Miracle-dz/UniFAR-FTUs huggingface dataset:
Mishmash on Mismatch? Balance-Sheet Effects and Emerging-Markets Crises

We critically assess the recent empirical literature on the importance of dollar debt and balance-sheet effects in the emerging-market financial crises of the 1990s. Using a simple model, we discuss which specifications are theoretically appropriate, and provide additional insights as to the proper interpretation of the reduced-form evidence in the literature. We show that the variety of results found in the existing literature are related to the heterogeneity of regression specifications. Using harmonized micro data on corporates across a dozen countries in Latin America and Asia, and we replicate common specifications for the effect of dollar debt on investment and output at the firm level. In this light, we suggest that the literature on corporate-level currency mismatch is hardly a mishmash, but in fact quite concordant.
Forecasting severity of software vulnerability using grey model GM(1,1)

Vulnerabilities usually represents the risk level of software, and it is of high value to forecast vulnerabilities so as to evaluate the security level of software. Current researches mainly focus on predicting the number of vulnerabilities or the occurrence time of vulnerabilities, however, to our best knowledge, there are no other researches focusing on the prediction of vulnerabilities' severity, which we think is an important aspect reflecting vulnerabilities and software security. To compensate for this deficiency, we borrows the grey model GM(1,1) from grey system theory to forecast the severity of vulnerabilities. The experiment is carried on the real data collected from CVE and proves the feasibility of our predicting method.
An Access Control Model for Online Social Networks

Over the last decade online social networking sites have increased in popularity. This popularity has brought many users together, increasing their ability to share information. While the general information sharing is desirable, some users might be concerned with the privacy implications of disclosing so much personal information online. Currently users have two main options- They can either refuse to enter the information they are uncomfortable disclosing, or they may limit access to the information via privacy controls provided by the social networking site. It is important to note, however, that in the second case the information is stored remotely, and thus the control of the data is lost to the user. This paper presents an access control model for online social networks.
Plan Execution for Autonomous Spacecraft

The New Millennium Remote Agent (NMRA) vSll be the first AI system to control an actual spacecraft. The spacecraft domain raises a number of challenges for planning and execution, ranging from extended agency and long-term planning to dynamic recoveries and robust concurrent execution, all in the presence of tight real-time deadlines, changing goals, scarce resource constraints, and a v-ide variety of possible failures. We believe NMRA is the first system to integrate closed-loop planning and e.xecution of concurrent temporal plans, and the first autonomous ystem that v-ill be able to achieve a sustained multi-stage multiyear mission v-ithout communication or guidance from earth.
A halo mass—concentration relation from weak lensing

We perform a statistical weak lensing analysis of dark matter profiles around tracers of halo mass from galaxy-size to cluster-size halos. In this analysis we use 170 640 isolated ∼L* galaxies split into ellipticals and spirals, 38 236 groups traced via isolated spectroscopic luminous red galaxies and 13 823 maxBCG clusters from the Sloan Digital Sky Survey covering a wide range of richness. Together these three samples allow a determination of the density profiles of dark matter halos over three orders of magnitude in mass, from to . The resulting lensing signal is consistent with a Navarro–Frenk–White (NFW) or Einasto profile on scales outside the central region. In the inner regions, uncertainty in modeling of the proper identification of the halo center and inclusion of baryonic effects from the central galaxy make the comparison less reliable. We find that the NFW concentration parameter c200b decreases with halo mass, from around 10 for galactic halos to 4 for cluster halos. Assuming its dependence on halo mass in the form of we find c0 = 4.6 ± 0.7 (at z = 0.22) and β = 0.13 ± 0.07, with very similar results for the Einasto profile. The slope (β) is in agreement with theoretical predictions, while the amplitude is about two standard deviations below the predictions for this mass and redshift, but we note that the published values in the literature differ at a level of 10–20% and that for a proper comparison our analysis should be repeated in simulations. We compare our results to other recent determinations, some of which find significantly higher concentrations. We discuss the implications of our results for the baryonic effects on the shear power spectrum: since these are expected to increase the halo concentration, the fact that we see no evidence of high concentrations on scales above 20% of the virial radius suggests that baryonic effects are limited to small scales, and are not a significant source of uncertainty for the current weak lensing measurements of the dark matter power spectrum.
Using mixed and virtual Reality systems for Gait- Rehabilitation

