Miracle-dz/UniFAR-FTUs huggingface dataset:
Early alterations in cortical and cerebellar regional brain growth in Down Syndrome: An in vivo fetal and neonatal MRI assessment

Prachi A. Patkee Data curationFormal analysisInvestigationMethodologyProject administrationSoftwareValidationVisualizationWriting Original draft preparation and Writing Reviewing & Editing , Ana A. Baburamani Funding acquisitionInvestigationProject administrationValidation and Writing Reviewing & Editing , Vanessa Kyriakopoulou Data curationFormal analysisInvestigationMethodologyValidation and Writing Reviewing & Editing , Alice Davidson Data curationFormal analysisMethodology and Validation , Elhaam Avini Formal analysisFunding acquisition and Validation , Ralica Dimitrova Data curationInvestigation and Writing Reviewing & Editing , Joanna Allsop Data curationInvestigation and Methodology , Emer Hughes Data curationInvestigation and Methodology , Johanna Kangas Data curation and Investigation , Grainne McAlonan ConceptualizationFunding acquisitionInvestigationMethodologyProject administrationSupervision and Writing Reviewing & Editing , Mary A. Rutherford ConceptualizationFunding acquisitionInvestigationMethodologyProject administrationSupervisionValidation. and Writing Reviewing & Editing
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks.

Spiking Neural Networks (SNNs) use spatio-temporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. Motivated by the success of deep learning, the study of Deep Spiking Neural Networks (DeepSNNs) provides promising directions for artificial intelligence applications. However, training of DeepSNNs is not straightforward because the well-studied error back-propagation (BP) algorithm is not directly applicable. In this paper, we first establish an understanding as to why error back-propagation does not work well in DeepSNNs. To address this problem, we propose a simple yet efficient Rectified Linear Postsynaptic Potential function (ReL-PSP) for spiking neurons and propose a Spike-Timing-Dependent Back-Propagation (STDBP) learning algorithm for DeepSNNs. In STDBP algorithm, the timing of individual spikes is used to convey information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner. Our experimental results show that the proposed learning algorithm achieves state-of-the-art classification accuracy in single spike time based learning algorithms of DeepSNNs. Furthermore, by utilizing the trained model parameters obtained from the proposed STDBP learning algorithm, we demonstrate the ultra-low-power inference operations on a recently proposed neuromorphic inference accelerator. Experimental results show that the neuromorphic hardware consumes 0.751~mW of the total power consumption and achieves a low latency of 47.71~ms to classify an image from the MNIST dataset. Overall, this work investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
Prognostic immune-related gene models for breast cancer: a pooled analysis

: Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accura-cies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER − ) and lymph node-positive (LN + ) breast cancer subtypes. The three top-ranked ER − breast cancer models achieved overall C-indices of 0.62–0.63. Two models predicted better than chance for ER + breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN + breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER + , ER − , LN + , and LN − breast cancer subtypes. Taken together, these data showed that immune-related gene signatures have good prognostic values in breast cancer, especially for ER − and LN + tumors.
Password Cracking

Passwords are a system designed to provide authentication. There are many different ways to authenticate users of a system: a user can present a physical object like a key card, prove identity using a personal characteristic like a fingerprint, or use something that only the user knows. In contrast to the other approaches listed, a primary benefit of using authentication through a password is that in the event that your password becomes compromised it can be easily changed. This paper will discuss what password cracking is, techniques for password cracking when an attacker has the ability to attempt to log in to the system using a user name and password pair, techniques for when an attacker has access to however passwords are stored on the system, attacks involve observing password entry in some way and finally how graphical passwords and graphical password cracks work.
Open source in Swedish companies: where are we?

