Miracle-dz/UniFAR-FTUs huggingface dataset:
Light propagation in tissue using the multidimensional PL equations

Optical tomography has found many medical applications that need to know how the photons interact with the different tissues. The majority of the photon transport simulations are done using the diffusion approximation, but this approximation has a limited validity when optical properties of the different tissues present large gradients, when structures near the photons source are studied or when anisotropic scattering has to be taken into account. As an alternative to the diffusion model, the PL equations for the radiative transfer problem are studied. These equations are discretized in a rectangular mesh using a nodal collocation method. The performance of this model is studied by solving different 2D benchmark problems of light propagation in tissue having media with isotropic and anisotropic scattering.
Architecture-based motivation vs Reward-based motivation

Whatever Hume may have meant by this, and whatever various commentators may have taken him to mean, I claim that there is at least one interpretation in which this statement is obviously true, namely: no matter what factual information an animal or machine A contains, and no matter what competences A has regarding abilities to reason, to plan, to predict or to explain, A will not actually do anything unless it has, in addition, some sort of control mechanism that selects among the many alternative processes that A's information and competences can support.
Translating the “Banana Genome” to Delineate Stress Resistance, Dwarfing, Parthenocarpy and Mechanisms of Fruit Ripening

Evolutionary frozen, genetically sterile and globally iconic fruit “Banana” remained untouched by the green revolution and, as of today, researchers face intrinsic impediments for its varietal improvement. Recently, this wonder crop entered the genomics era with decoding of structural genome of double haploid Pahang (AA genome constitution) genotype of Musa acuminata. Its complex genome decoded by hybrid sequencing strategies revealed panoply of genes and transcription factors involved in the process of sucrose conversion that imparts sweetness to its fruit. Historically, banana has faced the wrath of pandemic bacterial, fungal, and viral diseases and multitude of abiotic stresses that has ruined the livelihood of small/marginal farmers’ and destroyed commercial plantations. Decoding structural genome of this climacteric fruit has given impetus to a deeper understanding of the repertoire of genes involved in disease resistance, understanding the mechanism of dwarfing to develop an ideal plant type, unraveling the process of parthenocarpy, and fruit ripening for better fruit quality. Further, injunction of comparative genomics will usher in integration of information from its decoded genome and other monocots into field applications in banana related but not limited to yield enhancement, food security, livelihood assurance, and energy sustainability. In this mini review, we discuss pre- and post-genomic discoveries and highlight accomplishments in structural genomics, genetic engineering and forward genetic accomplishments with an aim to target genes and transcription factors for translational research in banana.
Transactions in doing design-based research for designing curriculum: a self-study

ABSTRACT Design-based research (DBR) is an emerging methodology in teacher education research that researchers claim has strong potential in systematically designing and using instructional strategies and tools to develop, enact, and sustain effective learning environments. Studies have been conducted employing this methodology, but very little research has been made on the process of engaging in DBR for designing curriculum. Through a self-study, I found that using DBR as a model for creating curriculum draws from personal meaning-making, where I tapped on my own education, background, and experiences in literacy instruction to create curriculum. The other dimensions to the DBR process were more intangible and thus harder to negotiate. These included the political aspects, where decision-making was needed regarding course components. Keeping positive relationships with stakeholders was another challenging dimension in order to achieve a collectively meaningful curriculum. Finally, dispositions on literacy instruction also implied tension on the process since dispositions are not necessarily aligned with some DBR principles.
MICROFLUIDIC DEVICES FOR FRACTIONATION OF DNA FRAGMENTS

The fraction collection of DNA fragments for studying gene expression, suffered from technical difficulties, including those related to the accurate capture of fast moving targets and pure collection under conditions of insufficient separation. We introduces a new concept of simultaneous space sampling for capturing targets in complicated situation, like overlapping fragments. Ten parallel extraction channels which covered 1.5 mm long sampling ranges were used to facilitate the capturing of fast moving fragments. Furthermore, the space-sampling extraction made it possible to acquire pure collection, even from partly overlapping fragments that had been insufficiently separated after a short electrophoretic run. Three fragments differed in size by one base bases, were simultaneously collected within 10 min.
An Author’s Guide to Writing Articles and Reviews for Educational Research Review

Contributable publications . 2 I. Reviews . 2 a) Different types . 2 Systematic Review. 2 Best-Evidence Synthesis. 3 Narrative Review. 3 b) Different Objectives . 4 Integrative Research Review . 4 Theoretical Review. 4 Methodological Review. 4 Thematic Review. 5 State-Of-The-Art Review . 5 Historical Review . 5 Comparison of two perspectives review . 5 Review complement . 6 II. Other possible contributions . 6 Theoretical Contribution. 6 Research Critique. 6 Forum Paper . 6
Neural network solution for finite-horizon H∞ constrained optimal control of nonlinear systems

