Miracle-dz/UniFAR-FTUs huggingface dataset:
Emigration and Brain Drain: Evidence from the Caribbean

Abstract This paper quantifies the magnitude and nature of migration flows from the Caribbean and estimates their costs and benefits. The Caribbean countries have lost 1040 percent of their labor force due to emigration to OECD member countries. The migration rates are particularly striking for the high-skilled labor force. Many countries have lost more than 70 percent of their labor force with more than 12 years of completed schoolingamong the highest emigration rates in the world. The region is also the world's largest recipient of remittances as a percent of GDP. Remittances constituted about 13 percent of the regions GDP in 2002. Simple welfare calculations (under very conservative assumptions of elasticities) suggest that the losses due to high-skill migration (ceteris paribus) outweigh the official remittances to the Caribbean region.
RSEP1 is a novel gene with functional involvement in neuropathic pain behaviour

Neuropathic pain from nerve injury by trauma, disease or surgery often causes prolonged suffering. To explore the molecular mechanisms that underlie neuropathic pain, we used mRNA from the L4–5 segments of the lumbar spinal cord of rats with chronic constriction injury (CCI)‐induced neuropathic pain, and differentially screened a cDNA library from the rat brain. A novel gene, termed RSEP1 (Rat Spinal cord Expression Protein 1), was identified. Northern blots revealed that RSEP1 was expressed mainly in the central nervous system including the cerebral cortex, hippocampus, brainstem and spinal cord, as well as in the kidney and ovary. In situ hybridization showed a high level of RSEP1 expression in the CA1, CA3 and dentate gyrus regions of the hippocampus and the small sensory neurons in the dorsal horn, as well as the large neurons in the ventral horn of the spinal cord. Intrathecal injection of RSEP1 antisense oligonucleotide into the spinal cord lumbar enlargement attenuated neuropathic pain behaviours in CCI rats, suggesting a functional involvement of RSEP1 in neuropathic pain.
Preserving Home Languages and Cultures in the Classroom: Challenges and Opportunities.

Decades of research document the powerful academic and socio-affective benefits of a strong home language base and affirmation of home language and culture as a valuable resource (Soto, 1998). Yet policymakers across America continue to consider ways of eliminating diverse languages and cultures by proposing learning environments where English is the only language of instruction and culture is a minor issue. Examples of the political pressures affecting our educational system can be seen in the passage of California's Proposition 227, "English for the Children," earmarked at eliminating bilingual education programs; in the Wall Street Journal (April 10, 1996) where anti-bilingual advocates call themselves "bilingual education abolitionists"; in a Texas courtroom where the home language issue became a child custody issue (NY Times, August 30, 1995); and in the U.S. Congress where English-only legislation continues to be debated. For educators hoping to instill respect for cultural and linguistic diversity through bilingual and multicultural content, this climate of controversy offers both challenges and opportunities.
An ontology-based interface for improving information exploration

The explosive growth of the Web necessitates the use of approaches such as search engines, social indexing and visualisation systems to assist in exploring online information. Current systems based on keyword search and sequential listing are becoming inadequate due to their inability to make sense of unstructured information. This calls for more work into the automatic structuring of text to produce accurate and machine-friendly metadata. This paper presents a working ontology-based interface for exploring large amounts of news articles across different domains based on the seamless and automatic discovery of document abstractions.
FP -GROWTH BASED NEW NORMALIZATION TECHNIQUE FOR SUBGRAPH RANKING

