Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Practical Usage of a Domain Adaptation Algorithm in the Early Hours of a Disaster

Many machine learning techniques have been proposed to reduce the information overload in social media data during an emergency situation. Among such techniques, domain adaptation approaches present greater potential as compared to supervised algorithms because they don’t require labeled data from the current disaster for training. However, the use of domain adaptation approaches in practice is sporadic at best. One reason is that domain adaptation algorithms have parameters that need to be tuned using labeled data from the target disaster, which is presumably not available. To address this limitation, we perform a study on one domain adaptation approach with the goal of understanding how much source data is needed to obtain good performance in a practical situation, and what parameter values of the approach give overall good performance. The results of our study provide useful insights into the practical application of domain adaptation algorithms in real crisis situations.
A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package

A basic requirement in virtual environments is the tracking of objects, especially humans. A real time motion-tracking system was presented and evaluated in this paper. System sensors were built using tri-axis microelectromechanical accelerometers, rate gyros, and magnetometers. A Kalman-based fusion algorithm was applied to obtain dynamic orientations and further positions of segments of the subject's body. The system with the proposed algorithm was evaluated via dynamically measuring Euler orientation and comparing with other two conventional methods. An arm motion experiment was demonstrated using the developed system and algorithm. The results validated the effectiveness of the proposed method.
Visualization and analysis of student enrollment patterns in foundational engineering courses

The literature in engineering education and higher education has examined the implications of course-taking patterns onstudent development and success. However, little work has analyzed the trajectories of students who need to retake coursesin the curriculum, especially those deemed to be fundamental to a student’s program of study, or sequences of courses.Sequence analysis in R was used to leverage historical transcript data from institutional research at a large, public, Land-Grant university to visualize student trajectories within individual courses—with attention to those who re-enrolled incourses—and the pathways students took through a sequence of courses. This investigation considered students enrolled inintroductory mechanics courses that are foundational for several engineering majors: Statics, Dynamics, and Strength ofMaterials (also called Mechanics of Deformable Bodies). This paper presents alluvial diagrams of the course-takingsequences and transition matrices for the different possible grades received upon subsequent attempts for the Mechanicscore courses to demonstrate how visualizing students’ paths through sequences of classes by leveraging institutional datacan identify patterns that might warrant programs to reconsider their curricular policies.
Regulation of Seed Germination by MFT, MEE26, and CYP89A2 in Arabidopsis thaliana

Germination is the process by which quiescent seeds transition to seedlings. To optimize germination over time, seeds enter a dormant state, which is the inability of a complete viable seed to go through germination even in optimal conditions. In many plant species, seed dormancy can be broken by prolonged storage (after-ripening) or imbibition at low temperatures (stratification). Germination can be affected by several factors such as light. Plants perceive light through a group of photoreceptors that detect Red (R) and Far-red (FR) light. R light such as sunlight promotes germination, however, FR light (shade light found under the canopy) inhibits germination. Light also affects the signalling of phytohormones such as Abscisic Acid (ABA) and Gibberellic Acid (GA). A high ABA/GA ratio is known to be involved in inducing and maintaining seed dormancy. The gene MOTHER-OF-FT-AND-TFL1 (MFT) also affects germination. MFT has been previously shown to promote primary dormancy in Arabidopsis. In this thesis, the function of MFT in after-ripened seeds was investigated and the results indicate that it also promotes dormancy in after-ripened Arabidopsis seeds treated with FR light. This is consistent with the fact that MFT expression has been previously shown to be promoted by FR light. The role of two other genes in the regulation of seed dormancy and germination in both freshly harvested and after-ripened seeds was investigated. They are Cytochrome P450 89A2 (CYP89A2) and Maternal Effect Embryo Arrest 26 (MEE26), which are both regulated by MFT. Expression of CYP89A2 was up-regulated by FR light. Though it is promoted by MFT, phenotypic analysis revealed that freshly harvested seeds of CYP89A2-overexpressor (oe) lines promote germination. This is also the case for after-ripened seeds of CYP89A2oe lines treated with FR light. MEE26 is also promoted by FR light. MEE26oe and silenced lines indicate that it promotes dormancy in freshly harvested seeds and represses germination in after-ripened seeds treated with FR, even though MEE26 is strongly repressed by MFT. This work therefore provides new insight into the function of these genes and shows that the regulation of germination is a complex interaction of different factors.
Parámetros cefalométricos para determinar biotipo facial en adultos chilenos.

