Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumer Credit: Evidence from Italian Micro Data

In this paper we analyse unique data on credit applications received by the leading provider of consumer credit in Italy (Findomestic). The data set covers a five-year period (1995-1999) during which the consumer credit market rapidly expanded in Italy and a new law (the usury law) came into force that set a limit on interest rates charged to consumers. We compute behavioural changes by controlling for changes in the observable characteristics of the Findomestic clientele and argue that, under suitable identifying assumptions, these changes can be given a structural interpretation. If the usury shock is assumed to have affected credit supply but not credit demand-that is, if the usury law had a differential impact on the supply of various types of credit but a uniform impact on demand-then we can identify and estimate a demand equation. Our key finding is that demand is interest-rate elastic, particularly in the more affluent North. (JEL: D14, E21, G21) Copyright (c) 2005 by the European Economic Association.
The Worse Clustering Performance Analysis

Partitional clustering algorithms are the most widely used in pattern recognition fields. And the output of partitional clustering is sensitive to the initial parameters. Therefore, it is very important to choose the optimal parameter for a specific clustering algorithm. In the past, parameter selection usually is up to the empirically optimal clustering performance. In this paper, we propose a novel approach to parameter selection for partitional clustering based on the stability analysis of dynamical system. The main idea is as follows: any clustering algorithm can not always partition a data set into meaningful subsets, therefore the parameters corresponding to the worse clustering result should not be the optimal, especially for those corresponding to the stable worse clustering output. Such framework is called the worse clustering performance analysis. As its application, we not only present how to do parameter selection for several clustering models, but also reveal that the extreme point of its objective function does not guarantee to be the stable fixed point of this clustering algorithm. From a machine learning point of view, such conclusion means that the learning algorithm maybe not reach its original expectation under some circumstance.
Exposure to Violence and Sexual Risk Among Early Adolescents in Urban Middle Schools

This article examines the relationship between exposure to violence, fear of exposure to violence, and sexual risk among a sample of urban middle school youth. The sample included 911 seventh-grade students who completed self-report surveys. Approximately 20% of the sample reported at least one direct threat or injury with a weapon in the past 3 months, and 52% reported ever seeing someone seriously injured or killed. Experiencing a direct threat was significantly associated with greater intentions to engage in vaginal intercourse and sexual touching behaviors; witnessing violence was significantly associated with sexual touching. Fearing exposure to violence was not associated with increased sexual risk. Most associations did not differ by gender. These data highlight the potential influence of exposure to violence on sexual risk among early adolescents, and underscore the potential of addressing exposure to violence in sexuality education programs and sexual health services for enhanced impact.
Intrusion Detection Quantitative Analysis with Support Vector Regression and Particle Swarm Optimization Algorithm

Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, a new method based on support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented and used for pattern analysis of intrusion detection in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. We use rough set to reduce dimension. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.
Vector-field-based deformable models for radiation dosimetry

Accurate age-specific models of pediatric patients for radiation dosimetry purposes are not presently available. In a world-first effort to build such models, we are currently developing a scheme that combines deformable models with a priori anatomical knowledge and minimal human supervision. However, the outcome of applying a deformable model is often significantly dependent on its initialization. This is an obstacle to accurate and robust automatic or near-automatic segmentation. In this paper, we propose a novel approach to reducing this sensitivity to initialization by deriving a vector field from topographic and Euclidean distance transforms. It is aimed to extend the influence of target gradients over the entire image in a consistent fashion, while enabling the model to ignore irrelevant gradients. Initiated by one or more seeds, the vector field is computed using an efficient numerical method, and has so far been integrated into a parametric (snake) model and a geodesic active contour level set model. Preliminary experiments targeting different organs have shown that this is a highly promising approach. We believe that this approach will satisfy the need for a high degree of automation in using deformable models for our dosimetry work.
Time-Critical Cooperative Control of Multiple Autonomous Vehicles: Robust Distributed Strategies for Path-Following Control and Time-Coordination over Dynamic Communications Networks

Worldwide, there has been growing interest in the use of autonomous vehicles to execute missions of increasing complexity without constant supervision of human operators. A key enabling element for the execution of such missions is the availability of advanced systems for motion control of autonomous vehicles. Usually, the problems of motion control for a single autonomous vehicle are roughly classified into three groups:.
Is Thermal Emission in Gamma-Ray Bursts Ubiquitous?

