Miracle-dz/UniFAR-FTUs huggingface dataset:
The Characteristics and Evolution of Credit Constraints in Argentina

This paper studies the determinants of credit to firms in Argentina. We find that the cost of credit in the margin (measured by authorized overdrafts) is smaller for firms with large assets, that offer collateral, with a good credit history and with close relationships to the marginal financial institution. Parallel to this, we find that the availability of credit lines in the main bank (measured by the unused credit lines as a proportion of total financial liabilities) depends positively on the assets of the firms, on its collateral, on a good credit history, and on close relationships with the main financial institution. This measure of availability of credit is less ambiguous than traditional measures like leverage, that are affected in opposite directions by demand and supply factors. The Argentine financial system seems to have substantially reduced the degree of asymmetric information through its public credit bureau system. JEL D82, E44, G14, G20 ∗ This paper was prepared for the Inter-American Development Bank Research Network Project ‘Determinants and Consequences of Financial Constraints Facing Firms in Latin America and the Caribbean’. ∗∗ The viewpoints of Javier Bolzico, Alejandro Henke and José Rutman do not necessarily represent the position of the BCRA. Carlos Rodríguez, Pablo Druck and Jorge Streb are from UCEMA. Walter Sosa Escudero is an Invited Reseacher from Universidad Nacional de La Plata (UNLP). ∗∗∗ Miguel Crivelli and Julián Cristiá gave us superb research assistance. We are grateful to Karina Manzaraz for her great work in building the database. Silvina Rodríguez and Marta Cao conducted an inquiry on how financial institutions reported interest rates to the BCRA. Liliana Castiñeira, Veronica Balzarotti, and other members of the Superintendencia de Entidades Financieras of the BCRA helped to interpret the database. We appreciate the suggestions made by José Fanelli, Arturo Galindo, Fabio Schiantarelli, Sergio Schmukler, Eduardo Siandra and other participants at seminars at the IADB and UCEMA, and at the XVI Jornadas Anuales de Economía of the Banco Central del Uruguay.
Speaker identification by K-nearest neighbors: Application of PCA and LDA prior to KNN

This article presents the task of speaker identification in a closed group. It discusses main steps of the identification process ranging from the proper speech features to the classification methods and statistical signal processing. However, its main focus is on tuning the final system using KNN classification method by setting up the number of neighbors, and reducing the feature vector dimension by PCA and LDA not only to speed up but possibly improve the overall performance. By selecting eligible number of neighbors a 6% improvement in the recognition was reached. Moreover, application of both PCA and LDA reduced the feature vector dimension by more than 50% while slightly increasing the recognition accuracy.
Interorganizational-trust ; the choice of make , buy , or ally ; and the performance of interorganizational relationships in the us auto industry

This paper investigates the implications of interorganizational-trust for the performance of exchange relationships and considers the role of varying antecedents of trust on this performance. It develops a theory of the relationship between exchange performance and two distinct sources of interorganizational-trust—trust arising out of past interactions or the institutional environment that existed prior to the focal exchange, which we call exogenous trust, and trust that is intrinsic to the choice of governance, which we call endogenous trust. The central focus of this paper is to consider in detail the role that exogenous interorganizational-trust plays in both directly enhancing exchange performance and indirectly enhancing exchange performance by facilitating the use of less costly modes of governance. Drawing on a sample of 222 sourcing arrangements for components from two assemblers in the automobile industry, we evaluate our hypotheses using a novel three-stage switching regression model. We find broad support for our theory that exogenous interorganizational-trust enhances performance both directly and indirectly through governance choice.
Tailoring complexity metrics for simulink models