Neurological deficits resulting from spinal cord lesions, stroke and traumatic brain injuries frequently affect walking functions. Since the ability to walk influences the participation in social and economic life, its restoration through rehabilitation plays an important role in improving a patients quality of life [1]. Nowadays gate therapy is mostly achieved through electrically driven gait orthosis combined with partial body weight support, where rehabilitation sessions involve the constant repetition of physiologically correct gait patterns. We have earlier proposed that neuronal reorganization and functional recovery can be optimized by embedding rehabilitation in a task oriented paradigm [2] here we present the application of this concept to gate rehabilitation. we have developed a virtual reality application that is capable of autonomously producing different procedural, virtual environments and rehabilitation tasks. The initial environmental variability is obtained by varying topography, weather condition, time, flora and fauna. These factors are constantly altered over time in a task dependent way. The application furthermore modifies rehabilitation tasks online through the assessment of a patients motor capability [3, 4]. Hence, using this technology we can enhance the ecological validity of the gate rehabilitation by defining terrain properties and tasks that implicitly define the gate patterns that the patient is supposed to exercise. We will present preliminary results of our system.
Factors affecting knowledge sharing and its effect on performance of vocational higher education of agriculture in Java

The agricultural sector have important role for a country. The level of performance of the agricultural sector of a country depends on the quality of education in agriculture. Basically agricultural vocational education in Indonesia was established in each region in order to meet the human resources in accordance with the potential of the area. Therefore, if the agricultural lecturers that have different specialties can share the knowledge among each other it is expected that the that one lecturer can provide knowledge that is appropriate for the processing of agricultural potential in these areas eventhough it is not his speciality. So it is not depending on the specific agricultural science owned by lecturers but it depends on the potential of the area. It can be anticipated by increasing knowledge sharing among educators. Therefore, it is necessary to determine the factors that influence knowledge sharing among educators in vocational higher education of agriculture. In addition, note also whether knowledge sharing affects the performance of educators and affect the performance of the institution. This study was conducted with the literature study, interviews, and surveys. Survey data were analyzed using Partial Least Square method - Structural Equation Modeling. The results of this study indicate that knowledge sharing is one of the factors that can improve the performance of educators and can improve the performance of vocational higher education of agriculture. Knowledge sharing in this domain is strongly influenced by attitudes, personal expectations, tacit knowledge, the IT infrastructure, knowledge of the IT usage, the ability of educators, and organizational culture.
The Rodney L. White Center for Financial Research Estimating the Returns to Insider Trading

This paper estimates the returns to insiders when they trade their company's stock. We first construct a rolling “purchase portfolio” that holds all shares purchased by insiders for a six-month period and an analogous “sale portfolio” that holds all shares sold by insiders for six months. The six-month horizon is chosen to coincide with the “short-swing” rule of the Securities and Exchange Act of 1934; a rule that prohibits profit-taking by insiders for offsetting trades within six months. We then employ performance-evaluation methods to analyze the returns to the purchase and sale portfolios. This approach yields a proxy for the value-weighted returns to insider transactions beginning on the day after their execution and avoids the statistical difficulties that plague event studies on long-horizon returns. Our methods are designed to estimate the returns earned by insiders themselves and thereby differ from the previous insider-trading literature, which focuses on the “informativeness” of insider trades for other investors. Using a comprehensive sample of reported insider transactions from 1975-1996, we find that the purchase portfolio earns abnormal returns of more than 50 basis points per month. About one-quarter of these abnormal returns accrue within the first five days after the initial transaction, and one-half accrue within the first month. The sale portfolio does not earn abnormal returns. Our portfolio-based approach also allows for straightforward decompositions of performance by various characteristics; we find that the abnormal returns to insider trades in small firms are not significantly different from those in large firms, and that top executives do not earn higher abnormal returns than do other insiders.
ECLC: Edge character and latency connection enhanced inter-frame difference algorithm for real-time outdoor surveillance

This paper presents a technique, ECLC, for improving the accuracy and robustness of the inter-frame difference motion detection algorithm at a very low computation cost, which surpass the latest classical background subtraction algorithm ViBe. It incorporates several innovative mechanisms: edge characteristic info is introduced for more accurately identifying the foreground model with gradient info consideration; neighbourhood mean filter based latency connection is carried out to strengthen the foreground pixel's spatial correlation. Thoroughly experiments have been carried out, and efficiency figures showed that our algorithm can get exact moving object from complex background accurately like GMM and ViBe at a very low computation cost, which is even lower than ViBe. And also ECLC can solve some other typical problems such as ghost, shadow and illumination changes.
Gender Differences in Hypertension Control Among Older Korean Adults: Korean Social Life, Health, and Aging Project