Open Source (OS) is a phenomenon of increasing significance for organizations, offering the prospect of effective alternative business solutions and new business opportunities. A number of surveys have been conducted in various countries with the purpose of understanding the state of practice with respect to OS in companies. In this paper we report on a study of the perceptions of OS and the uptake of OS products and development models in Swedish companies. The study used purposeful sampling of companies that have an expressed interest in OS, and the survey was conducted using a set of pre‐prepared questions. Its goal was to investigate the extent to which OS has influenced business thinking, as seen from the standpoint of stakeholders. We found that uptake is much higher than reported in earlier studies, but as with previous studies, activity is still concentrated in small and medium‐sized enterprises (SMEs). There is increased evidence of interest beyond the simple use of OS components at the infrastructure level. Further, a significant proportion of the companies studied are supporting the OS community as well as benefiting from it. Support includes participation in existing projects and the release of new software under OS licenses.
Comparison of HFNC , Bubble CPAP , and SiPAP on Aerosol Delivery in Neonates : An In-Vitro Study By

COMPARISON OF HFNC, BUBBLE CPAP, AND SIPAP ON AEROSOL DELIVERY IN NEONATES: AN IN-VITRO STUDY by Fatemah S. Sunbul Background: Aerosol drug delivery via high flow nasal cannula (HFNC), bubble continuous positive airway pressure (CPAP), and synchronized inspiratory positive airway pressure (SiPAP) has not been quantified in spontaneously breathing premature infants. The purpose of this study was to compare HFNC, bubble CPAP, and SiPAP on aerosol delivery in a simulated spontaneously breathing preterm model. Methods: A breath simulator was set to preterm infant settings (VT: 9 ml, RR: 50 bpm and Ti: 0.5 sec) and connected to the trachea of an anatomical upper airway model of a preterm infant (DiBlasi) via collecting filter distal to the trachea. The HFNC (Fisher & Paykel), Bubble CPAP (Fisher & Paykel), and SiPAP (Carefusion) were attached to the model via their proprietary nasal cannula and set to deliver 5 cm H2O pressure. Albuterol sulfate (2.5 mg/0.5 mL) was aerosolized with a mesh nebulizer (Aeroneb Solo) positioned (1) proximal to the patient and (2) prior to the humidifier (n=5). The drug was eluted from the filter with 0.1 N HCl and analyzed via spectrophotometry (276 nm). Data were analyzed using descriptive statistics, t-tests, and analysis of variance (ANOVA), with p<0.05 significant. Results: At position 1, the trend of lower deposition across devices was not significant; however, in position 2, drug delivery with SiPAP was significantly lower compared to both HFNC (p=0.003) and bubble CPAP (p=0.008). Placement of the nebulizer prior to the humidifier increased deposition with all devices (p<0.05). Conclusion: Aerosol can be delivered via all three devices used in this study; however, delivery efficiency of HFNC is better than the other CPAP devices tested. Device selection and nebulizer position impacted aerosol delivery in this simulated model of a spontaneously breathing preterm infant.
Hydrological simulation approaches for BMPs and LID practices in highly urbanized area and development of hydrological performance indicator system

Urbanization causes hydrological change and increases stormwater runoff volumes, leading to flooding, erosion, and the degradation of instream ecosystem health. Best management practices (BMPs), like detention ponds and infiltration trenches, have been widely used to control flood runoff events for the past decade. However, low impact development (LID) options have been proposed as an alternative approach to better mimic the natural flow regime by using decentralized designs to control stormwater runoff at the source, rather than at a centralized location in the watershed. For highly urbanized areas, LID stormwater management practices such as bioretention cells and porous pavements can be used to retrofit existing infrastructure and reduce runoff volumes and peak flows. This paper describes a modeling approach to incorporate these LID practices and the two BMPs of detention ponds and infiltration trenches in an existing hydrological model to estimate the impacts of BMPs and LID practices on the surface runoff. The modeling approach has been used in a parking lot located in Lenexa, Kansas, USA, to predict hydrological performance of BMPs and LID practices. A performance indicator system including the flow duration curve, peak flow frequency exceedance curve, and runoff coefficient have been developed in an attempt to represent impacts of BMPs and LID practices on the entire spectrum of the runoff regime. Results demonstrate that use of these BMPs and LID practices leads to significant stormwater control for small rainfall events and less control for flood events.
Factors influencing entrepreneurial intentions among Sabah IPTA students