The size and complexity of Simulink models is constantly increasing, just as the systems which they represent. Therefore, it is beneficial to control them already at the design phase. In this paper we establish a set of complexity metrics for Simulink models to capture diverse aspects of complexity by proposing new and redefining existing metrics. To evaluate the applicability of our metrics, we compare them with the closed-source metric proposed by Mathworks. Moreover, through a case study from the automotive domain, we relate such metrics to quality attributes as determined by domain experts, and correlate them to known faults. Preliminary assessment suggests that complexity is closely related to analysability, understandability, and testability.
Design of a New Kind of LDPC Decoder

The properties of the LDPC (Low-Density Parity-Check) error-correcting codes are bearing down on Shannon limit, but it also needs to solve the problem of reducing the complexity of decoding as much as possible in practical applications. A new type of (1008, 3, 6) rules LDPC decoder was introduced in this paper. It adopted the Min-Sum-Plus-Correction-Factor decoding algorithm and part parallel structure, which not only reduced the complexity of the hardware realization, but also improved the decoding speed. The maximum decoding rate could achieve 128Mbit/s. These properties laid a good foundation for the actual application of LDPC. Keywords-LDPCt; decoder;MSPCF;FPGA
Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method

In this supplementary material, we give the proofs for the theorems in the main text. We consider more general optimization problem: min x∈X ,y∈Y 1 T T t=1 f (x, w t) + ψ(y), (S-1a) s.t. Ax + By − b = 0, (S-1b) where A ∈ R l×m , B ∈ R l×d and b ∈ R l. We solve this problem in an online manner using OPG-ADMM and RDA-ADMM techniques. The algorithm in the main text corresponds to the situation where B = −I and b = 0. Corresponding to the optimization problem (S-1), we consider the following generalized version of OPG-ADMM: Let x 1 = 0, λ 1 = 0, and By 1 = b (we assume there exists y 1 that satisfies this equality for simplicity), and the update rule of the t-th step is given by x t+1 = argmin x∈X g ⊤ t x − λ ⊤ t (Ax + By t − b) + ρ 2 Ax + By t − b 2 + 1 2η t x − x t 2 Gt , (S-2a) y t+1 = argmin y∈Y ψ(y) − λ ⊤ t (Ax t+1 + By − b) + ρ 2 Ax t+1 + By − b 2 , (S-2b) λ t+1 = λ t − ρ(Ax t+1 + By t+1 − b), (S-2c) where G t = γI − η t ρA ⊤ A. Moreover we define ˜ λ t = λ t − ρ(Ax t+1 + By t − b). (S-3) This method satisfies the following regret property. 1 T T t=1 (f (x t , w t) + ψ(y t)) − 1 T T t=1 (f (x * , w t) + ψ(y *)) + 1 T T t=1   −A ⊤ ˜ λ t −B ⊤ ˜ λ t Ax t + By t − b   ⊤   x t − x * y t − y * ˜ λ t − λ *  
Multi-scale edge finding in noisy pictures

Finding edges in digital images is a very basic and important task. This paper describes a new technique to address this problem. The proposed technique tries to solve two of the main difficulties that edge finding algorithms must face: uncertainty due to noise artifacts and combination of details detected at different scales. In particular we adapt a fuzzy arithmetic approach called “Fuzzy Marching Squares” (G.Gallo, S.Spinello, Thresholding and fast iso-contour extraction with fuzzy arithmetic, Pattern Recognition Letters 21 (2000) 31-44) to obtain the edge map of a digital picture. Our map assembles together information about uncertainty and scale. Comparisons with results obtained with Canny’s algorithm show that our approach is particularly suitable in presence of additive noise. As for complexity the proposed approach is comparable with the application of traditional median filter.
Real-Time Radon Transform via the GPU Graphics Pipeline

Graphics processing units (GPUs) found in consumer hardware have emerged as first choice for computational-intensive image processing tasks, e.g., real-time computer tomography (CT) reconstruction. However, the GPU programming paradigm is rather intricate. To leverage the potential of GPUs for a broad audience, this paper is concerned with a self-contained, pedagogical implementation of the prototypical forward and inverse Radon transform. The full source code is available for download. Instead of utilizing the GPU merely as multiprocessor, the algorithm explicitly employs the graphics pipeline to take advantage of the dedicated GPU hardware components.
Knowing that You Know and Knowing that You don't Know: a fMRI Study on Feeling-of-Knowing (FOK)

