Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Personalized Search using Social Data

Search personalization that considers the social dimension of the web has attracted a significant volume of research in recent years. A user profile is usually needed to represent a user’s interests in order to tailor future searches. Previous research has typically constructed a profile solely from a user’s usage information. When the user has only limited activities in the system, the effect of the user profile on search is also constrained. This research addresses the setting where a user has only a limited amount of usage information. We build enhanced user profiles from a set of annotations and resources that users have marked, together with an external knowledge base constructed according to usage histories. We present two probabilistic latent topic models to simultaneously incorporate social annotations, documents and the external knowledge base. Our web search strategy is achieved using personalized social query expansion. We introduce a topical query expansion model to enhance the search by utilizing individual user profiles. The proposed approaches have been intensively evaluated on a large public social annotation dataset. Results show that our models significantly outper-formed existing personalized query expansion methods which use user profiles solely built from past usage information in personalized search.
From Bullets to Ballots: Using the People as Arbitrators to Settle Civil Wars

Previous research has not specified the circumstances under which foreign aid may increase the probability of armed conflict. The purpose of this dissertation is to address this gap by employing a theoretical framework in which foreign aid produces incentives for a rent-seeking scramble among elites. A set of conditions affecting the likelihood of armed conflict are identified and tested on global data in a series of statistical analyses. Paper I argues and finds that foreign aid increases the probability of armed conflict in states where there are few constraints on executive power, allowing for a scramble for rents. Paper II proposes and finds a threshold effect of aid, such that the likelihood of armed conflict increases only when aid has reached a certain level. Paper III suggests and demonstrates that sudden negative changes in aid flows enhance the risk of armed conflict as well as coup attempts, as aid shortfalls accelerate distributional conflict over aid rents. Paper IV claims and shows that civil wars are less likely to be terminated by settlement in the form of elections when conflict parties are dependent on rents. In sum, this dissertation contributes by theoretically specifying and empirically identifying conditions under which foreign aid increases the probability of armed conflict.
t \bar{t} Cross-Section and Forward-Backward Asymmetry at CLIC

A top-quark reconstruction method for CLIC is described. The reconstructed events are used to measure the e+ e- -> t \bar{t} cross-section as well as the forward-backward asymmetry. The results suggest that the relative measurement accuracy for the cross-section will be 1.4 % and for the asymmetry 4.2 % for 1 ab^{-1} of data.
Public Debt as Private Wealth: Some Equilibrium Considerations

Government bonds are interest-bearing assets. Increasing public debt increases wealth, income and consumption demand. The smaller government expenditure is, the larger consumption demand must be in equilibrium, and the larger must be public debt. Conversely, lower public debt implies higher government spending and taxation. Public debt plays, thus, an important role in establishing equilibrium. It distributes output between consumers and government. In case of insufficient demand, a larger public debt entails higher private consumption and less public spending. If upper bounds on public debt are introduced (as in the Maastricht treaty), such constraints place lower bounds on taxation and public spending and may rule out macroeconomic equilibrium. As an aside, a minor flaw in Domar's (American Economic Review, 34 (4), pp. 798-827) classical analysis is corrected. Copyright © 2006 The Author; Journal compilation © 2006 Blackwell Publishing Ltd.
Evidence-Based Parenting Interventions to Promote Secure Attachment

Various interventions are used in clinical practice to address insecure or disorganized attachment patterns and attachment disorders. The most common of these are parenting interventions, but not all have a robust empirical evidence base. We undertook a systematic review of randomized trials comparing a parenting intervention with a control, where these used a validated attachment instrument, in order to evaluate the clinical and cost-effectiveness of interventions aiming to improve attachment in children with severe attachment problems (mean age <13 years). This article aims to inform clinicians about the parenting interventions included in our systematic review that were clinically effective in promoting secure attachment. For completeness, we also briefly discuss other interventions without randomized controlled trial evidence, identified in Patient Public Involvement workshops and expert groups at the point our review was completed as being used or recommended. We outline the key implications of our findings for clinical practice and future research.
Farm Growth and Estate Transfer in an Uncertain Environment

Farm firm growth has been an important topic for research and discussion since the 1950s. Incentives for farm growth have been and continue to be substantial. An agricultural economy characterized by technological improvement, decreasing costs, competition, and an inelastic demand for farm products leaves little alternative but growth for a commercial farm. Farm growth is stimulated by the need to achieve size economies that arises partly from new technology and partly from large investments in machinery and equipment. Growth is encouraged by the improving managerial ability of the operator as he matures and gains experience. Increased family living needs and the desire to overcome the adverse effects of inflation on purchasing power spur interest in improving the farm's earning potential. In addition, the operator's goals may include size aspirations to satisfy the desire for a large operation or to support the family of a son or daughter attempting to become established in farming. Because the pressures for growth, both internal and external to the firm, are long-run phenomena in agriculture, continued study and evaluation of the process of entry, firm growth, and exit coordination are essential.
A Recoverability-Oriented Analysis for Operations on Cloud Applications