This study is done to identify the relationship of personal traits which include attitude toward behavior, social norm and perceived behavioral control as well as entrepreneurial knowledge with entrepreneurial intentions. In Malaysia, government has been encouraging young adults to venture into businesses or innovation activities rather than depending on employment opportunity. This research done in line with government encouragement from the tenth Malaysian Plan as "The shift to high income will need growth that is based on productivity and led by innovation. Sabah is the second largest state in the country after Sarawak and has many potential sources to be explored, but seem to have largest number of people below the poverty line in Malaysia, and lower than the Indonesian national poverty rate and in the same level as Aceh and Myanmar based on 2004 United Nations figures. As long as we can identify and test the factors of theory of planned behavior (TPB) and entrepreneurial knowledge could influencing them towards entrepreneurship intentions as their personal career choice we can help them to better develop Sabah. The samples in this study are students from three Sabah's' public higher learning Institutes (IPTA) which are University Teknologi MARA Sabah (UiTM), University Malaysia Sabah (UMS) and Politeknik Kota Kinabalu branch
Improving Multiclass Text Classification with the Support Vector Machine

The Problem: There are billions of text documents available in electronic form. More and more are becoming available every day. The Web itself contains over a billion documents. Millions of people send e-mail every day. Academic publications and journals are becoming available in electronic form. These collections and many others represent a massive amount of information that is easily accessible. However, seeking value in this huge collection requires organization. Many web sites offer a hierarchically-organized view of the Web. E-mail clients offer a systems for filtering e-mail. Numerous academic communities have a Web site that allows searching on papers and shows an organization of papers. However, organizing documents by hand or creating rules for filtering is painstaking and labor-intensive. This can be greatly aided by automated classifier systems. The accuracy of such systems determines their usefulness. We propose to use the Support Vector Machine (SVM) in conjunction with Error-Correcting Output Codes (ECOC) to improve the state-of-the-art in text classification.
Learning and example selection for object and pattern detection

Object and pattern detection is a classical computer vision problem with many potential applications, ranging from automatic target recognition to image-based industrial inspection tasks in assembly lines. While there have been some successful object and pattern detection systems in the past, most such systems handle only specific rigid objects or patterns that can be accurately described by fixed geometric models or pictorial templates. This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. Some examples of such object and pattern classes include human faces, aerial views of structured terrain features like volcanoes, localized material defect signatures in industrial parts, certain tissue anomalies in medical images, and instances of a given digit or character, which may be written or printed in many different styles. The thesis consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. We also discuss some pertinent learning issues, including ideas on virtual example generation and example selection. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. We show that this is indeed the case by demonstrating the technique on two other pattern detection/recognition problems. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. Active learning is an area of research that investigates how a learner can intelligently select future training examples to get better approximation results with less data. We propose an active learning formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. Finally, we simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Potential for microbial anaerobic hydrocarbon degradation in naturally occurring petroleum-associated deep-sea sediments

1 Department of Biological Sciences, University of Calgary, Calgary, T2N 1N4, Canada 6 2 School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia 7 3 Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The 8 University of Queensland, QLD 4072, Australia 9 4 Institute for Geo-Resources and Environment, Geological Survey of Japan, National Institute of 10 Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, 305-8567, Japan 11 5 TDI Brooks International, College Station, Texas, TX 77845, USA 12
Supplemental: Higher-Order Finite Elements for Embedded Simulation

Whereas measuring convergence rates for standard finite element discretizations is fairly straightforward, the measurement for embedded methods is more complicated by the need to design a measurement scheme which takes into account the precision with which the embedded domain is correctly captured. We discuss this problem more in detail in the following, and describe the setup we use to verify the convergence rate of the Finite Cell Method for a family of uniform background meshes with a non-trivial embedded geometry.
Detecting stealthy false data injection using machine learning in smart grid

