Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Analysis of Parental Involvement and Achievement in East Asian Countries

Countless meta-analytic studies document the relation between parental involvement and achievement, but they mostly include studies conducted in the United States where parental involvement is framed as a policy issue. This is the first meta-analytic study focusing on East Asian countries characterized by high achievement levels, a comparatively standardized education system, and no policy encouraging family–school relations. A meta-analysis of 15 studies retrieved from an exhaustive search of the literature reveals a positive association between parental involvement and achievement. The strength of the relation was highest for academic socialization, followed by home involvement and school involvement, similar to previous meta-analyses.
Language identification of individual words with joint sequence models

Abstract Within a multilingual automatic speech recognition (ASR) sys-tem, knowledge of the language of origin of unknown wordscan improve pronunciation modelling accuracy. This is of par-ticular importance for ASR systems required to deal with code-switched speech or proper names of foreign origin. For wordsthat occur in the language model, but do not occur in the pro-nunciation lexicon, text-based language identiﬁcation (T-LID)of a single word in isolation may be required. This is a chal-lenging task, especially for short words. We motivate for theimportance of accurate T-LID in speech processing systems andintroduce a novel way of applying Joint Sequence Models to theT-LID task. We obtain competitive results on a real-world 4-language task: for our best JSM system, an F-measure of 97:2%is obtained, compared to a F-measure of 95:2% obtained with astate-of-the-art Support Vector Machine (SVM). Index Terms: text-based language identiﬁcation, joint se-quence models, multilingual speech recognition
Persistent Homology for Path Planning in Uncertain Environments

We address the fundamental problem of goal-directed path planning in an uncertain environment represented as a probability (of occupancy) map. Most methods generally use a threshold to reduce the grayscale map to a binary map before applying off-the-shelf techniques to find the best path. This raises the somewhat ill-posed question, what is the right (optimal) value to threshold the map? We instead suggest a persistent homology approach to the problem-a topological approach in which we seek the homology class of trajectories that is most persistent for the given probability map. In other words, we want the class of trajectories that is free of obstacles over the largest range of threshold values. In order to make this problem tractable, we use homology in ℤ2 coefficients (instead of the standard ℤ coefficients), and describe how graph search-based algorithms can be used to find trajectories in different homology classes. Our simulation results demonstrate the efficiency and practical applicability of the algorithm proposed in this paper.paper.
Using the evidence base for social anxiety to understand and treat paranoia: a single case experimental design

Abstract Research suggests that paranoia and social anxiety can be understood as part of the same continuum, having shared processes such as the anticipation of threat, cognitive biases, poor self-concept, worry and safety-seeking behaviours. There is limited research on whether evidence-based interventions for social anxiety could be used with individuals who experience paranoia; however, an existing brief intervention study using techniques taken from cognitive behavioural therapy (CBT) for social anxiety has had promising results. This paper uses a single-case experimental design to explore whether using a clinical model of the maintenance of paranoia followed by CBT for social anxiety can be an effective formulation and intervention method in cases where social anxiety processes appear to be maintaining paranoid thoughts. This may be an effective formulation and intervention method, resulting in a reduction in anxiety and a reduction in the distress associated with paranoid thoughts. The clinical implications are discussed along with limitations and recommendations for further research. Key learning aims (1) To describe shared processes in social anxiety and paranoia. (2) To identify the benefits and limitations of using a clinical model of paranoia and CBT for social anxiety for formulation and intervention with individuals experiencing paranoia. (3) To identify areas where further research is warranted in treatment for individuals experiencing paranoia.
Online set packing and competitive scheduling of multi-part tasks

We consider a scenario where large data frames are broken into a few packets and transmitted over the network. Our focus is on a bottleneck router: the model assumes that in each time step, a set of packets (a burst) arrives, from which only one packet can be served, and all other packets are lost. A data frame is considered useful only if none of its constituent packets is lost, and otherwise it is worthless. We abstract the problem as a new type of online set packing, present a randomized distributed algorithm and a matching lower bound on the competitive ratio for any randomized online algorithm. Our bounds are expressed in terms of the maximal burst size and the maximal number of packets per frame. We also present refined bounds that depend on the uniformity of these parameters.
OBSERVATIONS ON SPERM PENETRATION IN THE RAT