The prompt emission of gamma-ray bursts has yet defied any simple explanation, despite the presence of rich observational material and great theoretical efforts. Here we show that all the types of spectral evolution and spectral shapes that have been observed can indeed be described with one and the same model, namely, a hybrid model with a thermal and a nonthermal component. We further show that the thermal component is the key emission process determining the spectral evolution. Even though bursts appear to have a variety of sometimes complex spectral evolutions, the behaviors of the two separate components are remarkably similar for all bursts, with the temperature describing a broken power law in time. The nonthermal component is consistent with emission from a population of fast-cooling electrons emitting optically thin synchrotron emission or nonthermal Compton radiation. This indicates that these behaviors are the fundamental and characteristic ones for gamma-ray bursts.
Learning-based movie summarization via role-community analysis and feature fusion

Movie summarization aims at condensing a full-length movie to a significantly shortened version that still preserves the movie's major semantic content. In this paper, we propose a learning-based movie summarization framework via role-community social network analysis and feature fusion. In our framework, scene-based movie summarization is formulated as a 0-1 knapsack problem, where the scene attention value for each significant scene is calculated as its “value” and the length of this scene is used as its “cost. ” To identify the significance of each scene, we propose a learning-based approach to fuse the information derived from visual saliency (based on low-level features and high-level cognitive process for an input movie), high-level semantic analysis (based on the global and local social networks constructed from the movie), and user preferences. Our evaluation results show that in most test cases, the proposed method subjectively outperforms attention-based and role-based summarization methods and our previous role-community-based method in terms of semantic content preservation.
Analysis of Bandwidth Allocation Algorithms for Bluetooth Wireless Personal Area Networks

A major issue in the design and operation of ad hoc networks is sharing the common spectrum among links in the same geographic area. Bandwidth allocation, to opti- mize the performance of networks in which each station can converse with at most a single neighbor at a time, has been recently studied in the context of Bluetooth networks. There, centralized and distributed, capacity assignment heuristics were developed, with applicabil- ity to a variety of ad hoc networks. Yet, no guarantees on the performance of these heuristics have been provided. In this paper we present our analytic results regarding these heuristics. Speciflcally, we show that they are fl-approximation (fl < 2) algorithms. Moreover, we show that even though the distributed and centralized algorithms allocate capacity in a difierent manner, both algorithms converge to the same results. Finally, we present numerical results that demonstrate the performance of the algorithms.
Using a Machine Learning Approach for Building Natural Language Interfaces for Databases: Application of Advanced Techniques in Inductive Logic Programming

Building a natural language interface for a database has been an interesting task since the 70‟s, which often requires creating a semantic parser. A study on using an advanced inductive logic programming (ILP) approach for semantic parser induction that combines different ILP learners to learn rules for ambiguity resolution is presented. Accuracy of the resulting induced semantic parser can be significantly improved due to the learning of more descriptive rules than those of each individual ILP learner. The task of learning semantic parsers in two different real world domains was attempted and results demonstrated that such an approach is promising.
A Novel Algorithm for Multichannel Deconvolutive based on ??-Divergence

We introduce a novel Algorithm for underdetermined convolutive mixture of source signals. Where the convolution is routinely approximated in the short-time Fourier transform (STFT) domain as linear instantaneous mixing in each frequency band. Each source STFT is given a model inspired from nonnegative matrix factorization (NMF) with the divergence, this divergence is a family of cost functions parameterized by a two tuning parameters ( and ), and smoothly connect the fundamental Alpha-, Betaand Gammadivergences. The proposed family of -multiplicative NMF algorithms is shown to improve robustness separation with respect to noise and outliers. Our decomposition algorithm is applied to stereo audio source separation in various settings, covering blind and supervised separation, music and speech sources, synthetic instantaneous and convolutive mixtures. General Terms Algorithms for blind signal separation .
Financial Institutions Center Pricing the Risks of Default

