Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between subjective actual sleep duration, subjective sleep need, age, body mass index, and gender in a large sample of young adults

Background Poor sleep is a major health concern, and there is evidence that young adults are at increased risk of suffering from poor sleep. There is also evidence that sleep duration can vary as a function of gender and body mass index (BMI). We sought to replicate these findings in a large sample of young adults, and also tested the hypothesis that a smaller gap between subjective sleep duration and subjective sleep need is associated with a greater feeling of being restored. Methods A total of 2,929 university students (mean age 23.24±3.13 years, 69.1% female) took part in an Internet-based survey. They answered questions related to demographics and subjective sleep patterns. Results We found no gender differences in subjective sleep duration, subjective sleep need, BMI, age, or feeling of being restored. Nonlinear associations were observed between subjective sleep duration, BMI, and feeling of being restored. Moreover, a larger discrepancy between subjective actual sleep duration and subjective sleep need was associated with a lower feeling of being restored. Conclusion The present pattern of results from a large sample of young adults suggests that males and females do not differ with respect to subjective sleep duration, BMI, or feeling of being restored. Moreover, nonlinear correlations seemed to provide a more accurate reflection of the relationship between subjective sleep and demographic variables.
Is Affective Intentionality Necessarily Irrelevant in Social Cognition

The aim of this work is to understand the meaning and the extent of “affective intentionality”, to discover whether or not it is analogous to other concepts of intentionality and if it can play a role in social cognition. I will compare Searle’s conception of intentionality, in particular affective intentionality, with Scheler’s concept of sympathy. The reason for this is that I believe the comparison shows that it is not always necessary to presuppose something to have affective intentionality.
Implementation of Parallel BACON-MVV method based on Data Decomposition in Intrusion Detection System

In Computer Security area, Intrusion Detection System (IDS) plays important role in detecting any kinds of network attacks. Denial of Service (DoS) and Probing attacks are common detectable intrusions that are frightened by most network users since the final result of these attacks is collapsing the network. Our previous research has proposed a robust statistical method, the BACON-MVV method, that provides 100% accuracy in detecting patterns of DoS and Probing attacks, inspite of the training sets used contains suspicious packet traffic called outliers. One problem not yet being addressed by previous research was the processing time taken as the packet traffics to be analysed for detecting any intrusion grows bigger. In this paper, we propose a Parallel BACON-MVV method based on Data Decomposition to be implemented in IDS. Experiment using our own generated simulation datasets shows that this proposed method runs significantly faster than its serial version.
Time Series Classification Using Locality Preserving Projections

The time series is generally of high dimensionality and classifying in such a high dimensional space is often infeasible due to the curse of dimensionality. We propose a new time series classifying method, which aims to classify the time series into different classes. By using locality preserving projections (LPP), the time series can be projected into a lower-dimensional space in which the time series related to the same class are close to each other, the time series in testing set can be identified by one-nearest-neighbor classifier in the lower-dimensional space. Extensive experimental evaluations are performed on 20 time series datasets, which come from diverse fields, including medicine, biometrics, astronomy and industry. The experiment results demonstrate the effectiveness of our approach.
A Mosaic of Functional Kainate Receptors in Hippocampal Interneurons

Although some physiological functions of kainate receptors (KARs) still remain unclear, recent advances have highlighted a role in synaptic physiology. In hippocampal slices, kainate depresses GABA-mediated synaptic inhibition and increases the firing rate of interneurons. However, the sensitivity to agonists of these responses differs, suggesting that the presynaptic and somatic KARs have a distinct molecular composition. Hippocampal interneurons express several distinct KAR subunits that can assemble into heteromeric receptors with a variety of pharmacological properties and that, in principle, could fulfill different roles. To address which receptor types mediate each of the effects of kainate in interneurons, we used new compounds and mice deficient for specific KAR subunits. In a recombinant assay, 5-carboxyl-2,4-di-benzamido-benzoic acid (NS3763) acted exclusively on homomeric glutamate receptor subunit 5 (GluR5), whereas 3S,4aR,6S,8aR-6-((4-carboxyphenyl)methyl) 1,2,3,4,4a,5,6,7,8,8a-decahydroisoquinoline-3-carboxylic acid (LY382884) antagonized homomeric GluR5 and any heteromeric combination containing GluR5 subunits. In hippocampal slices, LY382884, but not NS3763, was able to prevent kainate-induced depression of evoked IPSC. In contrast, neither prevented the concomitant increase in spontaneous IPSC frequency. The selectivity of these compounds was seen additionally in knock-out mice, such that they were inactive in GluR5-/- mice but completely effective in GluR6-/- mice. Our data indicate that in wild-type mice, CA1 interneurons express heteromeric GluR6 -KA2 receptors in their somatic compartments and GluR5-GluR6 or GluR5-KA2 at presynaptic terminals. However, functional compensation appears to take place in the null mutants, a new pharmacological profile emerging more compatible with the activity of homomeric receptors in both compartments: GluR5 in GluR6-/- mice and GluR6 in GluR5-/- mice.
Learning with Locally Linear Feature Regularization