The structural aspects of sperm penetration in the rat egg were investigated by electron microscopy. Eggs were recovered at intervals between 8 and 10:30 A.M. from females which had mated during the previous night. The oviducts were flushed with hyaluronidase and the eggs transferred into a 2 per cent osmium tetroxide solution, buffered at pH 7.8. After fixation, the eggs were mounted individually in agar, dehydrated in ethyl alcohol, and embedded in butyl-methyl methacrylate (3:1). The sperm penetrating the egg is covered by a plasma membrane which is present only on the side facing toward the zona pellucida; no membrane is visible on the side facing toward the vitellus. The sperm plasma membrane becomes continuous with the egg plasma membrane and forms a deep fold around the entering sperm. Cross-sections through the sperm midpiece in the perivitelline space show an intact plasma membrane. At the place of entrance, the plasma membrane of the sperm appears to fuse with the egg plasma membrane. After the sperm has penetrated the vitellus, it has no plasma membrane at all. The nuclear membrane is also absent. These observations suggest a new hypothesis for sperm penetration. After the sperm has come to lie on the plasma membrane of the egg, the egg and sperm plasma membranes rupture and then fuse with one another to form a continuous cell membrane over the egg and the outer surface of the sperm. As a result the sperm comes to lie inside the vitellus, leaving its own plasma membrane incorporated into the egg membrane at the surface of the egg.
Gain and noise ripple reduction in broadband Raman amplifiers using HNLF spectral broadening

The effects of using spectrally broadened pumps on the gain and noise ripple in broadband Raman amplifiers are investigated. Pump spectral broadening, achieved by initiating modulation instability in a highly nonlinear fiber (HNLF) using a continuous-wave input is shown to reduce the number of pumps required in addition to improving the flatness of the gain and noise profile. Optimal broadening of a pump spectrum in the E-and S-bands is achieved as a result of optimisation of the second order disperion and nonlinear coefficients of the HNLF. For a specific example of a 51-channel, C-band wavelength-division-multiplexed network employing a 50-km transmission fiber span, it is shown using simulation that spectrally broadened pumps improve the gain and noise ripple by 2 dB and 0.2 dB respectively. It is also shown that the number of pumps can be reduced by half when such a pumping scheme is used.
TWO SIMPLE APPROACHES TO PRICING CREDIT RISK AND CREDIT DERIVATIVES

Models and applications in pricing credit risk and credit derivatives are a growing industry. As a background, we provide an introduction to two simple approaches to pricing credit risk and credit derivatives, the stochastic credit spread and ratings migration matrix methods. We show how one might answer certain fundamental questions on credit rating stability (and by implication default), given assumptions regarding (Markov) rating transition probability matrices. Then we show the application of stochastic credit models in arriving at risk-neutral credit spreads, given leverage, defaulted loan recovery rates, and the volatility of the collateral (or firm issuing the debt).
Robust H∞ Filtering for LPV Discrete-Time State-Delayed Systems

This paper examines the problems of robust H∞ filtering design for linear parameter-varying discrete-time systems with time-varying state delay. We present parameter-dependent robust H∞ filters, which are derived using appropriately selected Lyapunov-Krasovskii functional. The resulting filters can be obtained from the solution of convex optimization problems in terms of parameterized linear matrix inequalities, which can be solved via efficient interior-point algorithms. The admissible filters guarantee a prescribed H∞ noise attenuation level, relating exogenous signals to the estimation error for all possible parameters that vary in compact sets. A numerical example illustrates the feasibility of the proposed methodologies. (Nature and Science, 2004,2(2):36-44)
Adoption of an IMF Programme and Debt Rescheduling. An empirical test of their relationship

The existence of an empirical relationship between the adoption of an IMF programme and the concession of a debt rescheduling by commercial and o¢cial creditors is tested using a bivariate probit model. If countries who have arrangements with the IMF are more likely than others to obtain a rescheduling of their external debt we could conclude that the adoption of an IMF programme could work as a sort of signal of a country’s “good willingness”, which is thus rewarded with the debt relief. The results con…rm the existence of a signi…cant e¤ect of the adoption of an IMF programme on the subsequent concession of a debt rescheduling by creditors.
Catecholamine-synthesizing enzymes in the rat adrenal gland during exposure to cold.

KVET&ANSK+, RICHARD, GEORGE P. EEWIRTZ, VIRGINIA K. WEISE, AND IRWIN J. KOPIN. CQtacholamine-synthesizing enzymes in the rat adrmal gland during exposuw to cold. Am. J, Physiol. 220(4) : 928-931. 1971 . -Rats were continuously exposed to a temperature of 3 C for 1, 7, 21, or 42 days. Adrenal epinephrine was decreased after I day but had returned to control levels at 7, 21, or 42 days. Adrenal norepinephrine was unchanged from controls after 1 day but increased after 7, 21, or 42 days. Levels of adrenal tyrosine hydroxylase were significantly increased after 7 or 21 days of exposure, but returned to control levels after 42 days. Dopamine-/3-hydroxylase levels were increased 1, 7, and 21 days after exposure; after 42 days the activity was less than that found after 21 days but higher than that in control animals. Adrenal phenylethanolamine-N-methyl transferase levels did not increase to the same extent as tyrosine hydroxylase or dopamine-p-hy.droxylase levels. Phenylethanolamine-N-methyl transferase was increased after 7 or 21 days of exposure but not after 42 days. The initial rise and subsequent return toward normal levels in adrenal enzyme levels follows previously reported patterns of urinary excretion of epinephrine and norepinephrine found during cold exposure and coincides with the reported development of increased sensitivity of the tissues to catecholamines.
Neural Network based indicative ECG classification

