LLukas22/scidocs huggingface dataset:
Evaluating Models of Latent Document Semantics in the Presence of OCR Errors

Models of latent document semantics such as the mixture of multinomials model and Latent Dirichlet Allocation have received substantial attention for their ability to discover topical semantics in large collections of text. In an effort to apply such models to noisy optical character recognition (OCR) text output, we endeavor to understand the effect that character-level noise can have on unsupervised topic modeling. We show the effects both with document-level topic analysis (document clustering) and with word-level topic analysis (LDA) on both synthetic and real-world OCR data. As expected, experimental results show that performance declines as word error rates increase. Common techniques for alleviating these problems, such as filtering low-frequency words, are successful in enhancing model quality, but exhibit failure trends similar to models trained on unprocessed OCR output in the case of LDA. To our knowledge, this study is the first of its kind.
Backstepping Control Design of a Single-Link Flexible Robotic Manipulator

In this paper, the backstepping design scheme is developed for the tip-position trajectory tracking control of single-link flexible robotic manipulator systems. An infinite dimensional dynamic model of a single-link flexible manipulator is derived through the assumed modes method associated with Lagrange approach. For simplicity, a linearized system model would be analyzed and investigated for our tracking control design with elimination of tip vibration. That is to say, the proposed backstepping controller is not only to stabilize the flexible robotic manipulator, but also to drive the trajectory tracking error and tip-deflection to converge to zero asymptotically. Furthermore, some simulation results are given to illustrate the excellent performance of the backstepping control design applied to a single-link flexible robotic manipulator.
Linearity and efficiency enhancement strategies for 4G wireless power amplifier designs

Next generation wireless transmitters will rely on highly integrated silicon-based solutions to realize the cost and performance goals of the 4G market. This will require increased use of digital compensation techniques and innovative circuit approaches to maximize power and efficiency and minimize linearity degradation. This paper summarizes the circuit and system strategies being developed to meet these aggressive performance goals.
Cell adhesion and proliferation on RGD-modified recombinant spider silk proteins.

Due to the biocompatibility and biodegradability as well as the mechanical properties of the fibers, spider silk has become an attractive material for researchers regarding biomedical applications. In this study, the engineered recombinant spider silk protein eADF4(C16) was modified with the integrin recognition sequence RGD by a genetic (fusing the amino acid sequence GRGDSPG) as well as a chemical approach (using the cyclic peptide c(RGDfK)). Both modified silk proteins were processed into films, and thereafter characterized concerning secondary structure, water contact angle and surface roughness. No influence of the RGD-modifications on any of these film properties could be detected. However, attachment and proliferation of BALB/3T3 mouse fibroblasts were significantly improved on films made of the RGD-modified silk proteins. Interestingly, the genetically created hybrid protein (with a linear RGD sequence) showed similar or slightly better cell adhesion properties as the silk protein chemically modified with the cyclic RGD peptide.
Factors influencing participation in a population-based biorepository for childhood heart disease.

BACKGROUND
Consenting minors for genetics research and biobanking involves ethical and social challenges. We examined factors influencing participation rates in a population-based biorepository for childhood heart disease.


METHODS
Individuals were prospectively enrolled across 7 centers in Ontario by using a standardized consent form. Individuals were approached for consent for the donation of blood/saliva (DNA), tissue, and skin from the affected individual for future genomics and stem cell research. Consent rates were compared between pediatric and adult patients and factors affecting consent were analyzed by using multiple logistic regression analysis.


RESULTS
From 2008 to 2011, 3637 patients were approached. A total of 2717 pediatric patients consented (90% consent rate); mean age was 8.5 ± 5.8 years (57% male; 76% white). A total of 561 adult patients consented (92% consent rate, P = .071 versus pediatric). Factors associated with lower pediatric consent rates included younger age, race, absence of complex defects, and location of consent; these were not associated with adult consent rates. Leading causes for refusal of consent were lack of interest in research (43%), overwhelmed clinically (14%), and discomfort with genetics (11%). Concerns related to privacy, insurability, indefinite storage, and ongoing access to medical records were not the leading causes for refusal.


CONCLUSIONS
The high pediatric consent rate (90%) was comparable with that of adults. Ethical, social, or legal issues were not the leading reasons for refusal of consent.
Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes.

BACKGROUND
Many alcohol research questions require methods that take a person-centered approach because the interest is in finding heterogeneous groups of individuals, such as those who are susceptible to alcohol dependence and those who are not. A person-centered focus also is useful with longitudinal data to represent heterogeneity in developmental trajectories. In alcohol, drug, and mental health research the recognition of heterogeneity has led to theories of multiple developmental pathways.