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H∞ state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game by approximating the cost using a Neural Network. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting nearly optimal constrained feedback controller provides closed-loop stability and bounded L2/ gain. The result is a nearly optimal H∞ feedback controller with time-varying coefficients that is solved a priori offline. The effectiveness of the method is shown on the rotational/translational actuator benchmark nonlinear control problem.
Robust language identification using convolutional neural network features

The language identification (LID) task in the Robust Automatic Transcription of Speech (RATS) program is challenging due to the noisy nature of the audio data collected over highly degraded radio communication channels as well as the use of short duration speech segments for testing. In this paper, we report the recent advances made in the RATS LID task by using bottleneck features from a convolutional neural network (CNN). The CNN, which is trained with labelled data from one of target languages, generates bottleneck features which are used in a Gaussian mixture model (GMM)-ivector LID system. The CNN bottleneck features provide substantial complimentary information to the conventional acoustic features even on languages not seen in its training. Using these bottleneck features in conjunction with acoustic features, we obtain significant improvements (average relative improvements of 25% in terms of equal error rate (EER) compared to the corresponding acoustic system) for the LID task. Furthermore, these improvements are consistent for various choices of acoustic features as well as speech segment durations.
A Comparative Analysis of Crossover Variants in Differential Evolution

This paper presents a comparative analysis of binomial and exponential crossover in differential evolution. Some theoretical results concerning the probabilities of mutating an arbitrary component and that of mutating a given number of components are obtained for both crossover variants. The differences between binomial and exponential crossover are identified and the impact of these results on the choice of control parameters and on the adaptive variants is analyzed.
Networked Control systems based on novel Smith predictor and GPC

In order to effectively restrain the impact of network delays, a novel approach is proposed that new Smith predictor combined with the generalized predictive control (GPC) in the networked control systems (NCS). Because new Smith predictor hides predictor model of the network delay into real network data transmission process, further the network delay no longer need to be measured, identified or estimated on-line. Simultaneously this new Smith predictor doesn't include the prediction model of the controlled plant, thus it doesn't need to know the exact mathematical model of the controlled plant. It is applicable to some occasions that network delay is random, timevariant or uncertain. Based on CAN bus, the results of simulation show that this approach is effective.
Fast normalized cross-correlation image matching based on multiscale edge information

In order to overcome the Large computation of cross correlation matching, we propose a method of quick cross correlation matching. In traditional cross correlation matching all the pixels take part in computing, so the speed of matching is slow down. In this paper, we use multi-scale edge information which was extracted by improved Laplacian pyramid(ILP) as feather temple; to convert the gray information matching to the feature information matching; take advantage of the feature template and the image to be matched to establish indirect similarity measure to achieve speed-up, anti-geometric distortion. The multi-scale features of the method can meet multiple needs of the matching accuracy and speed. Experiments show, the method improves the speed of cross correlation matching, and has certain robustness.
QClock: An interposed scheduling algorithm for performance virtualization in shared storage systems

I/O consolidation is a growing trend in production environments due to increasing complexity in tuning and managing storage systems. A consequence of this trend is the need to serve multiple applications simultaneously. It is imperative to ensure that these applications are insulated from each other by virtualization in order to meet any service-level objective (SLO) which represent application requirements in terms of the average throughput, latency and maximum burst size. Most previous approaches basically providing only the throughput guarantees; approaches which provide latency guarantee penalized applications for using spare resources or may not always exploit the full bandwidth offered by the storage system. This article presents an interposed I/O scheduling algorithm QClock which can provide performance virtualization without any support from the storage itself. Using real I/O trace and a detailed storage simulator, we demonstrate two important features of QClock: (1) benefits over existing algorithms by avoiding punish the applications which used the spare capacity; (2) the ability to exploit more capacity offered by the storage system to meet the performance requirement better.
Training Feed-Forward Neural Networks with Monotonicity Requirements

In this paper, we adapt the classical learning algorithm for feed-forward neural networks when monotonicity is require in the input-output mapping. Such requirements arise, for instance, when prior knowledge of the process being observed is available. Monotonicity can be imposed by the addition of suitable penalization terms to the error function. The objective function, however, depends nonlinearly on the first-order derivatives of the network mapping. We show that these derivatives can easily be obtained by an extension of the standard back-propagation algorithm procedure. This yields a computationally efficient algorithm with little overhead compared to back-propagation.
Ethnic Composition of Schools and Students’ Educational Outcomes: Evidence from Sweden