The Electrocardiogram (ECG) is undoubtedly the most used biological signal in the clinical world and it is a means for detection of several cardiac abnormalities. Pattern recognition, diagnostic classification of ECGs constitutes an interesting application of Artificial Neural Networks (ANNs). This paper illustrates the ability of a feed-forward back propagation using Neural Network for classify unknown ECG waveforms keen on one of the 4 discrete class. Out of the 4 classes, 3 of them correspond to abnormal ECG signals and 1 represents the healthy group. In addition, the Neural Network model developed has the option to categorize unknown ECG input signals as unclassified, since it represents an unknown pathology. Preliminary results are obtained using data from 4 different Physiobank ECG database.
Estimating species diversity in a guild of Neotropical skippers (Lepidoptera: Hesperiidae) with artificial lures is a sampling problem

Abstract. 1 Butterflies are frequently used to investigate Neotropical diversity, but the family Hesperiidae is almost never employed as a focal group. Sampling Hesperiidae with artificial lures has been used to assist in species richness estimates of males that use bird droppings as a resource, but its effectiveness for estimating total hesperiid site diversity is unknown. 2 This study characterises species richness and abundance in a diverse assemblage of Hesperiidae sampled at artificial lures. Sampled and estimated richness among taxonomic subsets were compared to the known site richness. These comparisons are used to assess artificial lures as a method for sampling total richness of Hesperiidae. We tested for potential differences in attraction in lures placed with and without the presence of army ant swarms. 3 Five years of intermittent sampling with lures recovered 65% of the known Hesperiidae species richness at the study site. We found the subfamilies were differentially attracted to lures, where 86% of Eudaminae, 56% of Hesperiinae and 55% of Pyrginae species known from the site were recovered by lures. Species accumulation and taxonomic diversity differed among lures placed alone, and those placed in the presence of army ants. 4 Our results suggest that sampling with artificial lures can be used to estimate diversity of Neotropical Eudaminae and a few tribes of Hesperiinae at some sites. Available evidence indicates that Hesperiidae feeding at bird droppings is predominately a Neotropical phenomenon. Finally, we conclude that estimating diversity in Neotropical Hesperiidae will require long‐term studies that use multiple sampling techniques.
Asymptotic dimension of a hyperbolic space and capacity dimension of its boundary at infinity

We introduce a quasi-symmetry invariant of a metric space Z called the capacity dimension, cdimZ. Our main result says that for a visual Gromov hyperbolic space X the asymptotic dimension of X is at most the capacity dimension of its boundary at infinity plus 1, asdimX ≤ cdim@1X + 1.
2.5-D poroelastic wave modelling in double porosity media

SUMMARY To approximate seismic wave propagation in double porosity media, the 2.5-D governing equations of poroelastic waves are developed and numerically solved. The equations are obtained by taking a Fourier transform in the strike or medium-invariant direction over all of the field quantities in the 3-D governing equations. The new memory variables from the Zener model are suggested as a way to represent the sum of the convolution integrals for both the solid particle velocity and the macroscopic fluid flux in the governing equations. By application of the memory equations, the field quantities at every time step need not be stored. However, this approximation allows just two Zener relaxation times to represent the very complex double porosity and dual permeability attenuation mechanism, and thus reduce the difficulty. The 2.5-D governing equations are numerically solved by a time-splitting method for the non-stiff parts and an explicit fourth-order Runge-Kutta method for the time integration and a Fourier pseudospectral staggered-grid for handling the spatial derivative terms. The 2.5-D solution has the advantage of producing a 3-D wavefield (point source) for a 2-D model but is much more computationally efficient than the full 3-D solution. As an illustrative example, we firstly show the computed 2.5-D wavefields in a homogeneous single porosity model for which we reformulated an analytic solution. Results for a two-layer, water-saturated double porosity model and a laterally heterogeneous double porosity structure are also presented.
Microarray Data Integration and Machine Learning Techniques For Lung Cancer Survival Prediction