Machine learning models are successfully being used for problems in language, vision, and biology that have millions or tens of millions of features. A common approach to alleviating the complexity of high dimensional feature spaces is to penalize the L1 or L2 norm of the parameter vector. We may be able to design more effective regularizers, though, if we possess external information about which features should behave similarly. For example, word co-occurrence statistics or thesauri such as Wordnet can indicate similarities which are useful for predicting the topic or sentiment of a document, and biological databases of gene pathways give similarities of genes which can predict disease based on gene expression levels. We present a simple framework in which similarities between features are encoded as a graph on features and a regression model is learned whose feature coefficients are similar for neighboring nodes. Our regularization criterion is closely related to locally linear embedding, a method for learning low dimensional embeddings of unlabeled, highdimensional data [1]. Because of this, we name it locally linear feature regularization (LLFR).
Economic bonding, auditor safeguard and audit quality: peer review evidence from individual auditors

In this paper, we examine whether the economic bond between an individual engagement partner and client threatens auditor independence and thus audit quality. We also investigate if auditor’s good economic position acts as a safeguard against this economic bond. Using a sample of peer reviewed individual audit engagements of 264 Finnish auditors we examine whether the client’s economic size is likely to affect audit quality as measured by the degree of compliance with audit standards (peer review). Furthermore, using taxable earned (salaries) and unearned (capital gains) income information of auditors we investigate if high income level of an auditor diminishes the risk of financial self-interest threat. Our results provide evidence that Finnish auditors show a very high independence although big client’s economic size (turnover) weakly affects audit quality. There is weak evidence that auditors are likely to show higher quality for larger clients. Our results also suggest that this financial self-interest threat can be safeguarded by auditor’s high unearned income level diluting the economic bond with the client. Unearned income acts here as a proxy for economic wealth.
TITLE : Performance Analysis of TCP Enhancements for WWW Traffic using UBR + with Limited Buffers over Satellite Links

This contribution presents the simulation results on the performance of TCP enhancements over ATM-UBR+ for satellite latencies with limited switch buffers for WWW traffic. We analyze the impact of Fast Retransmit and Recovery (FRR or Reno), New Reno, Selective Acknowledgement (SACK), Early packet drop (EPD), and Selective Drop on LEO, MEO, and GEO satellite configurations for several buffer sizes. NOTICE: This document has been prepared to assist the ATM Forum. It is offered as a basis for discussion and is not binding on the contributing organization, or on any other member organizations. The material in this document is subject to change in form and content after further study. The contributing organization reserves the right to add, amend or withdraw material contained herein.
Natural Deep Eutectic Solvents Nades As A Tool For

Deep Eutectic SolventsIonic Liquids in Separation TechnologyThermal Analysis of PolymersDeep Eutectic SolventsBio-Based SolventsRecent Advancements in Biofuels and Bioenergy UtilizationDeep Eutectic Solvents for Pretreatment of Lignocellulosic BiomassPolymer ReactionsThe Application of Green Solvents in Separation ProcessesRheological Methods in Food Process EngineeringEutectic SolventsGreen Food Processing TechniquesEnhancing Extraction Processes in the Food IndustryProperties of Liquids and SolutionsBiomaterials and ApplicationsIonic Liquids Current State of the ArtPlant Based “Green Chemistry 2.0”Biopolymer ElectrolytesMicrowave-Assisted Sample Preparation for Trace Element DeterminationPercutaneous Penetration Enhancers Drug Penetration Into/Through the SkinApplication of Ionic Liquids in BiotechnologyGreen Extraction Techniques: Principles, Advances and ApplicationsViscosimetry of Polymers and PolyelectrolytesGreen Extraction of Natural ProductsCurrent Protocols in Food Analytical Chemistry SupplementNew Generation Green Solvents for Separation and Preconcentration of Organic and Inorganic SpeciesIonic Liquid Applications: Pharmaceuticals, Therapeutics, and BiotechnologyAdvances in the Domain of Environmental BiotechnologySolvent ExtractionNature's ChemicalsPreservation of CellsDeep Eutectic Solvents for Medicine, Gas Solubilization and Extraction of Natural SubstancesThin Layer Chromatography in PhytochemistryGreen Analytical ChemistryElectrochemical Capacitors: Fundamentals to ApplicationsLiquid-Phase ExtractionEutectic Solvents and Stress in PlantsNatural Products IsolationThe Hydrogen Bond and the Water MoleculeNatural Bioactive Compounds
Face Recognition and Gender Classification Using Features of Lips