The various problems in large volume of data area have been solved using frequent itemset discovery algorithms. As data mining techniques are being introduced and widely applied to non-traditional itemsets, existing approaches for finding frequent itemsets were out of date as they cannot satisfy the requirement of these domains. Hence, an alternate method of modeling the objects in the said data set, is graph. Modeling objects using graphs allows us to represent an arbitrary relation among entities. The graph is used to model the database objects. Within that model, the problem of finding frequent patterns becomes that of finding subgraphs that occur frequently over the entire set of graphs. In this paper, we present an efficient algorithm for ranking of such frequent subgraphs. This proposed ranking method is applied to the FP-growth method for discovering frequent subgraphs. In order to find out the ranking of subgraphs we present a new normalization technique which is the modified normalization technique applied at each position for a chosen value of Discounted Cumulative Gain (DCG) of a subgraph. Instead of DCG another modified approach called Modified Discounted Cumulative Gain (MDCG) is introduced. The MDCG alone cannot be used to achieve the performance from one query to the next in the search engine’s algorithm. To obtain the new normalization technique an ideal ordering of MDCG (IMDCG) at each position is to be found out. A Modified Discounted Cumulative Gain (MDCG) is calculated using “lift” as a new approach. IMDCG is also evaluated. Then the new approach for finding the normalized values are to be computed. Finally, the values for all rules can be averaged to get an average performance of a ranking algorithm. And also the ordering of obtained values as a result at each position will provide the order of evaluation of rules which in turn gives an efficient ranking of mined subgraphs.
Incorporating information extraction in the relational database model

Modern information extraction pipelines are typically constructed by (1) loading textual data from a database into a special-purpose application, (2) applying a myriad of text-analytics functions to the text, which produce a structured relational table, and (3) storing this table in a database. Obviously, this approach can lead to laborious development processes, complex and tangled programs, and inefficient control flows. Towards solving these deficiencies, we embark on an effort to lay the foundations of a new generation of text-centric database management systems. Concretely, we extend the relational model by incorporating into it the theory of document spanners which provides the means and methods for the model to engage the Information Extraction (IE) tasks. This extended model, called Spannerlog, provides a novel declarative method for defining and manipulating textual data, which makes possible the automation of the typical work method described above. In addition to formally defining Spannerlog and illustrating its usefulness for IE tasks, we also report on initial results concerning its expressive power.
On speech features fusion, α-integration Gaussian modeling and multi-style training for noise robust speaker classification

This paper investigates the fusion of Mel-frequency cepstral coefficients (MFCC) and statistical pH features to improve the performance of speaker verification (SV) in non-stationary noise conditions. The α-integrated Gaussian Mixture Model ( α-GMM) classifier is adopted for speaker modeling. Two different approaches are applied to reduce the effects of noise corruption in the SV task: speech enhancement and multi-style training (MT). The spectral subtraction with minimum statistics (MS/SS) and the optimally-modified log-spectral amplitude with improved minima controlled recursive averaging (IMCRA/OMLSA) are examined for the speech enhancement procedure. The MT techniques are based on colored (Colored-MT), white (White-MT) and narrow-band (Narrow-MT) noises. Six real non-stationary noises, collected from different acoustic sources, are used to corrupt the TIMIT speech database in four different signal-to-noise ratios (SNR). The index of non-stationarity (INS) is chosen for the stationarity tests of the acoustic noises. Complementary SV experiments are conducted in realistic noisy conditions using the MIT database. The results show that the best SV accuracy was obtained with the MFCC + pH features fusion, the MS/SS and the Colored-MT.
FINDING FREQUENT SUBPATHS IN A G RAPH

The problem considered is that of finding frequent subpaths of a database of paths in a fixed undirected graph. This problem arises in applications such as predicting congestion in network and vehicular traffic. An algorithm, called AFS, based on the classic frequent itemset mining algorithm Apriori is developed, but with significantly improved efficiency over Apriori from exponential in transaction size to quadratic through exploiting the underlying graph structure. This efficiency makes AFS feasible for practical input path sizes. It is also proved that a natural generalization of the frequent subpaths problem is not amenable to any solution quicker than Apriori.
An Incremental Algorithm Based on Irregular Grid for Clustering Data Stream