Aging power industries together with increase in the demand from industrial and residential customers are the main incentive for policy makers to define a road map to the next generation power system called smart grid. In smart grid, the overall monitoring costs will be decreased but at the same time, the risk of cyber attacks might be increased. Recently a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We devise two machine learning based techniques for stealthy attack detection. The first method utilizes supervised learning over labeled data and trains a support vector machine. The second method requires no training data and detects the deviation in measurement In both methods, principle component analysis is used to reduce the dimensionality of the data to be processed, and thus leads to lower computation complexities. The results of the proposed detection methods on the IEEE standard test systems demonstrate effectiveness of both schemes.
Quantitative PCR provides a simple and accessible method for quantitative microbiome profiling

The use of relative next generation sequencing (NGS) abundance data can lead to misinterpretations of microbial community structures as the increase of one taxon leads to concurrent decrease of the other(s). To overcome compositionality, we provide a quantitative NGS solution, which is achieved by adjusting the relative 16S rRNA gene amplicon NGS data with quantitative PCR (qPCR-based) total bacterial counts. By comparing the enumeration of dominant bacterial groups on different taxonomic levels in human fecal samples using taxon-specific 16S rRNA gene-targeted qPCR we show that quantitative NGS is able to estimate absolute bacterial abundances accurately. We also observed a higher degree of correspondence in the estimated microbe-metabolite relationship when quantitative NGS was applied. Being conceptually and methodologically analogous to amplicon-based NGS, our qPCR-based method can be readily incorporated into the standard, high-throughput NGS sample processing pipeline for more accurate description of interactions within and between the microbes and host.
Towards a mixed approach to extract biomedical terms from text corpus

The objective of this paper is to present a methodology to extract and rank automatically biomedical terms from free text. The authors present new extraction methods taking into account linguistic patterns specialized for the biomedical domain, statistic term extraction measures such as C-value and statistic keyword extraction measures such as Okapi BM25, and TFIDF. These measures are combined in order to improve the extraction process and the authors investigate which combinations are the more relevant associated to different contexts. Experimental results show that an appropriate harmonic mean of C-value associated to keyword extraction measures offers better precision, both for single-word and multi-words term extraction. Experiments describe the extraction of English and French biomedical terms from a corpus of laboratory tests available online. The results are validated by using UMLS (in English) and only MeSH (in French) as reference dictionary. Towards a Mixed Approach to Extract Biomedical Terms from Text Corpus
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- Two methods of determining the pitch or timbre of electrical stimuli in comparison with acoustic stimuli are described. In the first experiment, the pitch of pure tones and electrical stimuli were compared directly by implant users who have residual hearing in the non-implanted ear. This resulted in a relationship between frequency in the non-implanted ear and position of the best-matched electrode in the implanted ear. In the second experiment, one-and two-formant synthetic vowels, with formant frequencies covering the range from 200 to 4000 Hz, were presented to the same implant users through their implant or through their hearing aid. The listeners categorised each stimulus according to the closest vowel from a set of eleven possibilities, and a vowel centre was caiculated for each response category for each ear. Assuming that stimuli at the vowei centres in each ear sound alike,a second relationship between frequency and electrode position was derived. Both experiments showed that electrically-evoked pitch is much lower than that produced by pure tones at the corresponding cochlear location in normally-hearing listeners. This helps to explain why cochlear implants with electrode arrays that rarely extend beyond the basal turn of the cochlea have achieved high levels of speech recognition in postlinguistically deafened adults without major retraining or adaptation by the users. The techniques described also have potential for optimising speech recognition for individual implant users.
DEPARTMENT OF ECONOMICS

This paper develops a classical-Marxian macroeconomic model to examine the growth and distributional consequences of education. First, the role of education in skill formation is considered and it is shown that an expansion in education will promote growth and have beneficial distributional effects within the working class, but it will redistribute income from workers to capitalists. Second, the model is extended analyze the broader political economic consequences of education on class relations and class conflict. The model suggests the importance of a progressive type of education rather than one which weakens the power workers, for it allows for equitable growth outcomes which improve the position of workers as a whole and reduces inequality within workers. Finally, the model shows that education leads to multiple equilibria and it stresses the importance of providing suitable incentives to workers for taking advantage of greater education access, without which the economy can be caught in a low-skill trap. JEL classification system: E2, E11, O41, J31.