Objectives: Controlling blood pressure is a key step in reducing cardiovascular mortality in older adults. Gender differences in patients’ attitudes after disease diagnosis and their management of the disease have been identified. However, it is unclear whether gender differences exist in hypertension management among older adults. We hypothesized that gender differences would exist among factors associated with hypertension diagnosis and control among community-dwelling, older adults. Methods: This cross-sectional study analyzed data from 653 Koreans aged ≥60 years who participated in the Korean Social Life, Health, and Aging Project. Multiple logistic regression was used to compare several variables between undiagnosed and diagnosed hypertension, and between uncontrolled and controlled hypertension. Results: Diabetes was more prevalent in men and women who had uncontrolled hypertension than those with controlled hypertension or undiagnosed hypertension. High body mass index was significantly associated with uncontrolled hypertension only in men. Multiple logistic regression analysis indicated that in women, awareness of one’s blood pressure level (odds ratio [OR], 2.86; p=0.003) and the number of blood pressure checkups over the previous year (OR, 1.06; p=0.011) might influence the likelihood of being diagnosed with hypertension. More highly educated women were more likely to have controlled hypertension than non-educated women (OR, 5.23; p=0.013). Conclusions: This study suggests that gender differences exist among factors associated with hypertension diagnosis and control in the study population of community-dwelling, older adults. Education-based health promotion strategies for hypertension control might be more effective in elderly women than in elderly men. Gender-specific approaches may be required to effectively control hypertension among older adults.
Lifting ℓ1-optimization strong and sectional thresholds

In this paper we revisit under-determined linear systems of equations with sparse solutions. As is well known, these systems are among core mathematical problems of a very popular compressed sensing field. The popularity of the field as well as a substantial academic interest in linear systems with sparse solutions are in a significant part due to seminal results \cite{CRT,DonohoPol}. Namely, working in a statistical scenario, \cite{CRT,DonohoPol} provided substantial mathematical progress in characterizing relation between the dimensions of the systems and the sparsity of unknown vectors recoverable through a particular polynomial technique called $\ell_1$-minimization. In our own series of work \cite{StojnicCSetam09,StojnicUpper10,StojnicEquiv10} we also provided a collection of mathematical results related to these problems. While, Donoho's work \cite{DonohoPol,DonohoUnsigned} established (and our own work \cite{StojnicCSetam09,StojnicUpper10,StojnicEquiv10} reaffirmed) the typical or the so-called \emph{weak threshold} behavior of $\ell_1$-minimization many important questions remain unanswered. Among the most important ones are those that relate to non-typical or the so-called \emph{strong threshold} behavior. These questions are usually combinatorial in nature and known techniques come up short of providing the exact answers. In this paper we provide a powerful mechanism that that can be used to attack the "tough" scenario, i.e. the \emph{strong threshold} (and its a similar form called \emph{sectional threshold}) of $\ell_1$-minimization.
Distributed spectrum allocation with the Cournot competition

The paper considers a new method of the spectrum scheduling process and QoS support in distributed wireless networks using a game theoretic-framework. The game uses incomplete information model, so the concept of the Bayesian Nash equilibrium is used. Moreover, we consider the new algorithm of distributed spectrum sharing based on the Cournot oligopoly competition. Multiple traffic classes of high and low priority have been used with special parameters which represent priority classes and the volume of users' individual demands. The proposed resource-sharing algorithm may be used in distributed opportunistic or cognitive wireless networks.
Optimal packet scheduling in a multiple access channel with energy harvesting transmitters

In this paper, we investigate the optimal packet scheduling problem in a two-user multiple access communication system, where the transmitters are able to harvest energy from the nature. Under a deterministic system setting, we assume that the energy harvesting times and harvested energy amounts are known before the transmission starts. For the packet arrivals, we assume that packets have already arrived and are ready to be transmitted at the transmitter before the transmission starts. Our goal is to minimize the time by which all packets from both users are delivered to the destination through controlling the transmission powers and transmission rates of both users. We first develop a generalized iterative backward waterfilling algorithm to characterize the maximum departure region of the transmitters for any given deadline T. Then, based on the sequence of maximum departure regions at energy arrival instants, we decompose the transmission completion time minimization problem into convex optimization problems and solve the overall problem efficiently.
Dutch named entity recognition using classifier ensembles

Named Entity Recognition (NER) is the task of automatically identifying names within text and classifying them into categories, such as persons, locations and organizations. A variety of machine learning algorithms has been applied to the task, with research often aimed at feature selection and parameter optimization to improve a single classifier’s performance. However, finding the optimal features and parameters is a complex problem. An alternative research direction is to combine several classifiers into an ensemble, and combining their output using a voting procedure. The assumption is that combining a diverse set of classifiers improves the generalization accuracy, provided that the ensemble’s members have sufficient individual performance and the errors they make are, to some extent, non-overlapping. Again, finding such an ensemble is a non-trivial problem. In this paper, we describe a system that uses genetic algorithms to construct the best ensemble from a set of classifiers, based on the approach proposed in [3]. Instead of using only maximum entropy classifiers, however, we select classifiers from three different frameworks, namely memory-based learning (MBL), conditional random fields (CRF) and support vector machines (SVM), and evaluate the performance on a Dutch data set.
A new Markovian approach towards neural spike sorting

