Miracle-dz/UniFAR-FTUs huggingface dataset:
Essays on business cycles fluctuations and forecasting methods

Los profundos efectos socio-economicos que trajo aparejada la ultima crisis economica global, junto con la rapidez con la que dicha crisis se propago alrededor del planeta, forzo a los hacedores de politica economica a actuar con celeridad, aunque en muchos casos sin contar con herramientas de diagnostico adecuadas. Por ello no sorprende que, desde entonces, se haya incrementado el interes por el estudio y el desarrollo de mecanismos de analisis y evaluacion que permitan, no solo una mejor comprension respecto de los origenes y la transmision de las perturbaciones que afectan a los ciclos economicos, sino que tambien tengan el potencial de proporcionar mayor precision a la hora de valorar la situacion presente y prever su evolucion futura. Asimismo, los ultimos anos tambien han sido claves en el desarrollo de nuevas tecnologias de la informacion, que han posibilitado la acumulacion de grandes volumenes de datos, cuyo acceso es cada vez menos restringido y su disponibilidad puede llegar a ser instantanea. En este contexto, nuevos desafios surgen para los investigadores. Por un lado, el incremento exponencial del volumen de datos disponible supone dificultades desde el punto de vista de la distincion y seleccion de aquella informacion que pueda ser potencialmente relevante. Por otro lado, el desarrollo de tecnicas que permitan lidiar con dicho volumen de informacion implica nuevos retos desde el punto de vista del analisis econometrico. En linea con los nuevos desafios planteados, esta tesis doctoral desarrolla metodologias que, desde una perspectiva lineal o no lineal y adaptandose al flujo de la informacion, son capaces de lidiar con grandes volumenes de datos, cuyo contenido puede cambiar en tiempo real. La aplicacion empirica de las propuestas contribuye a tratar con algunas de las inquietudes que surgieron o se potenciaron tras la crisis de global de 2008. Asi, se analizan aspectos esenciales para el analisis macroeconomico como lo son la prediccion e identificacion de puntos de quiebre en el ciclo economico, la interaccion de dichos ciclos entre diversas economias o el desarrollo de tecnicas capaces de pronosticar la evolucion de indicadores economicos clave con base en nuevas fuentes de informacion, como aquella de surge de los motores de busqueda en Internet. A lo largo de los tres capitulos de esta tesis, se desarrollan propuestas para trabajar con grandes bases de datos que cambian en tiempo real y que, desde el punto de vista lineal o no, se adaptan al flujo de disponibilidad de la informacion, ya sea por ser publicada en distinta frecuencia o con diferentes retardos. Dichos modelos son aplicados con exito en la evaluacion de cuestiones de relevancia como lo son la fluctuacion de los ciclos economicos, la trasmision de las perturbaciones a dichos ciclos entre distintos paises o la prediccion de variables economicas de relevancia con base a nuevas fuentes de informacion.
State Fiscal Effort and Juvenile Incarceration Rates: Are We Misdirecting our Investment in Human Capital?

abstract: The relationship between lack of a high school diploma and juvenile incarceration is well known. This study investigated whether an increase in state fiscal effort for education is associated with decreased juvenile incarceration rates. Each state’s and the District of Columbia’s educational fiscal effort and its relation with state juvenile incarceration rates over a 25-year time period was examined at 5, 10, 15, and 20-year lag intervals to account for effect delays. A statistically significant inverse association between state educational fiscal effort and state juvenile incarceration rates across the United States was found using a GEE with raw data at a 5-year time lag. Statistically significant associations were found using Pearson’s Product Moment analysis in 10 states as well. On average across the U.S., a 1% increase in fiscal effort is estimated to provide a potential average yearly savings of $4,953,967.25 per state.
Job embeddedness and employee enactment of innovation-related work behaviours

In a highly competitive globalised environment, the innovation behaviour of employees plays a key role in the economic viability and competitive advantage of organisations. In this context, developing the understanding of innovation work behaviour is important for the field of individual innovation and this is the focus of the study. The paper aims to discuss this issue. ,Data were collected using a survey from 549 employees in organisations operating in four major business centres in South Africa. ,On-the-job embeddedness was positively and significantly related to innovation behaviours by employees in organisations operating in diverse industries. Consistent with the view that small organisations have a “behavioural” innovation advantage over larger organisations, the size of the organisation moderated the positive relationship between on-the-job embeddedness and innovation behaviours. On-the-job embeddedness was more positively related to innovation behaviours in small organisations than in larger organisations. ,Employees who are highly embedded in their jobs (but not necessarily their communities) are more likely to enact innovation behaviours than employees who are not similarly embedded. Human resource management professionals and line managers can potentially foster employee innovation behaviours through adopting strategies aimed at positively influencing the fit, links and sacrifice dimensions of on-the-job embeddedness. ,The study contributes to theoretical and empirical expansion of job embeddedness (JE) by examining: how work and non-work forces that attach employees to their organisations influence their propensity to enact innovation behaviours; and how organisation size moderates the relationship between JE and innovation behaviours. The results will help managers who wish to foster innovation.
Investigating Global–Local Networks and Policy Learning in University Curriculum Policy Transformations

