Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning-based named entity recognition via effective integration of various evidences

Named entity recognition identifies and classifies entity names in a text document into some predefined categories. It resolves the “who”, “where” and “how much” problems in information extraction and leads to the resolution of the “what” and “how” problems in further processing. This paper presents a Hidden Markov Model (HMM) and proposes a HMM-based named entity recognizer implemented as the system PowerNE. Through the HMM and an effective constraint relaxation algorithm to deal with the data sparseness problem, PowerNE is able to effectively apply and integrate various internal and external evidences of entity names. Currently, four evidences are included: (1) a simple deterministic internal feature of the words, such as capitalization and digitalization; (2) an internal semantic feature of the important triggers; (3) an internal gazetteer feature, which determines the appearance of the current word string in the provided gazetteer list; and (4) an external macro context feature, which deals with the name alias phenomena. In this way, the named entity recognition problem is resolved effectively. PowerNE has been benchmarked with the Message Understanding Conferences (MUC) data. The evaluation shows that, using the formal training and test data of the MUC-6 and MUC-7 English named entity tasks, and it achieves the F-measures of 96.6 and 94.1, respectively. Compared with the best reported machine learning system, it achieves a 1.7 higher F-measure with one quarter of the training data on MUC-6, and a 3.6 higher F-measure with one ninth of the training data on MUC-7. In addition, it performs slightly better than the best reported handcrafted rule-based systems on MUC-6 and MUC-7.
An altered neonatal behavioral phenotype in Mecp2 mutant mice

We examined somatic growth, somatosensory reflexes, and ultrasonic calls from postnatal day 3 to day 18 in Mecp21lox mutant mice, a mouse model of Rett syndrome. Both Mecp2 null male and Mecp2 heterozygous female mice exhibited normal somatic growth, but transient delays in the development of some reflexes relative to sex-matched wild-type mice. Both Mecp2 null male and heterozygous female mice exhibited dramatic increases in ultrasonic vocalizations in response to social isolation; these differences were evident as early as postnatal day 5. These results suggest very early abnormalities in sensory reflex development and behavioral responsiveness in the Mecp2 mutants that may provide a target for early therapeutic intervention.
Learning and Generalization Theories of Large Committee-Machines

The study of the distribution of volumes associated to the internal representations of learning examples allows us to derive the critical learning capacity $\left( {\alpha _c = \frac{{16}}{\pi }\sqrt {\ln \,k} } \right)$ of large committee machines, to verify the stability of the solution in the limit of a large number K of hidden units and to find a Bayesian generalization cross-over at a=K.
Coreless current measurement transducer and instrument for distorted and noisy currents

Accurate measurement of distorted, noisy and pulsed currents is still a challenge. Current transformers introduce unacceptable errors in magnitude and/or phase shift. Rogowski coils are useful for clamp-on field measurements, but can be cumbersome to use. This paper presents a transducer consisting of coreless transformer and a non-integrating electronic amplifier. Calibration of two prototype transducers, for primary currents of 20 A and 60 A rms, is presented, confirming excellent linearity throughout the range, both in terms of magnitude and frequency. Together with a dedicated stochastic instrument, these transducers can perform very accurate measurement of distorted mains currents, including the current harmonics as well as measurements of active and reactive energy. Measurement accuracy is confirmed in two cases of low-level, distorted and noisy currents.
Cerebrospinal fluid markers for differential dementia diagnosis in a large memory clinic cohort

Objective: To determine how amyloid β 42 (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau) levels in CSF behave in a large cohort of patients with different types of dementia. Methods: Baseline CSF was collected from 512 patients with Alzheimer disease (AD) and 272 patients with other types of dementia (OD), 135 patients with a psychiatric disorder (PSY), and 275 patients with subjective memory complaints (SMC). Aβ42, t-tau, and p-tau (at amino acid 181) were measured in CSF by ELISA. Autopsy was obtained in a subgroup of 17 patients. Results: A correct classification of patients with AD (92%) and patients with OD (66%) was accomplished when CSF Aβ42 and p-tau were combined. Patients with progressive supranuclear palsy had normal CSF biomarker values in 90%. Patients with Creutzfeldt-Jakob disease demonstrated an extremely high CSF t-tau at a relatively normal CSF p-tau. CSF AD biomarker profile was seen in 47% of patients with dementia with Lewy bodies (DLB), 38% in corticobasal degeneration (CBD), and almost 30% in frontotemporal lobar degeneration (FTLD) and vascular dementia (VaD). PSY and SMC patients had normal CSF biomarkers in 91% and 88%. Older patients are more likely to have a CSF AD profile. Concordance between clinical and neuropathologic diagnosis was 85%. CSF markers reflected neuropathology in 94%. Conclusion: CSF Aβ42, t-tau, and p-tau are useful in differential dementia diagnosis. However, in DLB, FTLD, VaD, and CBD, a substantial group exhibit a CSF AD biomarker profile, which requires more autopsy confirmation in the future.
ICOTS6, 2002: Utts 1 WHAT EDUCATED CITIZENS SHOULD KNOW ABOUT STATISTICS AND PROBABILITY