Objective: The aim of this study was to determine cephalometric standards related to facial type in a eugnathic adult Chilean population and to compare them with the classical cephalometric parameters. Methods: Cross-sectional study. Ninety six eugnathic young Chilean adults (between 18 and 35 years old) were included in this study. Lateral digital standardized cephalometric radiographs were used. The cephalometric analysis used Quick-Ceph 2000® software. Results: The mean value of the gonial angle was 121.96o, the Biörk-Jarabak ratio was 67.11%, SN-GoGn angle was 32.65o, interbasal angle (Schwarz) was 23.8o, Ricketts VERT was 2.33 and Ricketts lower facial height 44.1o. There was a significant difference between men and women for the Biörk-Jarabak ratio, SN-GoGn, interbasal angle and Ricketts VERT. Conclusion: Cephalometric standards obtained are similar to those established by the original authors, except for the BjörkJarabak ratio and Ricketts lower facial height. However, there is a significative sexual dimorphism that should be considered when making a patient ́s diagnosis.
Frequency‐dependent facilitation of synaptic throughput via postsynaptic NMDA receptors in the nucleus of the solitary tract

Hindbrain NMDA receptors play important roles in reflexive and behavioural responses to vagal activation. NMDA receptors have also been shown to contribute to the synaptic responses of neurons in the nucleus of the solitary tract (NTS), but their exact role remains unclear. In this study we used whole cell patch‐clamping techniques in rat horizontal brain slice to investigate the role of NMDA receptors in the fidelity of transmission across solitary tract afferent–NTS neuron synapses. Results show that NMDA receptors contribute up to 70% of the charge transferred across the synapse at high (>5 Hz) firing rates, but have little contribution at lower firing frequencies. Results also show that NMDA receptors critically contribute to the fidelity of transmission across these synapses during high frequency (>5 Hz) afferent discharge rates. This novel role of NMDA receptors may explain in part how primary visceral afferents, including vagal afferents, can maintain fidelity of transmission across a broad range of firing frequencies.
Reduced-Order Observer Design for Small-Signal Multi-Machine Power System Stability Improvement by Optimal Control

In this paper, we present the design of an observer of reduced-order used, in cascade, with optimal control to improve the small-signal stability of a multi-machine power system. The limitation of the measurement of all state variables of the system requires the design of reduced-order observer to reconstruct the unavailable part of the state variable vector. The non linear system is first linearized, and then a state space model is constructed. The optimal control is calculated with an interesting method which is briefly presented. The main construction of the observer is illustrated and the observer gain is calculated with the minimization of a quadratic index.
TRPA1 Modulates Mechanotransduction in Cutaneous Sensory Neurons

Transient receptor potential ankyrin 1 (TRPA1) is expressed by nociceptive neurons of the dorsal root ganglia (DRGs) and trigeminal ganglia, but its roles in cold and mechanotransduction are controversial. To determine the contribution of TRPA1 to cold and mechanotransduction in cutaneous primary afferent terminals, we used the ex vivo skin–nerve preparation from Trpa1+/+, Trpa1+/−, and Trpa1−/− adult mouse littermates. Cutaneous fibers from TRPA1-deficient mice showed no deficits in acute cold sensitivity, but they displayed striking deficits in mechanical response properties. C-fiber nociceptors from Trpa1−/− mice exhibited action potential firing rates 50% lower than those in wild-type C-fibers across a wide range of force intensities. Aδ-fiber mechanonociceptors also had reduced firing, but only at high intensity forces (>100 mN). Surprisingly, the firing rates of low-threshold Aβ and D-hair mechanoreceptive fibers were also altered. TRPA1 protein and mRNA expression was assessed in DRG neurons and cutaneous innervation by using Trpa1 in situ hybridization, an antibody for TRPA1, and an antibody for placental alkaline phosphatase (PLAP) in mice in which PLAP was substituted for Trpa1. DRG neurons of all sizes expressed Trpa1 mRNA or PLAP immunoreactivity. TRPA1 or PLAP immunolabeling was detected not only on many thin-caliber axons and intraepidermal endings but also on many large-caliber axons as well as lanceolate and Meissner endings. Epidermal and hair follicle keratinocytes also express TRPA1 message and protein. We propose that TRPA1 modulates mechanotransduction via a cell-autonomous mechanism in nociceptor terminals and possibly through a modulatory role in keratinocytes, which may interact with sensory terminals to modify their mechanical firing properties.
Multiple object decomposition based on independent component analysis of multi-energy x-ray projections