Consumer-initiated sporadic operations on cloud applications, such as deployment, upgrade and reconfiguration, may fail because of the inherent uncertainty of operating in a cloud environment. For example, if a VM is not able to start, operations dependent on that VM will fail. In this paper we propose an approach for analyzing sporadic operations on cloud applications to facilitate recovery. We do this by first creating a process model of the sporadic cloud operation. The Process may not be suitable for recovery operations or the application's deployment architecture since an individual step may be at the wrong level of granularity or a failure may unnecessarily affect multiple steps. We then propose a set of process division criteria for revising an operation process to support recovery. This set of criteria contains four aspects: 1) Atomicity to support the imposition of transactions on portions of the process, 2) Idempotence to allow for re-execution of a failed portion, 3) Granularity to allow reuse of existing steps, 4) Recovery Actions Identifiable to allow proper recovery actions to be taken to recover from the errors occurring during the operation. We demonstrate the feasibility of imposing these criteria by using the rolling upgrade operation in Asgard - a popular cloud management tool for AWS EC2.
Mean-Variance Portfolio Selection with Random Parameters in a Complete Market

This paper concerns the continuous-time, mean-variance portfolio selection problem in a complete market with random interest rate, appreciation rates, and volatility coef.cients. The problem is tackled using the results of stochastic linear-quadratic (LQ) optimal control and backward stochastic differential equations (BSDEs), two theories that have been extensively studied and developed in recent years. Specifically, the mean-variance problem is formulated as a linearly constrained stochastic LQ control problem. Solvability of this LQ problem is reduced, in turn, to proving global solvability of a stochastic Riccati equation. The proof of existence and uniqueness of this Riccati equation, which is a fully nonlinear and singular BSDE with random coefficients, is interesting in its own right and relies heavily on the structural properties of the equation. Efficient investment strategies as well as the mean-variance efficient frontier are then analytically derived in terms of the solution of this equation. In particular, it is demonstrated that the efficient frontier in the mean-standard deviation diagram is still a straight line or, equivalently, risk-free investment is still possible, even when the interest rate is random. Finally, a version of the Mutual Fund Theorem is presented.
No longer 'out of sight, out of mind': intellectual capital approach in AssetEconomics Inc. and Accenture LLP

Purpose – The purpose of this paper is to describe the logic and processes for identifying, measuring and managing intellectual capital resources along side traditional economic resources to achieve sustainable outcomes valued by investors.Design/methodology/approach – The approach is founded on classical finance theory and draws on and multi‐attribute value theory, system dynamics and the strands of thought known as intellectual capital in order to produce a grounded framework that both produces reliable results as well as achieves acceptance in the financial community.Findings – The findings from this approach, the FVMT Methodology, provide a comprehensive management framework that is agnostic as to the form of resources being utilized and the activities that are involved in the transformation of one resource form into another on the way to achieving sustainable outcomes valued by investors.Research limitations/implications – Whilst the approach has proven to work well the limitations are that it requir.
Research on Network Intrusion Detection System Based on Improved K-means Clustering Algorithm

With the development of computer technology, network security has become an important issue of concern. In view of the growing number of network security threats and the current intrusion detection system development, this paper gives a new model of anomaly intrusion detection based on clustering algorithm. Because of the k-means algorithm’s shortcomings about dependence and complexity, the paper puts forward an improved clustering algorithm through studying on the traditional means clustering algorithm. The new algorithm learns the strong points from the k-medoids and improved relations trilateral triangle theorem. The experiments proved that the new algorithm could improve accuracy of data classification and detection efficiency significantly. The results show that this algorithm achieves the desired objectives with a high detection rate and high efficiency.
Age- and Sex-Dependence of Dopamine Release and Capacity for Recovery Identified in the Dorsal Striatum of C57/Bl6J Mice