The advent of high-throughput technologies such as gene microarrays and protein chips are currently revolutionizing biology and medicine. Machine learning techniques play a pivotal role in analyzing the generated data. Recent studies have reported on the successful application of supervised machine learning approaches to prediction of cancer subclasses, treatment outcome, and drug response. Novel diagnostic tools promise the development of patient-tailored cancer treatment. However, the major step towards individualized therapy is expected to use a combination of various data sources, e.g. transcriptional data, proteomic data, clinical data. In this paper, we have integrated two lung cancer data sets, the Harvard Data Set and the Michigan Data Set. We developed a relational database for the data management and data preprocessing. From the integrated data, we selected 211 lung cancer patients and their transcriptional profile of 3,588 selected genes. Using Kaplan-Meier analysis, we divided the patients into two survival risk groups that are motivated by clinical relevance with respect to survival prediction (5 year survival). We applied six machine learning techniques to predict the survival risk groups. Based on quantitative and qualitative evaluation criteria, we chose decision trees as the most relevant technique for this data set. The comparative study of six machine learning techniques and different subsets of the clinical and transcriptional data revealed that the best overall classification accuracy on unseen test data is obtained by an ensemble of decision trees using boosting (test set accuracy of 77.5%). Integrating clinical and transcriptional data results in a remarkably improved classification performance of the support vector machine. Integrating heterogeneous data did not improve the prediction accuracy of the decision trees (best prediction accuracy is achieved based on anamnestic data alone), but led to the identification of a set of eight potential marker genes that are most important for lung cancer outcome prediction. All these eight genes have been previously reported as marker genes in malignant neoplasia, e.g., breast cancer, glioblastomas, pancreatic cancer, and fibrosarcoma.
Predictive motion planning for AUVs subject to strong time-varying currents and forecasting uncertainties

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A*-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A* approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.
Benchmarking ISPs in New Zealand

Measuring quality of Internet access is important because it provides rich information on capability of Internet services and user experience. As Internet services actively evolve, users tend to spend more time and require more network capacity over time. To meet the needs of consumers, there is a wide mix of ISPs and access technologies offered in New Zealand. Benchmarking ISPs in New Zealand for their quality of service enables us to predict what users may experience among different ISPs. In this paper, we present a study of broadband service performance in New Zealand ISPs. Our results will provide accurate information on the quality of service users experience from New Zealand ISPs and helps both ISPs and Government to understand current Internet service market.
Torque ripple minimization of SRM using torque sharing function and hysteresis current controller

Switched Reluctance Motor (SRM) is a type of synchronous AC machine that converts the reluctance torque into mechanical power. Torque pulsations are inherent in SRM due to the doubly salient structure of the machine. The SRM offers similar overall efficiency to an induction motor of the same rating since the windage and friction losses are comparable. Indirect torque control methods using Torque Sharing Functions (TSF) is applied for the minimization of the torque ripple in switched reluctance motor. The linear and cosine functions are applied to produce the torque references. The hysteresis current controller is used to generate the gate pulses for the asymmetric converter. This paper provides the comparative analysis of the simulation results of the closed loop control of the switched reluctance motor with hysteresis controller using the linear and cosine torque sharing functions.
The Importance of Oil to an Export Dependent Economy: The Case of Nigeria

The paper examines the influence of oil on an export dependent, developing economy, Nigeria. There are mixed findings in the academic literature on the effect of oil prices on exporting nations and the paper contributes to understanding these by including a measure of price uncertainty simultaneously with the level. A vector auto-regressive model includes variables measuring the foreign reserve account, exchange rate and (a proxy for) the interest rate in an examination of gross domestic product.
Estimating Symbiont Abundances and Gill Surface Areas in Specimens of the Hydrothermal Vent Mussel Bathymodiolus puteoserpentis Maintained in Pressure Vessels

The hydrothermal vent mussel Bathymodiolus puteoserpentis hosts gill-associated sulfur- and methane-oxidizing bacteria which sustain host nutrition and allow it to reach high densities at various sites along the northern Mid-Atlantic Ridge. Previous studies have demonstrated that in similar dual symbioses, relative abundances of each bacterial type could change following variations in symbiont substrate availabilities. In this study, pressurized recovery and incubations in pressure vessels were used to test whether B. puteoserpentis symbionts displayed similar behavior in the presence of symbiont substrates. The relative abundances of both types of symbionts were analyzed using fluorescence in situ hybridization (FISH) and group-specific gene copy numbers were assessed using qPCR. Specimens sampled using isobaric and non-isobaric recovery contained similar relative proportions (in surface coverage) of sulfur- and methane-oxidizing bacteria indicating that recovery type did not have impact on measured relative areas. Similarly, pressurized incubations with different substrates did lead to significant differences in the relative surface coverage of the two types of bacteria, although slight variations were measured with qPCR, suggesting changes in relative abundances of gene copy numbers but not in relative areas covered. Total gill surface areas and total bacterial numbers in specimens were estimated for the first time. Symbiont bearing-mussels display exchange surfaces about 20-fold higher than those found in similar-sized coastal mussels, and mean bacterial numbers of 2.5*1012 per specimen were estimated. This emphasizes that symbiotic mussels are a major reservoir of bacteria in vent ecosystems.