The focus is on universities around the world strengthening their competitive positioning in a global knowledge era through curriculum policy transformations. It highlights the need to study how ideological shifts and policy flows as well as networks between global and local levels are producing ‘radical’ change in curriculum policies in certain research-intensive universities. The article sets out a research approach for such study in relation to the Asia-Pacific Region. Pursuing such a research approach should make a significant contribution to the field of globalization and higher education.
Regulating Characteristics of New Tamping Device Exciter Controlled by Rotary Valve

Tamping devices have a common problem that the amplitude and frequency of the tamping tool cannot be adjusted and, thus, are too inflexible to satisfy different working demands. In order to solve this problem, mathematical modeling and analysis of a new tamping arm are developed according to the technical parameters of the existing tamping devices, and the configuration of the new tamping exciter controlled by a rotary valve is analyzed using analytical method. Considering the feasibility and practicability of the new tamping device, the special electrohydraulic exciter and rotary valve are determined. Then, the control characteristic, performance, and dynamic motions of the exciter are studied in view of the ballast condition feature. Finally, the test verifies the theoretical model and the new tamping device exciter shows good performance.
Contracting Nonlinear Observers: Convex Optimization and Learning from Data

A new approach to design of nonlinear observers (state estimators) is proposed. The main idea is to (i) construct a convex set of dynamical systems which are contracting observers for a particular system, and (ii) optimize over this set for one which minimizes a bound on state-estimation error on a simulated noisy data set. We construct convex sets of continuous-time and discrete-time observers, as well as contracting sampled-data observers for continuous-time systems. Convex bounds for learning are constructed using Lagrangian relaxation. The utility of the proposed methods are verified using numerical simulation.
Engineering responsive supramolecular biomaterials: Toward smart therapeutics

Abstract Engineering materials using supramolecular principles enables generalizable and modular platforms that have tunable chemical, mechanical, and biological properties. Applying this bottom‐up, molecular engineering‐based approach to therapeutic design affords unmatched control of emergent properties and functionalities. In preparing responsive materials for biomedical applications, the dynamic character of typical supramolecular interactions facilitates systems that can more rapidly sense and respond to specific stimuli through a fundamental change in material properties or characteristics, as compared to cases where covalent bonds must be overcome. Several supramolecular motifs have been evaluated toward the preparation of “smart” materials capable of sensing and responding to stimuli. Triggers of interest in designing materials for therapeutic use include applied external fields, environmental changes, biological actuators, applied mechanical loading, and modulation of relative binding affinities. In addition, multistimuli‐responsive routes can be realized that capture combinations of triggers for increased functionality. In sum, supramolecular engineering offers a highly functional strategy to prepare responsive materials. Future development and refinement of these approaches will improve precision in material formation and responsiveness, seek dynamic reciprocity in interactions with living biological systems, and improve spatiotemporal sensing of disease for better therapeutic deployment.
Inductive tangential displacement sensor

The objective of this paper is to investigate a new design of an inductive sensor, and its application for measuring tangential displacement. The sensor consists of one stationary inductor and a movable ferrite plate relative to the inductor. Tangential displacement can be measured as a change in the overlapping area between the inductor and the ferrite plate, while their normal distance is constant. The inductance of the inductor changes, so does the resonant frequency of the antenna-sensor system when the ferrite plate moves in one - x axis direction with respect to the stationary inductor. The operating principle of the sensor is tested using the fabricated prototype. The resonant frequency of the system is wirelessly measured. The obtained sensitivity of the planar tangential inductive displacement sensor is 2.44 MHz/mm.
Near optimal multipoint receive beamforming with finite samples