The dorsal striatum is the main input structure of the basal ganglia and the major target area of dopaminergic projections originating in the substantia nigra pars compacta. Heavily involved in the regulation of voluntary movement and habit formation, this structure is of strong importance in Parkinson's disease, obsessive-compulsive disorder, Tourette's syndrome and addiction. The C57/Bl6J mouse strain, the most commonly used strain in preclinical research today, is frequently used as a model organism for analysis of dopaminergic parameters implicated in human pathophysiology. Several components of the dopamine system have been shown to vary with age and sex, however knowledge of the contribution of these factors for dopamine release kinetics in the C57/Bl6J mouse strain is lacking. In the present study, we used an intracranial KCl-stimulation challenge paradigm to provoke release from dopaminergic terminals in the dorsal striatum of anaesthetized C57/Bl6J mice. By high-speed in vivo chronoamperometric recordings, we analyzed DA release parameters in male and female mice of two different ages. Our experiments demonstrate elevated DA amplitudes in adult compared to young mice of both sexes and higher DA amplitudes in females compared to males at both ages. Adult mice exhibited higher recovery capabilities after repeated stimulation than did young mice and also showed a lower variability in the kinetic parameters trise and t80 between stimulations. These results identified age- and sex- dimorphisms in DA release parameters and point to the importance of taking these dimorphisms into account when utilizing the C57/Bl6J mouse strain as model for neurological and neuropsychiatric disorders.
Formal specification of the user interface by using parametric context-free grammars ∗

Despite the significant progress and the emergence of proficient tools that have been witnessed recently, the development of user interfaces remains a hard task in the software development process. The use of formal methods for the specification of user interfaces has been proposed as an aid in this task and various formal models have been considered. Grammars were one of the earliest models that were proposed. Several drawbacks hinder the application of ordinary context-free grammars in practice and have led to the adoption of more complex variations. A relatively simple family of grammars, termed “parametric contextfree grammars”, is proposed in this paper for the formal specification of the user interface, in comparison to ordinary context-free grammars. The proposed grammars can describe interface languages with context-sensitive features, which are typical in user interfaces. Their main advantages lie in their simplicity and ability to produce shorter and more understandable specifications.
RNA helicases

Similar to proteins, RNA molecules must fold into the correct conformation and associate with protein complexes in order to be functional within a cell. RNA helicases rearrange RNA secondary structure and RNA-protein interactions in an ATP-dependent reaction, performing crucial functions in all aspects of RNA metabolism. In prokaryotes, RNA helicase activity is associated with roles in housekeeping functions including RNA turnover, ribosome biogenesis, translation and small RNA metabolism. In addition, RNA helicase expression and/or activity are frequently altered during cellular response to abiotic stress, implying they perform defined roles during cellular adaptation to changes in the growth environment. Specifically, RNA helicases contribute to the formation of cold-adapted ribosomes and RNA degradosomes, implying a role in alleviation of RNA secondary structure stabilization at low temperature. A common emerging theme involves RNA helicases acting as scaffolds for protein-protein interaction and functioning as molecular clamps, holding RNA-protein complexes in specific conformations. This review highlights recent advances in DEAD-box RNA helicase association with cellular response to abiotic stress in prokaryotes.
Performance evaluation of monocular predictive display

In teleoperation systems, operator performance is negatively affected by time-delayed visual feedback. Predictive display (PD) compensates for delays by providing synthesized visual feedback. While most existing PD methods rely on a priori models (e.g., from laser range finding or stereo vision), recent work on monocular SLAM and SFM makes it possible to acquire PD models in single camera applications. In this work, we evaluate operator performance of PD visual feedback based on a coarse 3D model. We report the experimental results of 12 human tele-operators each performing 96 visual alignment tasks with a 300ms delay. Four operating modes are considered: delayed video (no PD), video-based PD using a stabilizing plane (homography), 3D model-based PD, and no delay (ground truth). The results indicate that vision-based PD (both plane and 3D model-based) is significantly better than delayed video. It reduced task completion time 40% and is nearly as good as the no delay condition. PD based on a sparse a 3D model was somewhat better than the simpler plane based method.
Multi-Dimensional Sentiment Analysis with Learned Representations

Treating sentiment analysis as a classification problem has proven extremely useful, but it misses the blended, continuous nature of sentiment expression in natural language. Using data from the Experience Project, we study texts as distributions over sentiment categories. Analysis of the document collection shows the texts contain blended sentiment information substantially different from a categorization view of sentiment. We introduce a statistical vector-space model that learns from distributions over emotive categories, in addition to capturing basic semantic information in an unsupervised fashion. Our model outperforms several baselines in predicting sentiment distributions given only the text of a document.
Fast detection of moving objects in complex scenarios