METHODS
This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. Methods discussed include latent class analysis, latent transition analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. These methods are presented in a general latent variable modeling framework that expands traditional latent variable modeling by including not only continuous latent variables but also categorical latent variables.


RESULTS
Four examples that use the National Longitudinal Survey of Youth (NLSY) data are presented to illustrate latent class analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. Latent class analysis of antisocial behavior found four classes. Four heavy drinking trajectory classes were found. The relationship between the latent classes and background variables and consequences was studied.


CONCLUSIONS
Person-centered and variable-centered analyses typically have been seen as different activities that use different types of models and software. This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. The general framework makes it possible to combine these models and to study new models serving as a stimulus for asking research questions that have both person- and variable-centered aspects.
Perianal mucinous adenocarcinoma arising from chronic anorectal fistulae: a review from a single institution

Mucinous adenocarcinoma arising from a chronic anorectal fistula is rare, with few reports in the literature. Such lesions present in an innocuous manner and can be mistaken for the more common clinical condition of a benign perianal abscess or fistula. From our prospectively collected database, we identified 4 patients with chronic perianal inflammatory conditions who were subsequently found to have developed perianal mucinous adenocarcinoma on biopsy. We received the symptomatology, subsequent management and further follow-up of each patient. These patients were treated with radical surgery, with or without adjuvant therapy. A high index of clinical suspicion is required to make the diagnosis of perianal tumours while assessing patients presenting with perianal inflammatory conditions. Wide excision of the tumour with abdominoperineal resection is the surgical treatment of choice and can provide good long-term results in patients with localized disease.
What is the Velocity of the Electromagnetic Field

Abstract A new definition for the electromagnetic field velocity is proposed. The velocity depends on the physical fields. Translated by Marianna Euler and reprinted from Dopovidi of the Academy of Sciences of Ukraine N4, p.51-53 (1997)
Changes in the myosin heavy chain isoform profile of the triceps brachii muscle following 12 weeks of resistance training

The purpose of this investigation was to determine whether 12 weeks of resistance training, which increased arm girth (5%) and forearm extensor strength (39%), also altered the myosin heavy chain (MHC) characteristics of the triceps brachii muscle. Fifteen healthy, active men volunteered to participate under experimental (n = 11) or control (n = 4) conditions. The experimental group completed four sets of eight to 12 repetitions for each exercise (i.e. triceps pushdown, close grip bench press, triceps kickbacks and biceps curl) with loads of between 70–75% of one repetition maximum (1RM) three times a week. The inter-set and inter-exercise recovery period was only 90 s. Skeletal muscle tissue was removed from the triceps brachii muscle prior to (W0) and following 4 (W4), 8 (W8) and 12 (W12) weeks of the investigation. Samples were analysed for MHC isoform content using 6% sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). MHC isoform composition in the control group did not change significantly. However, the percentage of MHC type IIb isoform decreased significantly from W0 to W4 and again from W4 to W12 [W0: 39.7 (9.2); W12: 29.2 (8.2)%] in the experimental condition. The increments in MHC type IIa [W0: 34.0 (9.9); W12: 41.5 (10.4)] and type I [W0: 26.3 (7.9); W12: 29.3 (9.6)] isoforms were not significant for the experimental group. However, the effect size (ES) transformation of changes in types IIa MHC content was moderate (ES = 0.75). Changes in MHC isoform content were not significantly correlated with changes in 1RM strength for the triceps pushdown exercise. These data indicated that resistance training rapidly, and in an ongoing manner, changed the contractile protein profile of trained skeletal muscle. However, changes in MHC isoform composition in the first 12 weeks of training were not implicated in the development of 1RM triceps pushdown strength.
Performance Evaluation of IPv4 and IPv6 on Windows Vista and Linux Ubuntu

A small network of computing devices that started as ARPANET project in early 1980s is now a worldwide network of devices for billions of users. This global network, the Internet, has become an integral part of worldwide economy and life of individuals. Internet Protocol (IP) v4 is the basic building block of the Internet and has served well, but it has limitations that hinder its growth. The solution is IPv6, which addresses inherent problems of the earlier version. However, due to the increased overhead in IPv6 and its interaction with the operating system that hosts this communication protocol, there may be network performance issues. In this paper, two operating systems namely, Windows Vista and Linux Ubuntu are configured with the two versions of IP and empirically evaluated for performance difference. Performance related metrics like throughput, delay, jitter and CPU usage are empirically measured on a test-bed implementation. The results show that network performance depends not only on IP version and traffic type, but also on the choice of the operating system.
Outlier Detection on Semantic Space for Sentiment Analysis With Convolutional Neural Networks