Neural correlates of feeling-of-knowing (FOK) were investigated by event-related fMRI and unrelated word-pairs in a standard Recall-Judgment-Recognition (RJR) procedure. According to performance in post-scan criterion test, FOK trials were categorized as ”pp” (positive-FOK, negative-recognition), and ”PN” (positive-FOK, negative-recognition). Contrasts between accurate FOK predictions (PP, NN) and inaccurate ones (NP, PN) revealed no difference. Further analysis indicated PP and NN were different; combining them together might conceal differences. Specifically, PP was associated with left prefrontal activities in BA 8 or BA 47 relative to NN or NP respectively. This observation queried the conventional view that regarded PP and NN as the same kind of ”accurate FOK predictions”, and called for dissociations between feeling-of-knowing (PP) and feeling-of-not-knowing (NN).
Prediction of image popularity over time on social media networks

This paper introduces a new problem that investigates image popularity on social networks. It is referred to as the stability of image popularity. It focuses on whether the popularity of a certain image on social networks will change over time or not. In order to investigate what drives the changes of popularity, we employ three features of social media data, i.e., social context, image semantics, and an image's early popularity. To predict the changes of images popularity, we collected 69,000 images from Instagram and kept track of their popularity for one month. For the popularity measure, we use Pareto principle. Using a GaussianNaive Bayes classifier, we predict the changes of image popularity, and the results show that the semantic feature is the only one that predicts the changes in the popularity of an image over time.
Systematic Review and Meta-Analysis of Neuropsychiatric Symptoms and Executive Functioning in Adults With Phenylketonuria

ABSTRACT This systematic review and meta-analysis (MA) investigates the impact of elevated blood phenylalanine (Phe) on neuropsychiatric symptoms in adults with phenylketonuria (PKU). The meta-analysis of PKU is challenging because high-quality evidence is lacking due to the limited number of affected individuals and few placebo-controlled, double-blind studies of adults with high and low blood Phe. Neuropsychiatric symptoms associated with PKU exceed general population estimates for inattention, hyperactivity, depression, and anxiety. High Phe is associated with an increased prevalence of neuropsychiatric symptoms and executive functioning deficits whereas low Phe is associated with improved neurological performance. Findings support lifelong maintenance of low blood Phe.
Intrusion Detectionusing Address Monitoring

Security is emerging as an important concern in embedded system design. Security of an embedded system is compromised when software that can be trusted is resulting in unintended behaviour such as leakage of sensitive data and execution of spiteful code. Several counter measures have been proposed to counteract these sorts of intrusions. The general idea behind most of the methods is to define the security policy at system level and check for security breach either statically or at run time. A hardware assisted run time address monitoring system is presented here to enhance embedded system security by detecting and preventing unintended program behaviour. The embedded processor is augmented with the hardware monitor that observes the dynamic execution trace of the processor. It checks whether the execution trace falls within the allowed program behaviour, and flags any deviation from the expected behaviour. This technique can counteract several common software and physical attacks. Furthermore it can ensure secure program execution with minimal overheads.
Damage Identification for Transmission Tower Based on Support Vector Machine and RBF

Transmission tower occupies an important position in the event of transmission of electricity. The failure of transmission tower would cause serious economic losses. As a damage identification parameter, variation ratio of curvature mode has a great ability to damage location. In the field of damage location identification on transmission tower, variation ratio of curvature mode achieved good results even in the condition of tiny damage such as 1%. Support vector machine, as new machine learning algorithm, has shown its superiority of the ability of regression in the fields of damage identification. In this paper, the method of least squares support vector machine is applied to study on the damage extent identification of transmission tower. It is found that this method can extremely approach the targets even under the condition of little sample and it has accurate ability of damage extent identification.
Study on speaker verification on emotional speech