The CluStream algorithm can not process arbitrary shapes of clusters well. Having analyzed the nature of data stream, proposed the incremental clustering algorithm based on irregular grid in data stream, namely, IIGStream. IIGStream has the advantage of clustering fast which traditional grid clustering algorithms has, and it adjust the structure of grid dynamically. To new coming data points, it promises the effectiveness of clustering of different cluster shapes by judging whether the grids are connected. During the process of clustering, it need not specify the number of clusters in advance, and it is not sensitive to outliers. The experiments on real datasets and synthetic datasets show the applicability and validity of IIGStream.
Evaluation of treatment outcomes in a 3 years post-graduate orthodontic program using the peer assessment rating (par)

Objectives: To maintain high treatment quality it is important to evaluate orthodontic treatment results using objective methods. Outcome assessments allow private practitioners and university students to evaluate their results and raise the level of treatment outcomes. The aim of this study was to assess the orthodontic treatment outcome in a post-graduate orthodontics program in the University of Valencia (Spain) and to determine whether the treatment outcome is related to several factors as gender, age at start of the treatment, treatment duration, treatment method, extraction-non extraction treatment and cooperation needed. Material and Methods: A sample of 50 patients treated in the post-graduate clinic was randomly selected. Pre-treatment and post-treatment study casts have been assessed by the Peer Assessment Rating (PAR index). The influence of various factors: gender, age at start of the treatment, treatment duration, treatment method, extraction-non extraction treatment, cooperation needed and number of students finishing each case, were statistically analyzed. Results: According to the PAR index, orthodontic treatment reduced the malocclusion in a mean point reduction of 21.4 (CI 95% 18.7-24.1) and a mean percentage reduction of 80.5% (CI 95% 75.9-85.1). The total of the cases improved, 44% of the patients were in greatly improved category. Conclusions: None of the variables studied influenced significantly the treatment outcomes regarding the PAR. Based on the general classification criteria of the index, the results showed that the patients received a high standard treatment. Key words:Treatment outcome, orthodontics education, PAR Index.
Emergency Department Problems: A Call for Hybrid Simulation

 Abstract— Emergency department (ED) is a unit in hospital that provides treatment for acutely ill and injured patients 24 hours daily. As a system, ED reveals detailed and dynamic complexity of its interaction between inter-departmental units and intra-departmental elements. Discrete event simulation (DES) has been recognized with its ability to comprehend micro level problems with detailed complexity. From the macro level perspective, system dynamics (SD) has advantages over DES based on its holistic view capability that is able to capture the dynamic relationships between elements in a system. Both modeling approaches have strengths and weaknesses and therefore integrating both will provide a more realistic view of complex systems. This paper is based on an on- going study that uses computer simulation, a branch of Operation Research techniques in modeling an actual ED of a general hospital in Penang, Malaysia. We describe the potential benefits of integrating SD and DES in exhibiting both detailed and dynamic complexity of the ED. We propose a hybrid simulation framework with intent to better understand system's behavior and how patient flow in the ED is influenced by capacity availability.
Configurable memory protection by aspects

We describe the implementation of memory protection by means of aspect-oriented programming (AOP) in CiAO, an AUTOSAR-like family of embedded operating systems. The use of AOP was originally motivated by the fact that memory protection is a cross-cutting policy, which, furthermore, has to be configurable at build-time in AUTOSAR. We learned, however, that besides switching between full protection and no protection, an AOP-based approach also makes it easy to apply completely different models of protection. For the domain of statically configured embedded systems, where certain failure scenarios can often be excluded by means of code analysis or even probability, this facilitates tailored and light-weight "pay-as-you-use" protection strategies.
How Histopathology Can Contribute to an Understanding of Defense Mechanisms against Cryptococci