Brain is the most complicated organ of body. It controls the activity of all other organs. Understanding its function and its language could give us a direct communication pathway for connecting with injured motor organ and it could be the core of functional repairing. Neurons are the vertices of a vast network that generates the brain signals. Neuronal recordings capture brain activity signatures. The processing of these signals can help to translate brain's language. Usually it follows three main stages: spike detection and extraction, spike sorting, and intention extraction from the encoded signal. In this work, we introduce an original idea based on Hidden Markov Models (HMM) which helps to improve the spike sorting stage. Our idea is a fast and simple method which uses Inter Spike Interval information besides spike waveforms to define a Hidden Markov Model that consecutive spikes should track.
Characterization of body node in PD SOI MOSFETs using 4-port VNA measurements

Body-contacts (BC) partially depleted (PD) silicon-on-insulator (SOI) MOSFETs suffer from a loss of body potential control at frequencies higher than several hundreds of MHz due to the high value of the body resistance (Rbe) in currently available PD SOI technologies. This causes a reduction of the transconductance in dynamic threshold (DT) MOSFETs and an increase of output conductance in both DT MOSFETs and body-tied (BT) MOSFETs, thereby altering the performance of those devices for analogue/RF applications. A correct small signal modeling of these effects requires an accurate characterization of the body resistance and the body capacitances. This is what is proposed in this work, using for the first time 4-port VNA measurements
An Automated Finger-Knuckle-Print Identification System Using Jointly RBF & RFT Classifiers

Ensuring the security of a wide variety of applicationssuch as public security, access control and banking, is becoming an increasingly important problem as moderntechnology is integrated into the majority of these applications. Thus, establishing identity of a person is the significant methodused to ensure a high security level. As a security method, biometric identification has a long tradition and is a synonym forthe uniqueness of person. In this paper, based on Finger-Knuckle-Print (FKP), we present a multimodal personal identificationsystem using two feature extraction methods with their fusionapplied at the matching score level. In this study, the segmentedFKP is firstly represented by the Histogram of Oriented Gradients(HOG) descriptors. Subsequently, the Radial Basis Function(RBF) and Random Forest Transform (RFT) models are used todesign two sub-systems. The proposed method is validated fortheir efficacy on the available PolyU FKP Database of 165 users. Our experimental results show the effectiveness and reliabilityof the proposed approach, which brings both high identificationand accuracy rate.
Transgenic plants: Risks, Concerns and Effects on Ecosystem and Human Health

The review portrays the current status of transgenic crops in the backdrop of sharply divided opinion of anti-GM group vis-A -vis those who advocate that genetically engineered crops do not pose any threat to human or animal health nor does it disrupt the environment through introduction of superweeds. While acknowledging the concern of anti-GM activists, the article supports the view of NE Borlaug who sees no difference between the birth of a genetically complex crop such as hexaploid bread wheat and a bioengineered crop except that the former occurs in mother nature and the latter in the laboratory through genetic manipulation by molecular breeders. The article brings into focus that the methods now being employed to bring about genetic change in crops are safer as the use of antibiotic markers have been replaced by biolistic mode of gene delivery or use of chemicals such as mannose 6-phosphate marker. The article also points out that the chances of birth of â€˜superweeds' destroying the environment will be minimized, if genetic transformation is brought about using chloroplasts. Another important role of biotechnology that is expected to be realized soon is its use in commercial production of oral vaccines for both humans and animals. The article mentions of some of the recent gene discovery such as genes for submergence tolerance in rice or genes that minimizes allergenicity in peanuts. Production of rice varieties capable of withstanding flood will dramatically increase rice yield in flood-prone countries and peanut lines with minimum or no allergen will be welcome in the consumer market where a sizeable portion of people suffer from peanut allergy. The article emphasizes the fact that every GM crop must be subject to rigorous scrutiny to ensure that it is free from any allergen, or hazardous toxic substance and that it is environmentally safe. The article supports the idea that in Europe GM crops with built-in terminator gene and traditional crops may coexist; i.e., may be planted in the same area as the European farmers buy their seeds every year unlike their counterparts in Asia and Africa. Finally the article recommends that the selected GM crops should be grown in the developing countries where the option before the resource-poor population lies between going without food or fall prey to the misinformation of anti-GM activists. Key words: GM Crops, Anti-GM activists, Coexistence, Superweed, Chloroplast Transformation, Oral vaccines, Golden rice, Biosafety, Food security DOI = 10.3329/ptcb.v16i2. 1114 Plant Tissue Cult. & Biotech. 16(2): 139-164, 2006 (December)