X-ray projection is not effective for representing complex overlapping objects. This paper presents a novel computational framework to decompose X-ray projections into multiple images with non-overlapping objects that are differentiated by their own material compositions. Based on energy-dependent X-ray attenuation characteristics for each material, multiple energy X-ray images are analyzed to obtain material-selective images, which correspond to projections of basis materials that constitute objects. We show that material-selective images can be considered as linear mixtures of independent components that are associated with object-selective images. As a result, multiple objects can be decomposed by independent component analysis (ICA) of material-selective images or ICA of multiple monochromatic energy X-ray images. To demonstrate the concept of the proposed method, we apply it to simulated images based on a 3-D human model.
Efficient Multicore Collaborative Filtering

This paper describes the solution method taken by LeBuSiShu team for track1 in ACM KDD CUP 2011 contest (resulting in the 5th place). We identied two main challenges: the unique item taxonomy characteristics as well as the large data set size. To handle the item taxonomy, we present a novel method called Matrix Factorization Item Taxonomy Regularization (MFITR). MFITR obtained the 2nd best prediction result out of more then ten implemented algorithms. For rapidly computing multiple solutions of various algorithms, we have implemented an open source parallel collaborative ltering library on top of the GraphLab machine learning framework. We report some preliminary performance results obtained using the BlackLight supercomputer.
Astrometric-Spectroscopic Determination of the Absolute Masses of the HgMn Binary Star ϕ Herculis

The mercury-manganese star ϕ Her is a well-known spectroscopic binary that has been the subject of a recent study by Zavala and coworkers in which they resolved the companion using long-baseline interferometry. The total mass of the binary is now fairly well established, but the combination of spectroscopy with astrometry has not resulted in individual masses consistent with the spectral types of the components. The motion of the center of light of ϕ Her was clearly detected by the Hipparcos satellite. Here we make use of the Hipparcos intermediate data ("abscissa residuals") and show that by combining them in an optimal fashion with the interferometry the individual masses can be obtained reliably using only astrometry. We reexamine and then incorporate existing radial velocity measurements into the orbital solution, obtaining improved masses of 3.05 ± 0.24 and 1.614 ± 0.066 M⊙ that are consistent with the theoretical mass-luminosity relation from recent stellar evolution models. These mass determinations provide important information for the understanding of the nature of this peculiar class of stars.
Ultrasound-based movement sensing, gesture-, and context-recognition

We propose an activity and context recognition method where the user carries a neck-worn receiver comprising a microphone, and small speakers on his wrists that generate ultrasounds. The system recognizes gestures on the basis of the volume of the received sound and the Doppler effect. The former indicates the distance between the neck and wrists, and the later indicates the speed of motions. Thus, our approach substitutes the wired or wireless communication typically required in body area motion sensing networks by ultrasounds. Our system also recognizes the place where the user is in and the people who are near the user by ID signals generated from speakers placed in rooms and on people. The strength of the approach is that, for offline recognition, a simple audio recorder can be used for the receiver. We evaluate the approach in one scenario on nine gestures/activities with 10 users. Evaluation results confirmed that when there was no environmental sound generated from other people, the recognition rate was 87% on average. When there was environmental sound generated from other people, we compare approach ultrasound-based recognition which uses only the feature value of ultrasound against standard approach, which uses feature value of ultrasound and environmental sound. Results for the proposed approach are 65%, for the standard approach are 57%.
Impella 5.0® as bridge-to-recovery short-term mechanical circulatory support after LVAD explantation.

A 56-year-old male presented in critical state of heart failure treated by an extracorporeal life support system (ECLS). He was transferred to our hospital for evaluation of left ventricular assist device (LVAD) implantation. Echocardiographic findings showed a severely impaired left ventricular function with an ejection fraction (EF) of 15% and normal right ventricular function.
Advancing the Understanding of Brain Function with Multivariate Pattern Analysis