Invasive fungal infections, particularly those considered opportunistic, have become a common and significant complication of procedures performed in advanced contemporary medicine. Among such infections, cryptococcosis, which is usually caused by infection with Cryptococcus neoformans and Cryptococcus gattii, is particularly problematic because this fungal infection occurs in immunocompromised and apparently immunocompetent individuals. It has been largely accepted that Cryptococcus species are recognized by cellular receptors and that Th1-type immune responses play an important role in defense mechanisms against the yeast. However, the interaction between the yeast and host tissue varies depending on the characteristics of the yeast and the immune status of the host. To gain a better understanding of the pathophysiology of cryptococcosis, we wish to emphasize the usefulness of histopathological examinations, because it allowed more detailed information of an extremely complex interaction between the causative yeasts and tissue response. In the present review, we describe the pathophysiology of cryptococcosis as largely revealed in our previous histopathological investigations of the experimental infection.
Bayesian Single-Epoch Photometric Classification of Supernovae

Ongoing supernova (SN) surveys find hundreds of candidates that require confirmation for their various uses. Traditional classification based on follow-up spectroscopy of all candidates is virtually impossible for these large samples. The use of Type Ia SNe as standard candles is at an evolved stage that requires pure, uncontaminated samples. However, other SN survey applications, such as measuring cosmic SN rates, could benefit from a classification of SNe on a statistical basis, rather than case by case. With this objective in mind, we have developed the SN-ABC, an automatic Bayesian classifying algorithm for supernovae. We rely solely on single-epoch multiband photometry and host-galaxy (photometric) redshift information to sort SN candidates into the two major types, Ia and core-collapse supernovae. We test the SN-ABC performance on published samples of SNe from the Supernova Legacy Survey (SNLS) and GOODS projects that have both broadband photometry and spectroscopic classification (so the true type is known). The SN-ABC correctly classifies up to 97% (85%) of the Type Ia (II-P) SNe in SNLS, and similar fractions of the GOODS SNe, depending on photometric redshift quality. Using simulations with large artificial samples, we find similarly high success fractions for Types Ia and II-P, and reasonable (∼75%) success rates in classifying Type Ibc SNe as core-collapse. Type IIn SNe, however, are often misclassified as Type Ia. In deep surveys, SNe Ia are best classified at redshifts z ≳ 0.6 or when near maximum. Core-collapse SNe (other than Type IIn) are best recognized several weeks after maximum, or at z ≲ 0.6. Assuming the SNe are young, as would be the case for rolling surveys, the success fractions improve by a degree dependent on the type and redshift. The fractional contamination of a single-epoch photometrically selected sample of SNe Ia by core-collapse SNe varies between less than 10% and as much as 30%, depending on the intrinsic fraction and redshift distribution of the core-collapse SNe in a given survey. The SN-ABC also allows the rejection of SN "impostors" such as active galactic nuclei (AGNs), with half of the AGNs we simulate rejected by the algorithm. Our algorithm also supplies a good measure of the quality of the classification, which is valuable for error estimation.
Large visual words for large scale image classification

Recently, using large visual vocabulary or codebooks to quantize and partition the set of local feature descriptors into large set of disjoint subsets termed visual words (or large visual words) has become an important research topic in solving many computer vision problems including near duplicate image retrieval, object retrieval, etc. Generally, large visual words means a heavy burden on the cost of time and memory space for both the construction of large vocabulary and the searching process, especially for large scale applications. In this paper, we present an efficient generation approach of large visual words with a very compact vocabulary, namely two dictionaries learned with sparse non-negative matrix factorization (NMF). After piecewise sparse decomposition of features with two learned dictionaries, we map a pair of indices of the dictionary's bases corresponding to the maximum elements of the two sparse codes to a large set of visual words upon the assumption that data with similar properties will share the same base with the largest sparse coefficient. With the help of an inverted file structure built through the large visual words, K-nearest neighbors (KNN) can be efficiently retrieved. Therefore, we can classify images very efficiently with the incorporation of our fast KNN search based on large visual words into SVM-KNN method. Experiments on the public Oxford dataset, and ACM Multimedia 2013 Yahoo! image classification challenge dataset show that our approach is both effective and efficient.
The Ponzi Scheme Puzzle: A History and Analysis of Con Artists and Victims