Besides background noise, channel effect and speaker’s health condition, emotion is another factor which may influence the performance of a speaker verification system. In this paper, the performance of a GMM-UBM based speaker verification system on emotional speech is studied. It is found that speech with various emotions aggravates the verification performance. Two reasons for the performance aggravation are analyzed, they are mismatched emotions between the speaker models and the test utterances, and the articulating styles of certain emotions which create intense intra-speaker vocal variability. In response to the first reason, an emotion-dependent score normalization method is proposed, which is borrowed from the idea of Hnorm. Index Terms: speaker verification, emotional speech
Automatic Sentiment Monitoring of Specific Topics in the Blogosphere

The classification of a text according to its sentiment is a task of raising relevance in many applications, including applications related to monitoring and tracking of the blogosphere. The blogosphere provides a rich source of information about products, personalities, technologies, etc. The identification of the sentiment expressed in articles is an important asset to a proper analysis of this user-generated data. In this paper we focus on the task of automatic determination of the polarity of blogs articles, i. e. , the sentiment analysis of blogs. In order to identify whether a piece of text expresses a positive or negative opinion, an approach based on word spotting was used. Empirical results on different domains show that our approach performs well if compared to costly and domain-specific approaches. In addition to that, if we consider an aggregation of a set of documents and not the polarity of each individual document, we can achieve an accuracy distribution around 90% for specific topics of a certain domain.
The ADAPT entry to the Blizzard Challenge 2016

This paper describes the text-to-speech synthesis system developed for the Blizzard Challenge 2016 by members of the ADAPT centre and colleagues from associated projects. The task was to build a synthetic voice for reading audiobooks to children, from a speech database of audiobooks around 5 hours long. Our entry system is an HMM-based parametric speech synthesizer which was built using a subset of the database (half the total number of the audiobooks of the full dataset). We only used this subset because it was the best quality data we could obtain under the time constraints posed by the Challenges’ deadlines. The main parts of the work undertaken on the development of the system for this challenge were on text chunking, including splitting of sentences and segments of text in quotes, and automatic alignment of speech and text data. We also aimed to synthesize speech with emotions to improve the expressiveness of the synthetic speech. Although we could not concretize this task on time for the submission, we plan to carry on this work and possibly use it in a future entry of our system to the Blizzard Challenge.
Vertical Integration: Results From a Cross-Course Student Collaboration

The authors report the results of a cross-class project involving sophomore-level students in an Operations Analysis (OA) class with junior-level students in an Operations Management (OM) class. The students formed virtual teams and developed a simulation model of a call center. The OM students provided the management expertise, while the OA students provided the technical background. Our objective was to reinforce concepts from both courses while providing an opportunity to work in virtual teams. Results suggest great dissatisfaction with virtual teams. Many communication and technology challenges need to be overcome to make this approach a practical one.
Four-dimensional distribution of the 2010 Eyjafjallajökull volcanic cloud over Europe observed by EARLINET