Much has changed since the widespread introduction of statistics courses into the curriculum in the 1960s and 1970s, but the way introductory statistics courses are taught has not kept up with those changes. This paper discusses the changes, and the way the introductory syllabus should change to reflect them. In particular, seven ideas are discussed that every student who takes elementary statistics should learn and understand in order to be an educated citizen. Misunderstanding these topics leads to cynicism among the public at best, and misuse of study results by physicians and others at worst.
Can Non-lytic CD8+ T Cells Drive HIV-1 Escape?

The CD8+ T cell effector mechanisms that mediate control of HIV-1 and SIV infections remain poorly understood. Recent work suggests that the mechanism may be primarily non-lytic. This is in apparent conflict with the observation that SIV and HIV-1 variants that escape CD8+ T cell surveillance are frequently selected. Whilst it is clear that a variant that has escaped a lytic response can have a fitness advantage compared to the wild-type, it is less obvious that this holds in the face of non-lytic control where both wild-type and variant infected cells would be affected by soluble factors. In particular, the high motility of T cells in lymphoid tissue would be expected to rapidly destroy local effects making selection of escape variants by non-lytic responses unlikely. The observation of frequent HIV-1 and SIV escape poses a number of questions. Most importantly, is the consistent observation of viral escape proof that HIV-1- and SIV-specific CD8+ T cells lyse infected cells or can this also be the result of non-lytic control? Additionally, the rate at which a variant strain escapes a lytic CD8+ T cell response is related to the strength of the response. Is the same relationship true for a non-lytic response? Finally, the potential anti-viral control mediated by non-lytic mechanisms compared to lytic mechanisms is unknown. These questions cannot be addressed with current experimental techniques nor with the standard mathematical models. Instead we have developed a 3D cellular automaton model of HIV-1 which captures spatial and temporal dynamics. The model reproduces in vivo HIV-1 dynamics at the cellular and population level. Using this model we demonstrate that non-lytic effector mechanisms can select for escape variants but that outgrowth of the variant is slower and less frequent than from a lytic response so that non-lytic responses can potentially offer more durable control.
Information retrieval in context using various health terminologies

Information retrieval is a branch of computer science concerned with the acquisition, storage, search and selection of information. From the user point of view, the access to information can be carried out in a deliberate way through an information retrieval system, or in a passive way through an information filtration system. CISMeF (Catalogue and Index of the French-speaking Medical Sites) is a health portal aiming to catalogue and index the most important French-speaking institutional health information sources in order to make them available to health professionals, medical students and the general public. The Internet resources were manually indexed and have remained mono-terminological from 1995 to 2007 originally based exclusively on the MeSH thesaurus (Medical Subject Headings). Categorization allows a contextual information retrieval parameterized according to the user's needs. In 2007, the CISMeF team directed its objectives towards a multi-terminological universe by the integration of the medical data heterogeneous sources into its back-office. To date, the practical application is the creation of a bilingual (French/English) drug information portal in order to facilitate the user information retrieval about drugs.
Distributed log information processing with Map-Reduce: A case study from raw data to final models

With the high development of Internet, e-commerce websites now routinely have to work with log datasets which are up to a few terabytes in size. How to remove messy data timely with low cost and find out useful information is a problem we have to face. The mining process involves several steps from pre-processing the raw data to establishing the final models. In this paper we describe our method to solve the problem with Map-Reduce. Hadoop[7] is a Map-Reduce implementation develops open-source software for reliable, scalable, distributed computing. Several applications which we have proposed: data extracting, sum operation, join operation and clustering algorithm are applied on hadoop. We can apply them on data pre-processing and detect users with the same interests. In particular, we focus on clustering algorithms. A clustering algorithms which integrate SOM(Self-Organized Map) and fuzzy[13] logic is combined with Map-Reduce and we call it MRSF here. With the help of hadoop cluster, large calculation of jobs with MRSF can be accommodated easily by just adding more nodes or computers to the cluster. From the experiment, we show that MRSF can scale well and efficiently process and analyze extremely large datasets.
Constraining Light Gravitino Mass from Cosmic Microwave Background(16th YKIS Conference Progress in Particle Physics 2008)