Receive beamfoming techniques play an important role in MIMO communications systems as means to mitigate interference and enhance the link quality. Multipoint receive beamforming methods are expected to provide additional gains in systems employing advanced coordinated multipoint processing. Due to its importance, receive beamforming has been studied extensively in the literature. The optimal detection schemes are well known in the case of perfect channel knowledge at the receiver. The more realistic case, where the beamformer is constructed using a finite training sequence transmitted by the desired transmitter has also been addressed. The study of the latter case, known as finite sample beamforming, focused on linear detection. In this paper we propose new nonlinear finite sample detection schemes for the case of multiple spatial streams. We show that the proposed schemes significantly outperform the known linear detectors and exhibit near optimal performance. We also show that the complexity of the proposed schemes is very similar to that of standard multi stream MIMO detectors, which makes them ideal for real life systems. We apply the new schemes to uplink space division multiple access and demonstrate the implementation and respective performance gains over standard detectors. Finally, we apply our approach to the case of coordinated multipoint reception where multiple base stations jointly decode multiple spatial streams. We study the tradeoff between performance and backhaul capacity and suggest near optimal finite sample schemes.
Portfolio Selection Problem with a Non-Zero Recovery Bond

In an earlier paper Liang [1 ]， the author applies the framework of Merton [2] to a market model that consists of a n か recoverydefaultable bond as well as a riskless asset. In this paper ， we extend the resul t of Liang [1] for a non-zero recovery bond. We study an optimization problem of consumption and investment strategy for an investor who can invest in a riskless asset and a defaultable asset with a positive 児 covery.Merton [司 isthe firs もも oformulate and solve such an optimization problem ， and many researchers have extended and generalized the model of consum ption and investment. However ， optimization problems with defaultable assets have been rarely s 凶 died ， though it is so important in today's financial market. This paper applies the framework of original Merton's model to a new market model that consists of a recovery bond as well as a riskless asset. U nder the. assum ption that the defaultable asset's price is modeled as a geometric Brownian motion with an unpredictable jump to a positive value ， the optimal problem is reformulated and analytically solved.
Time‐course effect of exercise‐induced muscle damage on localized muscle mechanical properties assessed using elastography

Changes in muscle stiffness after exercise‐induced muscle damage have been classically inferred from passive torque–angle curves. Elastographic techniques can be used to estimate the shear modulus of a localized muscular area. This study aimed to quantify the changes in shear elastic modulus in different regions of the elbow flexors after eccentric exercise and their relation to muscle length.
Extraction of easily interpretable representation using five-layered autoencoder

In Deep Learning, which has become a topic of intense study in recent years, an auto encoder that learns an identity map by a neural network plays an important role to extract features of data. One disadvantage of this method is that it is not always possible to extract appropriate features in the middle layer. In this study, a five-layer auto-encoder based on a sensory integration model proposed by Fukumura et.al is examined. In order to gain easily interpretable and symbolic expressions for pattern recognition, the outputs of neurons in the middle layer are forced to take a binary value and parts of these outputs are forced to take the same value when the input data belongs to the same category. As a result of computer simulation focused on simple pattern recognition, it was confirmed that it is possible to obtain the desired expression in the middle layer.
Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions

Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used.
Carotid IMT is more associated with stroke than risk calculators

It is unclear whether a natural marker of atherosclerosis (carotid intima‐media thickness: CIMT) or calculated risk score is more associated with stroke. We therefore comparatively examined the relationship between CIMT as well as two cardiovascular risk calculators (Omnibus Risk Score ‐ORS and Framingham Risk Score‐ FRS) and the occurrence of stroke among hypertensive African patients.
Peering Into The Future: Three Essays on the Nascent Phenomenon of Collaborative Consumption

We review a marketing reality borne of the digital era, collaborative consumption, in which individuals actively engage in the production of service offerings for the benefit of others. This phenomenon is rapidly gaining momentum with the advent of new technology and firms that seek to develop profitable business models by leveraging their Web platforms to engender trust among strangers and facilitate transactions among consumers. As a result, collaborative consumption has helped push traditional consumption communities from localized marketplaces with limited economic activity to collaborative global communities with important economic, environmental, and social effects.
Multi-target SSVEP-based BCI using Multichannel SSVEP Detection

Spatial filtering method and fast Fourier transform (FFT) based spectrum estimation method are applied to reveal the presence of steady state visual evoked potential (SSVEP) in multiple-electrodes electroencephalogram (EEG) signals used in Brain-Computer Interface (BCI) system. The SSVEP responses are elicited by visual stimuli in the form of flickering light emitting diode (LED) array and computer animation on the screen monitor. The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. Subjects are instructed to shift their gaze during the trial to elicit multiple components of SSVEP spectrums. This approach which is called multi-target SSVEP is proposed to extend the feasibility of a BCI system. Using four subjects with two distinct stimuli, the experiment give a result of 41.6% accuracy for detecting dualfrequency combinations.
Anal Pap smears: Should we be doing them?