Sentiment analysis is a text categorization problem that consists in automatically assigning text documents to pre- defined classes that represent sentiments or a positive/negative opinion about a subject. To solve this task, machine learning techniques can be used. However, in order to achieve good gen- eralization, these techniques require a thorough pre-processing and an apropriate data representation. To deal with these fundamental issues, this work proposes the use of convolutional neural networks and density-based clustering algorithms. The word representations used in this work were obtained from vectors previously trained in an unsupervised way, denominated word embeddings. These representations are able to capture syntactic and semantic information of words, which leads to similar words to be projected closer together in the semantic space. In this scenario, in order to improve the performance of the convolutional neural network, the use of a clustering algorithm in the semantic space to extract additional information from the data is proposed. A density-based clustering algorithm was used to detect and remove outliers from the documents to be classified before these documents were used to train the con- volutional neural network. We conducted experiments with two different embeddings across three datasets in order to validate the effectiveness of our method. Results show that removing outliers from documents is capable of slightly improving the accuracy of the model and reducing computational cost for the non-static training approach. (0)
Data-driven polynomial chaos expansion for machine learning regression

We present a regression technique for data-driven problems based on polynomial chaos expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ), where it is typically used to replace a runnable but expensive computational model subject to random inputs with an inexpensive-to-evaluate polynomial function. The metamodel obtained enables a reliable estimation of the statistics of the output, provided that a suitable probabilistic model of the input is available. Machine learning (ML) regression is a research field that focuses on providing purely datadriven input-output maps, with the focus on pointwise prediction accuracy. We show that a PCE metamodel purely trained on data can yield pointwise predictions whose accuracy is comparable to that of other ML regression models, such as neural networks and support vector machines. The comparisons are performed on benchmark datasets available from the literature. The methodology also enables the quantification of the output uncertainties, and is robust to noise. Furthermore, it enjoys additional desirable properties, such as good performance for small training sets and simplicity of construction, with only little parameter tuning required.
Ionospheric Scintillation Effects on GPS Receivers during Solar Minimum and Maximum

The ionosphere has practical importance in GPS (Global Positioning System) applications because it influences transionospheric radio wave propagation. Among various phenomena in the ionosphere, ionospheric scintillation is characterized by rapid fluctuation and fading of the received signal intensity due to electron density irregularity inside the ionosphere. Deep signal fading caused by scintillation can lead to loss of lock of the carrier tracking loop in GPS receivers. The aircraft navigation system based on GPS and WAAS (Wide Area Augmentation System) uses code and carrier phase data to create the position estimate. If a significant number of carrier tracking channels lose lock simultaneously, the system cannot provide service until reacquiring a sufficient number of channels. Although scintillation is not frequent in the mid-latitude region, it is a potential hazard in terms of availability and continuity of the service in the equatorial region [1]. This paper investigates scintillation effects on GPS receivers during solar minimum in the equatorial region. Among four different receivers used for data collection in Brazil, this paper primarily focuses on the scintillation effects on a certified WAAS receiver because it is currently used for aircraft navigation. A maximum of two simultaneous loss of phase locks was observed in 0.05% of epochs. With the given satellite geometry, two satellites loss in a small fraction of time was not a critical problem for navigation. This paper also analyzes solar maximum data collected at Ascension Island during the previous solar maximum period. More satellite channels were affected by scintillation compared to solar minimum, but the maximum number of simultaneous loss of lock in a software receiver was the same as the solar minimum case. One of the most important results of this paper is that instances of simultaneous loss of satellite data can be reduced by minimizing reacquisition time of GPS receivers. Although 20 seconds reacquisition time is allowed by WAAS MOPS (Minimum Operational Performance Standards) [2], this number should be reduced to guarantee enough availability during strong scintillation.
A Tree Based Routing Protocol for Mobile Sensor Networks (MSNs)

Wireless Sensor Networks (WSNs) has foreseen big changes in data gathering, processing and disseminating for monitoring specific applications such as emergency services, disaster management, and military applications etc. Wireless sensor networks (WSN’s) are application dependent. Wireless sensor network can be classified into Static Sensor Network (SSN) & Mobile Sensor Network (MSN). In Static Sensor Network, the sensor nodes localize only first time during deployment. In case of Mobile Sensor Network, nodes collect the data by moving from one place to another place hence localization is needed. Mobile sensor networks are more energy efficient, better targeting and provide more data fidelity than Static Sensor Network (SSN). Mobile Sensor networks have gained great attention in recent years due to their ability to offer economical and effective solutions in a variety of fields. There are many routing protocols present for the static sensor network. In this paper we have present a Tree based Routing Protocols (TBRP) for mobile sensor network. TBRP was compared with LEACH and TEEN Protocol. Simulation results show that TBRP outperforms LEACH and TEEN in terms of mobility, energy efficiency & network life
Geographic distributions and origins of human head lice (Pediculus humanus capitis) based on mitochondrial data.