We investigate the possibilities of constraining the light gravitino mass m{sub 3/2} from future cosmic microwave background (CMB) surveys. A model with light gravitino with the mass m{sub 3/2} < O(10) eV is of great interest since it is free from the cosmological gravitino problem and, in addition, can be compatible with many baryogenesis/leptogenesis scenarios such as the thermal leptogenesis. We show that the lensing of CMB anisotropies can be a good probe for m{sub 3/2} and obtain an expected constraint on m{sub 3/2} from precise measurements of lensing potential in the future CMB surveys, such as the PolarBeaR and CMBpol experiments. If the gravitino mass is m{sub 3/2} = 1 eV, we will obtain the constraint for the gravitino mass as m{sub 3/2} ≤ 3.2 eV (95%C.L.) for the case with Planck+PolarBeaR combined and m{sub 3/2} = 1.04{sup +0.22}{sub −0.26} eV (68%C.L.) for CMBpol. The issue of Bayesian model selection is also discussed.
Investigating the impact of technology trust on the acceptance of mobile banking technology within Nigeria

This empirical study investigates the factors influencing Nigerians’ trust and adoption of mobile banking technology; using an integrative model. Research was conducted using a questionnaire developed and distributed in Nigeria. Out of 2256 returned questionnaires, 1725 were deemed to have been completed and hence usable. The data was analysed using confirmatory factor analysis and the results showed that confidentiality, integrity, authentication, access control, best business practices and non-repudiation significantly influenced technology trust with availability showing unsatisfactory values for consideration. In addition, technology trust showed a direct significant influence on perceived ease of use and usefulness. Technology trust also showed an indirect influence on intention to use through its impact on perceived usefulness and perceived ease of use. Also, perceived ease of use and perceived usefulness showed significant influence on consumer’s intention to adopt the technology. As a result of increased understanding of customer trust and adoption trends in Nigeria, these findings have important theoretical implications for researchers with interests in technology acceptance trends and the role of external factors, such as trust, in user adoption of technology. Such implications include the provision of empirical data, which highlights the role technology trust, demographic segmentations, perceived ease of use and perceived usefulness play in mobile banking adoption in Nigeria. In addition, the successful completion of this study provides justification for the use of this research’s model as a suitable framework for investigating user adoption of technology.
Investigating Cardiac Arrhythmia in ECG using Random Forest Classification

Electrocardiogram (ECG) is used to assess the heart arrhythmia. Accurate detection of beats helps determine different types of arrhythmia which are relevant to diagnose heart disease. Automatic assessment of arrhythmia for patients is widely studied. This paper presents an ECG classification method for arrhythmic beat classification using RR interval. The methodology is based on discrete cosine transform (DCT) conversion of RR interval. The RR interval of the beat is extracted from the ECG and used as feature. DCT conversion of RR interval is applied and the beats are classified using random tree. Experiments were conducted using MIT-BIH arrhythmia database.
Detecting Hierarchical and Overlapping Community Structures in Networks

Community structure can be observed in many natural, biological and social networks. Studies sug- gest that these communities may have organized in a hi- erarchical manner while some communities overlap with others. This paper introduces an algorithm to detect such hierarchical and overlapping community structures in net- works based on the concept of maximal cliques. It intro- duces an alternate modularity for evaluating overlapping community structures. Unlike existing algorithms for de- tecting hierarchical and overlapping community structures, the new algorithm is free of parameter tuning and random seeds. Experiments conducted on two real-world networks show that this algorithm is capable of providing satisfac- tory and consistent results.
The "distance-varying" gravity model in international economics: is the distance an obstacle to trade?

In this paper, we address the problem of the role of the distance between trading partners by assuming the variability of coefficients in a standard gravity model. The distance can be interpreted as an indicator of the cost of entry in a market (a fixed cost): the greater the distance, the higher the entry cost, and the more we need to have a large market to be able to cover a high cost of entry. To explore this idea, the paper uses a method called Flexible Least Squares. By allowing the parameters of the gravity model to vary over the observations, our main result is that the more the partner's GDP is large, the less the distance is an obstacle to trade.
Novel Bayesian multiscale method for speckle removal in medical ultrasound images

