and Wiktionary to allow users to discover semantically related words, etymologically related words, alternative spellings, as well as misspellings. Last but not least, images, audio recordings, and geographical maps extracted from Wikipedia and Wiktionary allow for a multimodal experience.
Author ' s personal copy Ramp preserving Perona – Malik model

The Perona–Malik (PM) model, a classical anisotropic diffusion, can preserve edges while removing the noise. However, the defect of traditional PM model is tending to contain the staircase effect in the processed image. For this reason, a novel ramp preserving Perona–Malik (RPPM) model based on a new edge indicator is presented. In the RPPM model, the diffusion coefficient of the PM model is adaptively determined to introduce following property: isotropic diffusion in flat and ramp regions to prevent the staircase effect and anisotropic diffusion in edge regions to preserve edges. Comparative results on synthetic and real image denoising demonstrate that our model can preserve important structures, such as edges and ramps, while avoiding the speckles and the complex numerical implementation arising from high-order partial differential equations (PDEs). & 2009 Elsevier B.V. All rights reserved.
Extracting, Ranking, and Evaluating Quality Features of Web Services through User Review Sentiment Analysis

Quality of Service (QoS) has become a standard way of evaluating web services and selecting the one that suites user interests the best. Traditional methods adopt a fixed set of QoS parameters and typical ones include response time, fee, and availability. There currently lacks an effective way of identifying quality features that users are actually interested in when choosing a service. Meanwhile, the traditional way of collecting QoS values relies on either public information released by service providers or test results from repeatedly invoking a service. Therefore, the values can be heavily affected by authenticity of the provider offered information or the quality/configuration of the test code/environment. As a result, existing QoS evaluation methods are not applicable to subject features, such as usability and affordability, where the values depend on user personal judgement. In this paper, we propose a novel approach to extracting domain-related QoS features, ranking those features based on their interestingness, evaluating the value of these features through sentiment analysis on user reviews. More specifically, we leverage natural language processing techniques and machine learning approaches to identify top QoS features that users are interested in and simultaneously learn their sentiment orientation towards those features. We model the problem as sentiment classification, where relevant terms in a review are modeled as features that determine whether a review is positive or negative. Logistic regression is used so that the impact of these terms are learned simultaneously when the classifier is learned through a supervised learning process. The nontrivial terms are selected as the candidate QoS featured. A comprehensive experiment has been conducted on a real-world dataset and the result demonstrates the effectiveness of our approach.
MergeGraphs: a web-based system for merging heterogeneous big graphs

The quantity and quality of the information represented by means of graph structures is more and more increasing nowadays. Merging this information assumes a deep relevance in order to identify unknown relationships and properties and conduct different kinds of analysis. However, this task is complicated due to the variety of formats with which graphs are represented and their size. In this paper we propose MergeGraphs, a web based system in which the user can graphically drag and drop heterogeneous graphs and specify transformation and merging operations (union, intersection and join). The obtained execution plan is translated in Pig Latin scripts and executed in a cluster of machines. The use of Google App/Compute Engines allow to develop a scalable distributed approach.
Preferences recovery in address models of product differentiation

Product d i f f e r e n t i a t i o n i s a f e a t u r e of most modern markets. The economics of product d i f f e r e n t i a t i o n has r e l i e d on two major approaches: t h e representa t ive consumer approach and t h e address approach. I t i s argued i n t h e l i t e r a t u r e t h a t address models a r e more appropr ia te f o r studying most r e a l cases of product d i f f e r e n t i a t i o n . Y e t l i t t l e empirical work has been done i n t h i s framework, due pr imar i ly t o t h e absence of preferences recovery techniques f o r address models. The purpose of t h i s t h e s i s i s t o begin t h e development and implementation of preferences recovery techniques f o r address models. I n address models, goods a r e described by po in t s i n a continuous space of a t t r i b u t e s o r c h a r a c t e r i s t i c s . Consumer preferences a r e def ined over a l l p o t e n t i a l products and each consumer has a most p r e f e r r e d product known a s h i s o r her i d e a l address i n t h e p roduc t -a t t r ibu tes space. Aggregate consumer preferences f o r d i v e r s i t y a r e captured by a preferences dens i ty funct ion i n some space of u t i l i t y parameters. Preferences recovery involves t h e es t imat ion of t h e preferences dens i ty function, given aggregate da ta on product a t t r i b u t e s , p r i c e s , and q u a n t i t i e s sold. The bulk of t h e t h e s i s i s on recovering preferences i n t h e space of l o t t e r i e s . L o t t e r i e s a r e chosen because w e need "products" t h a t can be e a s i l y c rea ted i n t h e labora tory t o genera te s u f f i c i e n t data. Given a parametric form f o r preferences from t h e o r i e s of choice under uncer ta in ty , w e c r e a t e a parameter space t h a t descr ibes ind iv idua l preferences f o r l o t t e r i e s . Aggregate preferences a r e
Marginal Causal Consistency in Constraint-based Causal Learning