Human head lice (Pediculus humanus capitis) are subdivided into 3 deeply divergent mitochondrial clades (Clades A, B, and C), each having unique geographical distributions. Determining the evolutionary history and geographic distribution of these mitochondrial clades can elucidate the evolutionary history of the lice as well as their human hosts. Previous data suggest that lice belonging to mitochondrial Clade B may have originated in North America or Asia; however, geographic sampling and sample sizes have been limited. With newly collected lice, we calculate the relative frequency, geographic distribution, and genetic diversity of louse mitochondrial clades to determine the geographic origin of lice belonging to Clade B. In agreement with previous studies, genetic diversity data support a North American origin of Clade B lice. It is likely that lice belonging to this mitochondrial clade recently migrated to other geographic localities, e.g., Europe and Australia, and, if not already present, may disperse further to occupy all geographic regions.
Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling

A systematic comparison of two basic types of neural network, static and dynamic, is presented in this study. Two backpropagation (BP) learning optimization algorithms, the standard BP and conjugate gradient (CG) method, are used for the static network, and the real-time recurrent learning (RTRL) algorithm is used for the dynamic-feedback network. Twenty-three storm-events, about 1632 rainfall and runoff data sets, of the Lan-Yang River in Taiwan are used to demonstrate the efficiency and practicability of the neural networks for one hour ahead streamflow forecasting. In a comparison of searching algorithms for a static network, the results show that the CG method is superior to the standard BP method in terms of the efficiency and effectiveness of the constructed network’s performance. For a comparison of the static neural network using the CG algorithm with the dynamic neural network using RTRL, the results show that (1) the static-feedforward neural network could produce satisfactory results only when there is a sufficient and adequate training data set, (2) the dynamic neural network generally could produce better and more stable flow forecasting than the static network, and (3) the RTRL algorithm helps to continually update the dynamic network for learning—this feature is especially important for the extraordinary time-varying characteristics of rainfall–runoff processes. q 2004 Elsevier B.V. All rights reserved.
The Agile Teaching/Learning Methodology and Its e-Learning Platform

The Agile Teaching/Learning Methodology (ATLM) is a teaching/learning methodology designed for higher-education based on the best practices and ideas from the field of software engineering and leveraging upon concepts from agile software methodologies. Although ATLM was designed using concepts borrowed from software engineering, the methodology itself can easily be applied to a wide variety of courses that might require agility in teaching and learning. This paper explains the objectives behind ATLM and the process architecture of the methodology. ATLM emphasizes agility, communication and the learning process. The paper also presents the e-learning platform we have developed to support this ATLM approach to teaching/learning and the technologies behind this platform. The ATLM e-learning platform makes use of a number of modern collaboration and knowledge sharing technologies such as blogging, commenting, instant messaging, wiki and XML RSS.
Least significant changes and monitoring time intervals for high-resolution pQCT-derived bone outcomes in postmenopausal women

BACKGROUND
Least Significant Change (LSC) assists in determining whether observed bone change is beyond measurement precision. Monitoring Time Interval (MTI) estimates time required to reliably detect skeletal changes. MTIs have not been defined for bone outcomes provided by high resolution peripheral quantitative computed tomography (HR-pQCT). The purpose of this study was to determine the LSCs and MTIs for HR-pQCT derived bone area, density and micro-architecture with postmenopausal women.


METHODS
Distal radius and tibia of 33 postmenopausal women (mean age: 77, SD: ±7 years), from the Saskatoon cohort of the Canadian Multicentre Osteoporosis Study (CaMos), were measured using HR-pQCT at baseline and 1-year later. We determined LSC from precision errors and divided them by the median annual percent changes to define MTIs for bone area, density, and micro-architecture.


RESULTS
Distal radius: HR-pQCT LSCs indicated a 1-8% observed change was needed for reliable monitoring of bone area and density while a 3-18% change was needed for micro-architectural measures. The longest MTIs (>3 years) pertained to cortical and trabecular area and density measures, cortical thickness and bone volume fraction; the shortest MTIs (~2 years) pertained to bone micro-architectural measures (trabecular number, thickness, separation and heterogeneity). Distal tibia: LSCs indicated a <1-5% observed change was needed for reliable monitoring of bone area and density, while a 3-19% change was needed for micro-architectural measures. The longest MTIs (>3 years) pertained to trabecular density, bone volume fraction, number, separation and heterogeneity; the shortest MTIs (~1 year) pertained to cortical and trabecular area, cortical density and thickness.


CONCLUSION
MTIs suggest that performing HR-pQCT follow-up measures in postmenopausal women every 2 years at the distal radius and every 1 year at the distal tibia to monitor true skeletal changes as indicated by the LSCs.