In a Ponzi scheme, new investments are used to pay existing investors, to cover the cost of salespersons, and to finance the Ponzi schemer's satisfying lifestyle. Although Charles Ponzi recruited investors in Boston in 1919 and died in 1949, his design and mode of operation are alive and well today. Indeed, losses from Ponzi schemes in the United States are equal to losses from shoplifting. Ponzi schemes catch in their net highly sophisticated individuals and institutions as well as low-income and middle-income investors, and these schemes have attracted investors all over the world, in Russia, England, India, Albania, Romania, Portugal, Costa Rica, and elsewhere. Looking into the innumerable cases of Ponzi schemes throughout the years, Tamar Frankel observes that even though patterns began to emerge in the stories of con artists and their victims' behavior, the main puzzles still remain: How do con artists dazzle and lure wealthy and educated individuals and representatives of large institutions to hand over huge sums of money? How do con artists divert investors' attention from the soft spots of their stories? And while there are so many books and articles about Ponzi schemes, their warnings and constant advice on how to detect and avoid con artists go unheeded. In The Ponzi Scheme Puzzle, Frankel explores con artists' fascinating power of persuasion and deception, and analyzes their subtle signals that mimic truth and honesty. She identifies the reasons for the local and global success and longevity of such schemes and seeks to understand the nature of the con artists and their victims. She combines the many stories of Ponzi schemes, derived mostly from court cases and newspaper articles, to show the patterns of such frauds, the nature of the con artists, and character of their victims. These patterns tell us much about human nature, about our society, and about ourselves. The book first analyzes the design and pattern of the con artists' attractive offers and how they hide deceptions, then deals with the ways in which schemes are advertised and sold. Next, it focuses on the core of con artists' success, then discusses the characters of con artists and their victims. Finally, Frankel offers a number of observations on the lessons we can learn from these stories and analyses. She concludes that our attitude to con artists is ambivalent and uncertain perhaps because their behavior is so close to the behavior of honest people; or perhaps because they act like the social leaders with whom they are likely to mingle, or perhaps their actions are necessary to shake up a complacent society. Therefore, she writes, self-protection from charming, dangerous con artists must involve self-examination: once we recognize our own tendencies we can better protect ourselves from their toxic attraction. Available in OSO:
Nonstationary spectrum estimation and time-frequency concentration

This paper extends Thomson's (1982) multitaper spectrum estimation method to nonstationary signals. The method uses a newly-derived set of basis functions which generalize the concentration properties of the prolate spheroidal waveforms to the time-frequency case. We solve for the basis which diagonalizes the nonstationary spectrum generating operator over a finite region of the time-frequency plane. These eigenfunctions are maximally concentrated to and orthogonal over the specified time-frequency region, and are thus doubly orthogonal. Individual spectrograms computed with these eigenfunctions form direct time-frequency spectrum estimates. We next present a multitaper time-frequency spectrum estimation procedure using these time-frequency eigenestimates. Bias and variance expressions are derived, allowing for a statistical characterization of the accuracy of the estimate. The time-frequency concentration property of the basis functions yields an estimator with excellent bias properties, while the variance of the estimate is reduced through the use of multiple orthogonal windows.
Compensating for the latency of data acquisition for localization in mobile wireless sensor networks

The problem of latency in data acquisition, which is specific to the case of mobile wireless sensor networks, has been introduced and investigated. In this regard, the properties of the so-called Data Acquisition Latency Error, and the way it affects the performance of the respective localization schemes in a mobile network were discussed through both theoretical analysis and simulations. Moreover, a weighted version of lateration scheme was introduced and shown to outperform the traditional one, while tracking a mobile node. This work is to the best of our knowledge the first to have addressed the aforementioned problem in mobile wireless sensor networks, and to have offered a solution for its alleviation.