This paper presents a novel approach for Face Recognition and Gender classification strategy using the features of lips. Here feature extraction is carried out by using Principal component analysis (PCA) and Gabor wavelet. The proposed algorithm converts the RGB image into the YCbCr color space to detect the skin regions in the facial image. But in order to detect facial features the color image is converted in to gray scale image. This method locates the lip region and the mouth region. The gender classification method classifies almost all the images with different image sizes. The best classification rate is achieved by using the methods given in this work. The whole idea is offering a simple, reliable and robust method for extracting features of lips for face recognition and gender identification. For recognition experiments we used face images of persons from different sets of the FERET and AR databases. Recognition experiments with the FERET database (containing photographs of persons) showed that our method can achieve maximal 97-98% first one recognition rate and 0.3-0.4% Equal Error Rate.
Optimization Problems in Financial Mathematics : Explicit Solutions for Di usion Models

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva. nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.
Lung Function and Risk of Type 2 Diabetes and Fatal and Nonfatal Major Coronary Heart Disease Events: Possible Associations With Inflammation

OBJECTIVE We prospectively examined the relationship between lung function and risk of type-2 diabetes and fatal and nonfatal coronary heart disease (CHD) events and investigated the hypothesis that inflammation may underlie these associations. RESEARCH DESIGN AND METHODS A prospective study of 4,434 men aged 40–59 years with no history of cardiovascular disease (CHD or stroke) or diabetes drawn from general practices in 24 British towns and followed up for 20 years. RESULTS There were 680 major CHD events (276 fatal, 404 nonfatal) and 256 incident type 2 diabetes during the 20 years follow-up. Forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) but not FEV1-to-FVC ratio were significantly and inversely associated with incident type 2 diabetes and fatal CHD events (not nonfatal events) after adjustment for age, potential confounders, and metabolic risk factors. The adjusted relative risk (RR) for type 2 diabetes (Quartile 1 vs. Quartile 4) were 1.59 (1.07–2.56) and 1.74 (1.16–2.61) for FVC and FEV1, respectively (P = 0.03 and P = 0.04 for trend). The corresponding RR for fatal CHD were 1.48 (1.00–2.21) and 1.81 (1.19–2.76) (P = 0.002 and P = 0.0003 for trend). Lung function was significantly and inversely associated with C-reactive protein and interleukin-6; the inverse associations with type 2 diabetes for FVC and FEV1 were attenuated after further adjustment for these factors (P = 0.14 and P = 0.11 for trend) but remained significant for fatal CHD (P = 0.03 and P = 0.01, respectively). CONCLUSIONS Restrictive rather than obstructive impairment of lung function is associated with incident type 2 diabetes (and fatal CHD) with both associations partially explained by traditional and metabolic risk factors and inflammation.
Advances on tracking of extended objects and group targets using random matrices

The task of tracking extended objects or (partly) unresolvable group targets raises new challenges for both data association and track maintenance. Due to limited sensor resolution capabilities, group targets (i. e., a number of closely spaced targets moving in a coordinated fashion) may show a similar detection pattern as extended objects, namely a varying number of detections whose spread is determined by both the statistical sensor errors as well as the physical extension of the group or extended object. Different tracking approaches treating these situations have been proposed where physical extension is represented by a random symmetric positive definite matrix. This paper discusses some results that should give deeper insight into behavior and performance analysis of these approaches. Further improvements are presented.
CLICK—topology-independent comparison of biomolecular 3D structures

Our server, CLICK: http://mspc.bii. a-star.edu. sg/click, is capable of superimposing the 3D structures of any pair of biomolecules (proteins, DNA, RNA, etc.). The server makes use of the Cartesian coordinates of the molecules with the option of using other structural features such as secondary structure, solvent accessible surface area and residue depth to guide the alignment. CLICK first looks for cliques of points (3–7 residues) that are structurally similar in the pair of structures to be aligned. Using these local similarities, a one-to-one equivalence is charted between the residues of the two structures. A least square fit then superimposes the two structures. Our method is especially powerful in establishing protein relationships by detecting similarities in structural subdomains, domains and topological variants. CLICK has been extensively benchmarked and compared with other popular methods for protein and RNA structural alignments. In most cases, CLICK alignments were statistically significantly better in terms of structure overlap. The method also recognizes conformational changes that may have occurred in structural domains or subdomains in one structure with respect to the other. For this purpose, the server produces complementary alignments to maximize the extent of detectable similarity. Various examples showcase the utility of our web server.
Visual servoing invariant to changes in camera intrinsic parameters