More than one third of all traffic accidents with injuries occur in urban areas, especially at intersections. A suitable driver assistance system for such complex situations requires the understanding of the scene, in particular a reliable detection of other moving traffic participants. This contribution shows how a robust and fast detection of relevant moving objects is obtained by a smart combination of stereo vision and motion analysis. This approach, called 6D Vision, estimates location and motion of pixels simultaneously which enables the detection of moving objects on a pixel level. Using a Kalman filter attached to each tracked pixel, the algorithm propagates the current interpretation to the next image. In addition, a Kalman filter based ego-motion compensation is described that takes advantage of the 6D information. This precise information enables us to discriminate between static and moving objects exactly and to obtain a better prediction. This speeds up tracking and a real-time implementation is achieved. Examples of critical situations in urban areas exhibit the potential of the 6D Vision concept which can also be extended to robotics applications.
Expression of a gap junction protein, connexin43, in a large panel of human gliomas: new insights

Precise diagnosis of low and high grades of brain tumors permits determining therapeutical strategies. So far, diagnosis and prognosis of gliomas were based on histological and genetic criteria which need being completed by a panel of molecular markers. Highly distributed in brain, gap junction proteins, connexins, could be considered as markers of glioma progression as previous studies indicated that expression of a connexin type, connexin43 (Cx43), is inversely correlated to tumor grading. However, this assumption was weakened by the low number of glioma samples used. Taking advantage of tissue microarray technique, we pursued this analysis by studying in situ expression of Cx43 on 85 samples (37 grade IV, 18 grade III, 24 grade II, and 6 grades II to III). Our analysis confirmed the global diminution of Cx43 expression in glioblastomas that was observed in previous studies. However, this analysis brought new insights such as the following ones. First, the high number of samples permitted to show that more than 60% of glioblastomas still express Cx43. Second, no gradual decrease in Cx43 expression was observed between grades II and III, but Cx43 appeared to be a marker distinguishing oligodendrocytic and astrocytic grade III tumors. Third, independently from tumor grade, a Cx43 nuclear staining was detected in areas where leukocytes are present. In conclusion, our study emphasizes the importance of in situ immunohistochemical approaches by giving more precise insights in the subcellular localization of Cx43. It also emphasizes the necessity to carry out such analysis on a wide range of samples to circumvent the high glioma heterogeneity.
MCL with sensor fusion based on a weighting mechanism versus a particle generation approach

The combined action of several sensing systems, so that they are able to compensate the technical flaws of each other, is common in robotics. Monte Carlo Localization (MCL) is a popular technique used to estimate the pose of a mobile robot, which allows the fusion of heterogeneous sensor data. Several sensor fusion schemes have been proposed which include sensors like GPS to improve the performance of this algorithm. In this paper, an Adaptive MCL algorithm is used to combine data from wheel odometry, an inertial measurement unit, a global positioning system and laser scanning. A particle weighting model which integrates GPS measurements is proposed, and its performance is compared with a particle generation approach. Experiments were conducted on a real robotic car within an urban environment.
Multispectral and panchromatic data fusion assessment without reference

This paper introduces a novel approach for evaluating the quality of pansharpened multispectral (MS) imagery without resorting to reference originals. Hence, evaluations are feasible at the highest spatial resolution of the panchromatic (PAN) sensor. Wang and Bovik’s image quality index (QI) provides a statistical similarity measurement between two monochrome images. The QI values between any couple of MS bands are calculated before and after fusion and used to define a measurement of spectral distortion. Analogously, QI values between each MS band and the PAN image are calculated before and after fusion to yield a measurement of spatial distortion. The rationale is that such QI values should be unchanged after fusion, i.e., when the spectral information is translated from the coarse scale of the MS data to the fine scale of the PAN image. Experimental results, carried out on very high-resolution Ikonos data and simulated Pleiades data, demonstrate that the results provided by the proposed approach are consistent and in trend with analysis performed on spatially degraded data. However, the proposed method requires no reference originals and is therefore usable in all practical cases.
PRICING INFLATION-INDEXED OPTIONS WITH STOCHASTIC VOLATILITY

In order to recover smile-consistent prices for inflation-indexed caps and floors, we propose a stochastic volatility model for forward consumer price indices, with volatility dynamics as in Heston (1993). Closed-form formulas for inflation-indexed caplets and floorlets based on the Carr and Madan (1998) Fourier transform approach are then derived. An example of calibration to market data and numerical details concerning our pricing procedure are finally given.
Mining Multi-word Named Entity Equivalents from Comparable Corpora

Named entity (NE) equivalents are useful in many multilingual tasks including MT, transliteration, cross-language IR, etc. Recently, several works have addressed the problem of mining NE equivalents from comparable corpora. These methods usually focus only on single-word NE equivalents whereas, in practice, most NEs are multi-word. In this work, we present a generative model for extracting equivalents of multi-word NEs (MWNEs) from a comparable corpus, given a NE tagger in only one of the languages. We show that our method is highly effective on three language pairs, and provide a detailed error analysis for one of them.