Maximal Ancestral Graphs (MAGs) are probabilistic graphical models that can model the distribution and causal properties of a set of variables in the presence of latent confounders. They are closed under marginalization. Invariant pair-wise features of a class of Markov equivalent MAGs can be learnt from observational data sets using the FCI algorithm and its variations (such as conservative FCI and order independent FCI). We investigate the consistency of causal features (causal ancestry relations) obtained by FCI in different marginals of a single data set. In principle, the causal relationships identiﬁed by FCI on a data set D measuring a set of variables V should not conﬂict the output of FCI on marginal data sets including only subsets of V . In practice, however, FCI is prone to error propagation, and running FCI in different marginals results in inconsistent causal predictions. We introduce the term of marginal causal consistency to denote the consistency of causal relationships when learning marginal distributions, and investigate the marginal causal consistency of different FCI variations.Results indicate that marginal causal consistency varies for different algorithms, and is also sensitive to network density and marginal size.
SIMD, SMP and MIMD-DM parallel approaches for real-time 2D image stabilization

We present a real-time image stabilization method, based on a 2D motion model and different levels of parallel implementation. This stabilization method is decomposed into three main parts. First, the image matching is determined by a feature-based technique, then the motion between consecutive frames is estimated and filtered to extract the unwanted motion component. This component is finally used to correct (warp) the images, resulting in a stable sequence. To validate our stabilization approach in a real-time on-board system context, the algorithm was implemented and tested over different hardware platforms, allowing a performance evaluation in function of the adopted architecture. In this paper, we present some of the results, concerning the parallel implementation of the algorithm, using the MW ALTIVEC/spl reg/ instructions set, a symmetric multi-processor (SMP) architecture and MIMD-DM architecture.
Modulation of Expression of the ToxR Regulon in Vibrio cholerae by a Member of the Two-Component Family of Response Regulators

ABSTRACT The ToxRS system in Vibrio cholerae plays a central role in the modulation of virulence gene expression in response to environmental stimuli. An integration of multiple signalling inputs mediated by ToxR, -S, and -T controls virulence gene expression leading to cholera toxin (CT) production. Recently, we identified a new virulence locus, varA (virulence associated regulator), in classical V. cholerae O1 that positively controls transcription of tcpA, the major subunit of the toxin-coregulated pilus (TCP) and the production of CT, two key factors in cholera pathogenesis. The varA locus is a homolog ofgacA (originally described for the soil organismPseudomonas fluorescens), which encodes a conserved global regulator belonging to the family of two-component signal transducing molecules. GacA homologs in a number of diverse gram-negative pathogenic bacterial species have been implicated in controlling the production of diverse virulence factors.varA mutants showed reduced levels of tcpAmessage and TcpA protein, lacked visible signs of autoagglutination (a phenotype associated with functional TCP), produced decreased levels of CT, and were attenuated in colonizing infant mice. Transcription ofvarA appears to be independent of ToxR, and overexpression of the regulators tcpPH and toxT from plasmids in the varA mutant restored wild-type levels of CT production and the ability to autoagglutinate. varArepresents an additional modulating factor in the coordinate expression of virulence factors in V. cholerae.
Extending the Scale Invariant Feature Transform Descriptor into the Color Domain

In recent years, Lowe’s Scale Invariant Feature Transform (SIFT) algorithm has become a widely used tool for object recognition. One shortcoming is that it only works on grayscale image intensities. In this paper, we propose extending the SIFT descriptor to also incorporate color information in a novel fashion. The keypoint descriptor proposed in this paper is a superset of the original feature vector and works exactly the same on grayscale or single band images as well as in regions of a color image that are essentially monochromatic. Examples are presented to demonstrate the properties and matching capability of the new descriptor in color image processing.
Individual Differences in Habitual Social Goals and Daily Well–Being: The Role of Age and Relationship Closeness

There is a robust evidence that social approach goals (i.e. approach of positive social outcomes) have positive consequences and social avoidance goals (i.e. avoidance of negative social outcomes) have negative consequences for subjective well–being in young adulthood. Little is known about individual differences in social goals in later life. The current diary study with young (n = 212), middle–aged (n = 232), and older adults (n = 229) tested––and supported––the hypotheses that age (i) differentially predicts the strength of habitual approach and avoidance goals in close and peripheral relationships and (ii) moderates the relation of approach and avoidance goals in peripheral (but not close) relationships and daily outcomes (subjective well–being, subjective health, and satisfaction with social encounters). Older adults compared to younger adults reported higher levels of avoidance goals in peripheral (but not close) relationships. Younger adults who reported high levels of approach goals and older adults who reported high levels of avoidance goals in peripheral relationships experienced the most positive daily outcomes. In addition, social goals moderated some of the associations between (positive and negative) daily interactions and daily outcomes. Results underscore the importance of the closeness of social partners for individual differences in social goals across adulthood. © 2019 European Association of Personality Psychology
Object co-segmentation based on directed graph clustering