This paper models default risk as composed of arrival and magnitude risks. In our model the two default components are explicitly priced as if they were traded in the futures market and the spot price of risky debt is derived as a consequence. We develop estimation strategies to evaluate the magnitude risks which are then employed to construct implicit prices of pure arrival risk contingent securities. The latter prices are used to estimate the structure of arrival risks. The models are estimated on monthly data for rates on certificates of deposit offered by institutions in the Savings and Loan Industry, during the 1987-1991 period. Empirical results support market expectations of lower likelihoods of default after 1989. PRICING THE RISKS OF DEFAULT The event of default has two underlying risk components, one associated with the timing of the event and the other with its magnitude. Ex-ante, default occurs at an uncertain future time when there is some, as yet unknown, reduction in the value of creditor claims. The event may be triggered by many sources that include the occurrence of large unsustainable operational losses and the development of superior alternative technologies that question the economic viability of the firm and its ability to continue to generate earnings. Once the event is triggered the magnitude depends on the relative value of assets in place to the value of creditor claims. A description of the risks of default, typically requires a description of these two risks. The evolution over time of the likelihood that default occurs, is termed the arrival rate of default. The conditional density of the discount on creditor claims in the event of default, is termed the magnitude risk of default. Market prices and models of such prices, value both these risks. In addition to these timing and magnitude risks imbedded in the prices of defaultable debt claims, the spot prices for risky debt incorporate interest rate risk as well. The current literature, by focusing attention on modeling the spot price of risky debt implicitly prices all three risks simultaneously. This paper follows a new approach of focusing attention on the timing and magnitude risks separately. We avoid term structure modeling by developing a model for the arbitrage free complete markets forward/ futures price of a defaultable claim. The timing risk is modeled in the current literature collectively by defining the default event, typically as occurring when the firm value reaches a threshold. Beginning with Merton (1974, 1977) and followed by Black and Cox (1976), Lee (1981), Ho and Singer (1982), Pitts and Selby (1983), Johnson and Stulz (1987), Chance (1990), and Cooper and
Data Analytics Using Ontologies of Management Theories: Towards Implementing "From Theory to Practice" (Application Paper)

We explore how computational ontologies can be impactful vis-à-vis the developing discipline of "data science. " We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially actualize the academic cliché, "From Theory to Practice," and be applicable to the burgeoning domain of data analytics is novel and exciting.
AM 1934-563: a giant spiral polar-ring galaxy in a triplet

We have observed the emission-line kinematics and photometry of a southern triplet of galaxies. The triplet contains a giant spiral galaxy AM 1934-563 whose optical structure resembles a polar-ring galaxy: a distorted spiral disk, seen almost edge-on, and a faint large-scale (45 kpc in diameter) warped structure, inclined by 60°–70° with respect to the disk major axis. The triplet shows a relatively small velocity dispersion (69 km s -1 ) and a large crossing time (0.17 in units of the Hubble time). The disk of AM 1934-563 demonstrates optical colors typical of early-type spirals, a strong radial color gradient, and almost exponential surface brightness distribution with an exponential scale-length value of 3.1 kpc ( R passband). The galaxy shows a maximum rotation velocity of about 200 km s -1 and it lies close to the Tully-Fisher relation for spiral galaxies. The suspected polar ring is faint ($\mu(B) \geq 24$) and strongly warped. Its total luminosity comprises 10-15% of the total luminosity of AM 1934-563. We model this system using numerical simulations, and study its possible formation mechanisms. We find that the most robust model that reproduces the observed characteristics of the ring and the host galaxy is the tidal transfer of mass from a massive gas-rich donor galaxy to the polar ring. The physical properties of the triplet of galaxies are in agreement with this scenario.
EasyZoom: Zoom-in-Context Views for Exploring Large Collections of Images

Image browsing and searching are some of the most common tasks in daily computer use. Zooming techniques are important for image searching and browsing in a large collection of thumbnail images in a single screen. In this paper we investigate the design and usability of different zoom-in-context views for image browsing and searching. We present two new zoom-in-context views, sliding and expanding views, that can help users explore a large collection of images more efficiently and enjoyably. In the sliding view the zoomed image moves its neighbors away vertically and horizontally. In the expanding view, the nearby images are pushed away in all directions, and this method uses a Voronoi diagram to compute the positions of the neighbors. We also present the results of a user study that compared the usability of the two zoom-in-context views and an overlapping, non-context zoom in the tasks of searching to match an image or a text description, and the task of brochure making. Although the task completion times were not significantly different, users expressed a preference for the zoom-in-context methods over the standard non-context zoom for text-matching image search and for image browsing tasks.
Expertise Search in a Time-Varying Social Network