A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. The authors show that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, the authors design a Bayesian estimator that exploits these statistics. They use the alpha-stable model to develop a blind noise-removal processor that performs a nonlinear operation on the data. Finally, the authors compare their technique with current state-of-the-art soft and hard thresholding methods applied on actual ultrasound medical images and they quantify the achieved performance improvement.
Anisotropic geometric diffusion in image and image sequence processing

In this thesis nonlinear anisotropic geometric diffusion methods for the processing of static images and image-sequences are discussed. The models depend only on the morphology of the underlying data, and thus they are invariant under monotone transformations of the gray values. The evolution, which depends on the principal curvatures and the principal directions of curvature of level-sets, is capable of preserving important features of codimension 2, i.e. corners and edges of the level-sets. For the processing of image-sequences an anisotropic behavior in direction of the apparent motion of the level-sets is prescribed. Important for the processing of noisy images is a suitable regularization of the data. Different approaches are discussed and the results of a local projection approach onto a polynomial space is compared with the convolution with kernels having compact support. For the nonlinear problems the existence of viscosity solutions is shown by using a result of Giga et. al. for the linearized problems together with a fixed point argument. The discretization of the models is done using a semi-implicit time-discretization together with finite elements on regular quadrilateral and hexahedral grids. Furthermore for the processing of image-sequences an operator splitting scheme is derived, which enables to solve this high dimensional problem with moderate effort.
On the Efficacy of Contractual Provisions for Processing Tomatoes

This paper uses extensive data on production outcomes for processing tomato growers in California to examine the efficacy of explicit incentives observed in grower-processor contracts. Our data include all deliveries of tomatoes to some 51 processors over a period of 7 years in which at least 65 unique types of contracts are employed. Results indicate that incentives account for a significant proportion of observed variation in production outcomes, and that complementarities across different sorts of "incentive instruments" play a prominent role in contract design. Although explicit incentives explain a substantial portion of the variation in production outcomes relative to that which could be explained by incentives (as captured by processor/year fixed effects), there remains considerable variation which might be accounted for by unobserved or implicit incentives. Finally, we control for a quite exhaustive set of factors other than incentive provisions that might conceivably affect expected production outcomes, yet are still left with a substantial amount of unexplained variation.
DPMM: A novel mobility model for delay tolerant networks

The mobility model is one of the most important factors in the performance evaluations of a mobile ad hoc network (MANET). The Manhattan mobility model has been used to model the node's mobility, where the movement pattern of mobile node on streets and roads defined by maps. However, DakNet ad hoc network that uses wireless technology to provide digital connectivity among villages, kiosks and private communication devices and between kiosks and a hub (for non-real time internet access) using low-cost WiFi radio transceivers. One typical mobility behavior is geographical restriction movement. To investigate DakNet scenario an underlying realistic mobility model is desired. In this paper, we propose a novel mobility model “Digital Postal Mobility Model” (DPMM), which closely approximates the mobility patterns in DakNet scenario; it models the movement of nodes at pre-defined path and have a set of destinations and also visit all destination. We demonstrate our proposed mobility model by evaluating various MANET routing protocols, including DSR and DSDV, the performance of DPMM is efficient than existing geographical restricted models and NS simulation results shows that the choice of mobility model has significant impact on network performance.
Obtaining measurement patterns of partial discharges in power cables XLPE using Probabilistic Neural Networks

This paper presents a computational implementation of a Probabilistic Neural Network for obtaining patterns of partial discharges in power cables XLPE. The experimental measurement data of the power cables are obtained in the Laboratory Testing Equipment and Materials (LAPEM), which is a certified laboratory property of the Comisión Federal de Electricidad (CFE) in México. These data implicitly contain the patterns of partial discharges and are used to carry out the training and testing of Probabilistic Neural Network. In order to illustrate the reliability and validity of the proposed computational implementation, the results obtained by this proposed implementation are compared with those calculated by the methodologies given in the standard IEC 60270.
Collaborative filtering via co-factorization of individuals and groups

Matrix factorization is one of the most successful collaborative filtering methods for recommender systems. Traditionally, matrix factorization only uses the observed user-item feedback information, which makes predictions on cold users/items difficult. In many applications, user/item content information are also available and they have been successfully used in content-based methods. In recent years, there are attempts to incorporate content information into matrix factorization. In particular, the Factorization Machine (FM) is one of the most notable examples. However, FM is a general factorization model that models interactions between all features into a latent feature space. In this paper, we propose a novel combination of tree-based feature group learning and matrix co-factorization that extends FM to recommender systems. Experimental results on a number of benchmark data sets show that the proposed algorithm outperforms state-of-the-art methods, particularly for predictions on cold users and cold items.