This paper presents a new visual servoing scheme which is invariant to changes in camera intrinsic parameters. Current visual servoing techniques are based on the learning of a reference image with the same camera used during the servoing. The scheme proposed in the paper differs from previous techniques in that it is camera independent. With the new scheme it is possible to position a camera (with eventually varying intrinsic parameters), with respect to a non-planar object, given a "reference image" taken with a completely different camera.
PASI: New narrowband direction-of-arrival estimator using pilot signals

An iterative subspace-based method, termed pilot-aided subarray iterative (PASI) technique, is proposed for estimating directions-of-arrival (DOAs) of correlated narrowband signals impinging at an array. With the use of pilot signals, DOA estimation for each source can be carried out in parallel; with the use of subarrays, correlated signals can be decorrelated by forward spatial smoothing. The inclusion of the iterative procedure further improves the accuracy of the DOA estimates. The PASI technique yields closely-spaced DOAs of high accuracy with only a small number of samples and a small array size. Moreover, it resolves correlated signals adequately at low signal-to-noise ratios (SNRs) with few iterations. The hardware requirement is also relaxed as the maximum number of detectable DOAs is no longer bounded by the number of elements in the array.
REGULATION OF INITIATION OF DNA SYNTHESIS IN CHINESE HAMSTER CELLS

Suspension cultures of Chinese hamster cells (line CHO) were grown to stationary phase (approximately 8–9 x 105 cells/ml) in F-10 medium. Cells remained viable (95%) for at least 80 hr in stationary phase, and essentially all of the cells were in G1 Upon resuspension or dilution with fresh medium, the cells were induced to resume traverse of the life cycle in in synchrony, and the patterns of DNA synthesis and division were similar to those observed in cultures prepared by mitotic selection. Immediately after dilution, the rates of synthesis of RNA and protein increased threefold. This system provides a simple technique for production of large quantities of highly synchronized cells and may ultimately provide information on the biochemical mechanisms regulating cell-cycle traverse.
Threshold behaviour and final outcome of an epidemic on a random network with household structure

In this paper we consider a stochastic SIR (susceptible→infective→removed) epidemic model in which individuals may make infectious contacts in two ways, both within ‘households’ (which for ease of exposition are assumed to have equal size) and along the edges of a random graph describing additional social contacts. Heuristically motivated branching process approximations are described, which lead to a threshold parameter for the model and methods for calculating the probability of a major outbreak, given few initial infectives, and the expected proportion of the population who are ultimately infected by such a major outbreak. These approximate results are shown to be exact as the number of households tends to infinity by proving associated limit theorems. Moreover, simulation studies indicate that these asymptotic results provide good approximations for modestly sized finite populations. The extension to unequal-sized households is discussed briefly.
Multi-Object Tracking in Airborne Video Imagery based on Compressive Tracking Detection Responses

Multi-object tracking (MOT) in airborne video is a challenging problem due to the uncertain airborne vehicle motion as well as mounted camera vibrations. Most approaches addressing tracking in such type of scenario, use data association based on motion detection responses. Such approaches fail tracking objects with low speed or static ones. To alleviate the motion detection failures, in this paper we propose a multi-object tracking system based on combining motion-detection and Compressive Tracking detection responses. In this work, as in [1], the multi-object tracking problem is solved by associating tracklets according to their confidence values. For reliable association between tracklets and detections, we propose using Compressive Tracking (CT) as a mean to detect objects when motion-detection fails. By exploiting the compressive tracking, which allows discriminating the appearances of objects, tracklet association can be successfully achieved even when objects undertake stop-and-go motion as well as when they are partially occluded. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods.
An Overview of Symbolic Analysis Techniques Needed for the Effective Parallelization of the Perfect Benchmarks

We have identified symbolic analysis techniques that will improve the effectiveness of parallelizing Fortran compilers, with emphasis upon data dependence analysts. We have done this by comparing the automatically and manually parallelized versions of the Perfect Benchmarks¿. The techniques include: symbolic data dependence tests for nonlinear expressions, constraint propagation, array summary information, and run time tests.
An Integrated Probabilistic and Logic Approach to Encyclopedia Relation Extraction with Multiple Features

We propose a new integrated approach based on Markov logic networks (MLNs), an effective combination of probabilistic graphical models and first-order logic for statistical relational learning, to extracting relations between entities in encyclopedic articles from Wikipedia. The MLNs model entity relations in a unified undirected graph collectively using multiple features, including contextual, morphological, syntactic, semantic as well as Wikipedia characteristic features which can capture the essential characteristics of relation extraction task. This model makes simultaneous statistical judgments about the relations for a set of related entities. More importantly, implicit relations can also be identified easily. Our experimental results showed that, this integrated probabilistic and logic model significantly outperforms the current state-of-the-art probabilistic model, Conditional Random Fields (CRFs), for relation extraction from encyclopedic articles.