Purpose: To identify the need to perform anal Papanicolau (Pap) smears for diagnosing histologic changes associated with human papilloma virus (HPV) infection in order to provide early intervention for the prevention of anal cancer. To offer the primary care provider information, based on evidence‐based research, about identifying the high‐risk patient using risk factors, instructions on performing the anal Pap smear, interpreting the results, and initiating treatment interventions and/or referrals for care. Additionally, the possibility of an HPV vaccine for men will be discussed. Data Sources: Evidence‐based literature, theoretical framework, and peer‐reviewed articles. Conclusions: Performing anal Pap smears is a valuable tool to detect cytologic changes associated with some strains of HPV infection. For persons participating in receptive anal intercourse, this diagnostic procedure provides the opportunity for early detection to guide appropriate follow‐up and interventions. Implications for practice: Appropriate screening can be incorporated easily into a primary care practice. High‐risk groups that would benefit from this screening include men who have sex with men, HIV‐infected men and women, immunocompromised men and women, women with a history of cervical or vulvar cancer (or high‐grade cervical lesions), and women participating in receptive anal intercourse.
Isolation of a DNA probe for identification of Mycobacterium kansasii, including the genetic subgroup

In order to develop a DNA-based assay to identify all Mycobacterium kansasii clinical isolates, a specific DNA probe was isolated in plasmid p6123. A total of 145 M. kansasii clinical isolates were collected from several countries and were examined with three probes by DNA hybridization. Of the 145 isolates, 115 (79%) were positive with the previously described probe pMK1-9 (Z. H. Huang, B. C. Ross, and B. Dwyer, J. Clin. Microbiol. 29:2125-2129, 1991), 129 (88%) were positive with the commercial Accu-probe assay (Gen-Probe), and 145 (100%) were positive with the p6123 probe. Southern blot analysis of EcoRI-digested M. kansasii chromosomal DNA with p6123 revealed that all Accu-probe-positive M. kansasii strains exhibited a 3-kb fragment, whereas all Accu-probe-negative M. kansasii strains displayed DNA fragments of variable molecular sizes. These results indicate that, unlike the previously described probes for M. kansasii, the fragment cloned into p6123 identified all 145 biochemically typical strains tested and provides an ideal target for future DNA-based speciation assays.
Sparse Unsupervised Dimensionality Reduction for Multiple View Data

Different kinds of high-dimensional visual features can be extracted from a single image. Images can thus be treated as multiple view data when taking each type of extracted high-dimensional visual feature as a particular understanding of images. In this paper, we propose a framework of sparse unsupervised dimensionality reduction for multiple view data. The goal of our framework is to find a low-dimensional optimal consensus representation from multiple heterogeneous features by multiview learning. In this framework, we first learn low-dimensional patterns individually from each view, considering the specific statistical property of each view. We construct a low-dimensional optimal consensus representation from those learned patterns, the goal of which is to leverage the complementary nature of the multiple views. We formulate the construction of the low-dimensional consensus representation to approximate the matrix of patterns by means of a low-dimensional consensus base matrix and a loading matrix. To select the most discriminative features for the spectral embedding of multiple views, we propose to add an l1-norm into the loading matrix's columns and impose orthogonal constraints on the base matrix. We develop a new alternating algorithm, i.e., spectral sparse multiview embedding, to efficiently obtain the solution. Each row of the loading matrix encodes structured information corresponding to multiple patterns. In order to gain flexibility in sharing information across subsets of the views, we impose a novel structured sparsity-inducing norm penalty on the loading matrix's rows. This penalty makes the loading coefficients adaptively load shared information across subsets of the learned patterns. We call this method structured sparse multiview dimensionality reduction. Experiments on a toy benchmark image data set and two real-world Web image data sets demonstrate the effectiveness of the proposed algorithms.
Fair Value versus Amortized Cost Measurement and the Timeliness of Other-than-Temporary Impairments: Evidence from the Insurance Industry

We investigate the impact of recurring fair value versus amortized cost measurement for accounting recognition purposes on the timeliness of insurers' other-than-temporary (OTT) impairments of non-agency residential mortgage-backed securities (NAMBS) around the 2007–2009 financial crisis. Unlike largely predetermined amortized cost measurement, recurring fair value measurement requires firms to invest in information and control systems to assess relevant economic conditions and estimate fair values quarterly. We expect these systems discipline insurers' OTT impairments. Exploiting statutory requirements that PC (life) insurers measure securities with NAIC designations from 3 to 5 at fair value (amortized cost) and disclose security-level accounting information, we predict and find that PC insurers record timelier OTT impairments of the same NAMBS with NAIC designations of 3 to 5 than life insurers. We predict and find weaker evidence of spillover effects to the timeliness of OTT impairments of the same NAMBS with NAIC designations of 1 or 2. JEL Classifications: G22; M41. Data Availability: Data are available from public sources cited in the text.