Abstract. The eruption of the Icelandic volcano Eyjafjallajokull in April–May 2010 represents a "natural experiment" to study the impact of volcanic emissions on a continental scale. For the first time, quantitative data about the presence, altitude, and layering of the volcanic cloud, in conjunction with optical information, are available for most parts of Europe derived from the observations by the European Aerosol Research Lidar NETwork (EARLINET). Based on multi-wavelength Raman lidar systems, EARLINET is the only instrument worldwide that is able to provide dense time series of high-quality optical data to be used for aerosol typing and for the retrieval of particle microphysical properties as a function of altitude. In this work we show the four-dimensional (4-D) distribution of the Eyjafjallajokull volcanic cloud in the troposphere over Europe as observed by EARLINET during the entire volcanic event (15 April–26 May 2010). All optical properties directly measured (backscatter, extinction, and particle linear depolarization ratio) are stored in the EARLINET database available at http://www.earlinet. org . A specific relational database providing the volcanic mask over Europe, realized ad hoc for this specific event, has been developed and is available on request at http://www.earlinet. org . During the first days after the eruption, volcanic particles were detected over Central Europe within a wide range of altitudes, from the upper troposphere down to the local planetary boundary layer (PBL). After 19 April 2010, volcanic particles were detected over southern and south-eastern Europe. During the first half of May (5–15 May), material emitted by the Eyjafjallajokull volcano was detected over Spain and Portugal and then over the Mediterranean and the Balkans. The last observations of the event were recorded until 25 May in Central Europe and in the Eastern Mediterranean area. The 4-D distribution of volcanic aerosol layering and optical properties on European scale reported here provides an unprecedented data set for evaluating satellite data and aerosol dispersion models for this kind of volcanic events.
Towards link characterization from content

In processing large volumes of speech and language data, we are often interested in the distribution of languages, speakers, topics, etc. For large data sets, these distributions are typically estimated at a given point in time using pattern classification technology. Such estimates can be highly biased, especially for rare classes. While these biases have been addressed in some applications, they have thus far been ignored in the speech and language literature. This neglect causes significant error for low-frequency classes. Correcting this biased distribution involves exploiting uncertain knowledge of the classifier error patterns. The Metropolis-Hastings algorithm allows us to construct a Bayes estimator for the true class proportions. We experimentally evaluate this algorithm for a speaker recognition task. In this experiment, the Bayes estimator reduces maximum RMSE by a factor of five. Performance is furthermore more consistent, with range of RMSE reduced by a factor of 4.
Analysis of Productive Learning Behaviors in a Structured Inquiry Cycle Using Hidden Markov Models

This paper demonstrates the generality of the hidden Markov model approach for exploratory sequence analysis by applying the methodology to study students’ learning behaviors in a new domain, i.e., an asynchronous, online environment that promotes an explicit inquiry cycle while permitting a great deal of learner control. Our analysis demonstrates that the high-performing students have more linear learning behaviors, and that their behaviors remain consistent across different study modules. We also compare our approach to a process mining approach, and suggest how they may complement one another.
Do Somatic Symptoms Predict the Severity of Depression? A Validation Study of the Korean Version of the Depression and Somatic Symptoms Scale

This study aimed at exploring the psychometric characteristics of the Korean Version of the Depression and Somatic Symptoms Scale (DSSS) in a clinical sample, and investigating the impact of somatic symptoms on the severity of depression. Participants were 203 consecutive outpatients with current major depressive disorders (MDD) or lifetime diagnosis of MDD. The DSSS was compared with the Montgomery-Åsberg Depression Rating Scale (MADRS) and the 17-items Hamilton Depression Rating Scale (HAMD). The DSSS showed a two-factor structure that accounted for 56.8% of the variance, as well as excellent internal consistency (Cronbach’s alpha = 0.95), concurrent validity (r = 0.44–0.82), and temporal stability (intraclass correlation coefficient = 0.79). The DSSS had a high ability to identify patients in non-remission (area under receiver operating characteristic [ROC] curve = 0.887). Maximal discrimination between remission and non-full remission was obtained at a cut-off score of 22 (sensitivity = 82.1%, specificity = 81.4%). The number of somatic symptoms (the range of somatic symptoms) and the scores on the somatic subscale (SS, the severity of somatic symptoms) in non-remission patients were greater than those in remission patients. The number of somatic symptoms (slope = 0.148) and the SS score (slope = 0.472) were confirmed as excellent predictors of the depression severity as indicated by the MADRS scores. The findings indicate that the DSSS is a useful tool for simultaneously, rapidly, and accurately measuring depression and somatic symptoms in clinical practice settings and in consultation fields.