In this paper, we develop a new algorithm to segment multiple common objects from a group of images. Our method consists of two aspects: directed graph clustering and prior propagation. The clustering is used to cluster the local regions of the original images and generate the foreground priors from these clusterings. The second step propagates the prior of each class and locates the common objects from the images in terms of foreground map. Finally, we use the foreground map as the unary term of Markov random field segmentation and segment the common objects by graph-cuts algorithm. We test our method on FlickrMFC and ICoseg datasets. The experimental results show that the proposed method can achieve larger accuracy compared with several state-of-arts co-segmentation methods.
The plasticity of social emotions

Social emotions such as empathy or compassion greatly facilitate our interactions with others. Despite the importance of social emotions, scientific studies have only recently revealed functional neural plasticity associated with the training of such emotions. Using the framework of two antagonistic neural systems, the threat and social disconnection system on the one hand, and the reward and social connection system on the other, this article describes how training compassion and empathy can change the functioning of these systems in a targeted manner. Whereas excessive empathic sharing of suffering can increase negative feelings and activations in the insula and anterior cingulate cortex (corresponding to the threat and social disconnection system), compassion training can strengthen positive affect and neural activations in the medial orbitofrontal cortex and striatum (corresponding to the reward and social connection system). These neuroimaging findings are complemented by results from behavioral studies showing that compassion is linked to helping and forgiveness behavior, whereas empathic distress not only decreases helping behavior, but is even associated with increased aggressive behavior. Taken together, these data provide encouraging evidence for the plasticity of adaptive social emotions with wide-ranging implications for basic science and applied settings.
Slit function measurements of an imaging spectrograph using Fourier transform techniques

Knowledge of a spectrograph slit function is necessary to interpret unresolved lines and spectral features in an observed spectrum. In a scanning spectrometer with a single exit slit, the slit function is easily measured by illuminating the entrance slit with a broadband source and scanning the dispersive element. In a fixed grating/or disperser spectrograph, the slit functions have been measured by illuminating the entrance slit with a monochromatic light using a premonochromator or a tunable laser and by varying the wavelength of the incident light. Generally these techniques are very expensive, complex or subject to a poor signal-to-noise ratio so that an accurate measurement is often not possible. Also it would be very laborious and prohibitive to an imaging spectrograph or a multi-object spectrograph that has many sets of entrance and exit slit equivalents. We explore an alternative technique that is manageable for the measurements and where the measurement is not limited by the available signal. In the proposed technique, a Fourier Transform Spectrometer (FTS) is used instead of a pre-monochromator with variable wavelengths in the conventional techniques. This approach can be extended to the visible and ultraviolet (UV) wavelength range and to imaging spectrographs and multi-object spectrograph where multiple entrance slits and multiple exit slit equivalents (detectors) produce numerous different slit functions. In this approach, the advantages of FTS are fully utilized for available signals and the computer-assisted nature of FTS makes the data processing of the measurements manageable.
The Immediate and Late Effects of Thyroid Hormone (Triiodothyronine) on Murine Coagulation Gene Transcription

Thyroid dysfunction is associated with changes in coagulation. The aim of our study was to gain more insight into the role of thyroid hormone in coagulation control. C57Black/6J mice received a low-iodine diet and drinking water supplemented with perchlorate to suppress endogenous triiodothyronine (T3) and thyroxine (T4) production. Under these conditions, the impact of exogenous T3 on plasma coagulation, and hepatic and vessel-wall-associated coagulation gene transcription was studied in a short- (4 hours) and long-term (14 days) setting. Comparing euthyroid conditions (normal mice), with hypothyroidism (conditions of a shortage of thyroid hormone) and those with replacement by incremental doses of T3, dosages of 0 and 0.5 μg T3/mouse/day were selected to study the impact of T3 on coagulation gene transcription. Under these conditions, a single injection of T3 injection increased strongly hepatic transcript levels of the well-characterized T3-responsive genes deiodinase type 1 (Dio1) and Spot14 within 4 hours. This coincided with significantly reduced mRNA levels of Fgg, Serpinc1, Proc, Proz, and Serpin10, and the reduction of the latter three persisted upon daily treatment with T3 for 14 days. Prolonged T3 treatment induced a significant down-regulation in factor (F) 2, F9 and F10 transcript levels, while F11 and F12 levels increased. Activity levels in plasma largely paralleled these mRNA changes. Thbd transcript levels in the lung (vessel-wall-associated coagulation) were significantly up-regulated after a single T3 injection, and persisted upon prolonged T3 exposure. Two-week T3 administration also resulted in increased Vwf and Tfpi mRNA levels, whereas Tf levels decreased. These data showed that T3 has specific effects on coagulation, with Fgg, Serpinc1, Proc, Proz, Serpin10 and Thbd responding rapidly, making these likely direct thyroid hormone receptor targets. F2, F9, F10, F11, F12, Vwf, Tf and Tfpi are late responding genes and probably indirectly modulated by T3.