This paper is concerned with the problem of expertise search in a time-varying social network. Previous research work on expertise search, aiming at finding the most important/authoritative objects, usually ignores an important factor - temporal information, which reveals a huge amount of information contained in large document collections. Many real-world applications, for example reviewers matching for academic papers and hot-topic finding from newsgroup posts need to consider the evolution of information over times. In this work, we propose a unified model by integrating the temporal information into a random walk model. Specifically, the time information is modelled in a forward-and-backward propagation process in the random walk. The proposed model has been applied to expertise search in an academic social network. Experimental results show that the proposed approach can significantly outperform the baseline methods of using the language model (2.0% in terms of MAP) and the traditional PageRank algorithm (17.2% in terms of MAP).
A Low-Power 136-GHz SiGe Total Power Radiometer With NETD of 0.25 K

This paper presents a low-noise SiGe radiometer at 136 GHz developed in an IBM 90-nm SiGe BiCMOS technology. The radiometer consists of a three-stage cascode low-noise amplifier with a gain of 36 dB, and a differential output square-law detector, all on a single chip. The detector results in responsivity of 11 kV/W and a noise equivalent power (NEP) of 0.6 pW/Hz1/2 at D-band frequencies. The radiometer chip consumes 45 mW and results in a minimum NEP of 1.4 fW/Hz1/2 with a peak responsivity of 52 MV/W at 136 GHz. The single-chip radiometer is suitable for high-resolution imaging systems having a noise bandwidth > 10 GHz and a low 1/f corner frequency . For an integration time of 3.125 mS (τ = 3.125 mS), the temperature resolution [noise equivalent temperature difference (NETD)] is determined to be 0.25 K using several different independent methods, and is the lowest NETD demonstrated in silicon technologies at D-band frequencies. This state-of-the-art performance is comparable to the best III-V imaging systems and proves that the advanced SiGe technology is a reliable option for imaging and radiometry applications.
A Flexible Containment Mechanism for Executing Untrusted Code

A widely used technique for securing computer systems is to execute programs inside protection domains that enforce established security policies. These containers, often referred to as sandboxes, come in a variety of forms. Although current sandboxing techniques have individual strengths, they also have limitations that reduce the scope of their applicability. In this paper, we give a detailed analysis of the options available to designers of sandboxing mechanisms. As we discuss the tradeoffs of various design choices, we present a sandboxing facility that combines the strengths of a wide variety of design alternatives. Our design provides a set of simple yet powerful primitives that serve as a flexible, general-purpose framework for confining untrusted programs. As we present our work, we compare and contrast it with the work of others and give preliminary re-
The Impact of an Erasmus Placement in Students’ Perception of their Intercultural Communicative Competence

Abstract Pre- and post-questionnaires answered by UK Nottingham Trent University (NTU) and Spain’s University of Salamanca (USAL) students on their placement abroad support a comparative analysis of its impact on their intercultural communicative competence, comprising the awareness, attitude, skills and knowledge dimensions. NTU students started their placements with better results in them all. Yet, while both groups completed their stay with a similarly increased awareness, the knowledge and skills of the USAL group matched the results of NTU students and, whereas the scores of the attitude dimension towards the host country of NTU students did not increase significantly, those of USAL students decreased.
An Explicit Feedback System for Preposition Errors based on Wikipedia Revisions

This paper presents a proof-of-concept tool for providing automated explicit feedback to language learners based on data mined from Wikipedia revisions. The tool takes a sentence with a grammatical error as input and displays a ranked list of corrections for that error along with evidence to support each correction choice. We use lexical and part-of-speech contexts, as well as query expansion with a thesaurus to automatically match the error with evidence from the Wikipedia revisions. We demonstrate that the tool works well for the task of preposition selection errors, evaluating against a publicly available corpus.
Efficient Language Independent Generation from Lexical Conceptual Structures

Abstract : This paper describes a system for generating natural-language sentences from an interlingual representation, Lexical Conceptual Structure (LCS). The system has been developed as part of a Chinese-English Machine Translation system; however, it is designed to be used for many other MT language pairs and Natural Language applications. The contributions of this work include: (1) Development of a language-independent generation system that maximizes efficiency through the use of a hybrid rule-based/statistical module; (2) Enhancements to an interlingual representation and associated algorithms for interpretation of multiply ambiguous input sentences; (3) Development of an efficient reusable language-independent linearization module with a grammar description language that can be used with other systems; (4) Improvements to an earlier algorithm for hierarchically mapping thematic roles to surface positions; (5) Development of a diagnostic tool for lexicon coverage and correctness and use of the tool for verification of English, Spanish, and Chinese lexicons. An evaluation of translation quality shows comparable performance with commercial translation system. The generation system can also be straightforwardly extended to their languages and this is demonstrated and evaluated for Spanish.