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more exible, more reliable, and more sensitive than standard univariate analysis. Drawing on the eld of statistical learning theory, these new multivariate pattern analysis (MVPA) techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classi cation analyses of fMRI data. This in turn prevents the adoption of these methods by a large number of research groups to fully assess their potential with respect to cognitive neuroscience research. Here, a novel, Python-based, cross-platform, and open-source software framework, called PyMVPA, for the application of multivariate pattern analysis techniques to fMRI datasets is introduced. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. The framework is presented in this thesis, and illustrative examples on its usage, features, and programmability are provided. In addition, this thesis provides an overview of promising strategies for the application of MVPA to neuroimaging datasets. While various possibilities are reviewed based on previously published studies, the primary focus lies on the sensitivity analysis technique that is shown to provide interesting additional information which are readily available as part of any typical MVPA-based study. Moreover, this technique is eminently suited for modality-independent data analysis, a feature that is demonstrated by an example of a uniform analysis of datasets from four di erent brain imaging domains. The thesis concludes with a discussion about the challenges that have to be faced to establish MVPA as a standard analysis procedure, including the statistical evaluation of results, as well as potential pitfalls in their interpretation.
HEALTH FACILITY FACTORS AND INDIVIDUAL FACTORS AS PREDICTORS OF UPTAKE OF SKILLED BIRTH SERVICES IN LURAMBI SUB COUNTY, KENYA.

Victor Emali Mukaka 1 and Beatrice Mukabana 2 . 1. Department of Clinical nursing and health informatics, Masinde Muliro University of science and technology. 2. Department of Reproductive Health, Masinde Muliro University of science and technology. Manuscript Info Abstract . Manuscript History Received: 10 January 2019 Final Accepted: 12 February 2019 Published: March 2019
Penalized-Distance Volumetric Skeleton Algorithm

Introduces a refined general definition of a skeleton that is based on a penalized distance function and that cannot create any of the degenerate cases of the earlier CEASAR (Center-line Extraction Algorithm-Smooth, Accurate and Robust) and TEASAR (Tree-structure Extraction Algorithm for Skeletons-Accurate and Robust) algorithms. Additionally, we provide an algorithm that finds the skeleton accurately and rapidly. Our solution is fully automatic, which frees the user from having to engage in manual data pre-processing. We present the accurate skeletons computed on a number of test data sets. The algorithm is very efficient, as demonstrated by the running times, which were all below seven minutes.
Central Visual Persistences: II. Effects of Hand and Head Rotations

In an earlier paper, kinesthetic effects on central visual persistences (CPs) were reported, including the ability to move these images by hand following eye closure. While all CPs could be translated anywhere within the frontal field, the present report documents a more selective influence of manual rotations on CPs in the same subjects. When common objects or figures drawn on cards were rotated (while holding one end of the object or one corner of a card between thumb and forefinger), it was found that CPs of larger objects rotated with the hand. By contrast, CPs of smaller objects, parts of objects, and textures remained stable in space as the hand rotated. It is proposed that CPs of smaller stimuli and textures are represented mainly by the ventral stream (temporal cortex) while larger CPs, which rotate, are represented mainly by the dorsal stream (parietal cortex). A second discovery was that CPs of small objects (but not of line segments or textures) could be rotated when the thumb and fingers surrounded the edges of the object. It is proposed that neuronal convergence of visual and tactile information about shape increases parietal responses to small objects, so that their CPs will rotate. Experiments with CPs offer new tools to infer visual coding differences between ventral and dorsal streams in man.
A hybrid metaheuristic using a corrected formulation for the Traveling Car Renter Salesman Problem

The Traveling Car Renter Problem (CaRS) is a generalization of the Traveling Salesman Problem. This paper presents a hybrid metaheuristic approach to deal with CaRS: an evolutionary algorithm (ScA) and the hybrid method Adaptive Local Search Procedure (ALSP), denoted by ScA+ALSP. A mixed integer programming model proposed for CaRS is corrected and used within the ALSP. The results of experimental studies using a suite of 21 instances taken from the literature indicated that the hybrid ScA+ALSP is competitive regarding the best known algorithm in literature for non-Euclidean CaRS instances. Three new best results are reported.
Clustering the Feature Space

In this paper we propose and test the use of hierarchical clustering for feature selection in databases. The clustering method is Ward’s with a distance measure based on Goodman-Kruskal τ . We motivate the choice of this measure and compare it with other ones. Our hierarchical clustering is applied to over 40 data-sets from UCI archive. The proposed approach is interesting from many viewpoints. First, it produces the feature subsets dendrogram which serves as a valuable tool to study relevance relationships among features. Secondarily, the dendrogram is used in a feature selection algorithm to select the best features by a wrapper method. Experiments were run with three different families of classifiers: Naive Bayes, decision trees and k nearest neighbours. Our method allows all the three classifiers to generally outperform their corresponding ones without feature selection. We compare our feature selection with other state-of-the-art methods, obtaining on average a better classification accuracy, though obtaining a lower reduction in the number of features. Moreover, differently from other approaches for feature selection, our method does not require any parameter tuning.