We examine the impact of ethnic school composition on students’ educational outcomes using Swedish population register data. We add to the literature on the consequences of ethnic school segregation for native and immigrant students by distinguishing social interaction effects from selection and environmental effects through one- and two-way fixed effects models. Our findings demonstrate that native and immigrant students’ grades are relatively unaffected by social interaction effects stemming from the proportion of immigrant schoolmates. However, we find nontrivial effects on their eligibility for upper secondary school. Immigrants’ educational outcomes are weakly positively affected by the proportion of co-ethnics in school.
Norm activation theory in the plastic age: Explaining children’s pro-environmental behaviour

Our excessive consumption of single-use plastic has made this era become a plastic age, thus pro-environmental behaviour (PEB) should become our means of survival. This study aims to test whether norm activation theory which is commonly used to explain PEB on adults, could also shed some light in the same behaviour on children sample. Questionnaires, observation, and focus group discussion were used to obtain data from 533 fifth grade students in Jabodetabek, Indonesia. School environment, cultural background, age, length of study, and where the participants live are controlled. Data were then processed through Structural Equation Modelling (SEM). As predicted, norm activation theory has an adequate goodness of fit. Moreover, this study discovers a new interpretation of the theory, that adverse consequence can directly predict personal norm and PEB, due to the malleability of this theory which is further explained in the discussion.
A scheduling algorithm for GPU-attached multicore hybrid systems

A complex task always has a tremendous computation need and it progresses by alternately performing work segments of different computation characteristics. H system is a GPU-attached multicore computer which emerges the potential of meeting the computation need of a complex task. In this paper, an algorithm has been proposed for scheduling complex tasks to H system. Our algorithm assigns priorities to complex tasks before mapping them to the cores of H system. The simulation has demonstrated that our algorithm is effective for scheduling complex tasks to H system under various numbers of cores.
A cautious approach to robust design with model parameter uncertainty

Industrial robust design methods rely on empirical process models that relate an output response variable to a set of controllable input variables and a set of uncontrollable noise variables. However, when determining the input settings that minimize output variability, model uncertainty is typically neglected. Using a Bayesian problem formulation similar to what has been termed cautious control in the adaptive feedback control literature, this article develops a cautious robust design approach that takes model parameter uncertainty into account via the posterior (given the experimental data) parameter covariance. A tractable and interpretable expression for the posterior response variance and mean square error is derived that is well suited for numerical optimization and that also provides insight into the impact of parameter uncertainty on the robust design objective. The approach is cautious in the sense that as parameter uncertainty increases, the input settings are often chosen closer to the center of the experimental design region or, more generally, in a manner that mitigates the adverse effects of parameter uncertainty. A brief discussion on an extension of the approach to consider model structure uncertainty is presented.
Low-temperature and Pressureless Sintering Technology for High-performance and High-temperature Interconnection of Semiconductor Devices

We present an interconnect technology based on low-temperature and pressureless sintering of a nanoscale metal paste to achieve high-performance and high-temperature packaging of semiconductor devices. The nanoscale metal paste, consisting of nanoparticles of silver mixed in an organic binder/solvent vehicle, can be sintered at temperatures close to 275degC. Measurements on electrical and thermal properties of the sintered die interconnect gave it at least five times better than the soldered or epoxied attachment. Die-shear tests of the sintered joints showed a bonding strength of about 25 MPa. The sintered joints exhibited excellent reliability in aging and temperature-cycling tests. Since silver melts at 961degC, the sintered interconnect can be used for wide band gap semiconductor devices (SiC or GaN), which are operable over 300degC where none of the existing solder alloys or epoxies can be used. In summary, the low- temperature sintering of nanoscale metal paste is shown to be a reliable, lead-free interconnect solution for high-temperature and high-performance packaging needs.
Sentiment analysis of greek tweets and hashtags using a sentiment lexicon

The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. We focus on the Greek language and the microblogging platform "Twitter", investigating methods for extracting sentiment of individual tweets as well population sentiment for different subjects (hashtags). The proposed methods are based on a sentiment lexicon. We compare several approaches for measuring the intensity of "Anger", "Disgust", "Fear", "Happiness", "Sadness", and "Surprise". To evaluate the effectiveness of our methods, we develop a benchmark dataset of tweets, manually rated by two humans. Our automated sentiment results seem promising and correlate to real user sentiment. Finally, we examine the variation of sentiment intensity over time for selected hashtags, and associate it with real-world events.
Discrimination in the Lab: Experiments Exploring the Impact of Performance and Appearance on Sorting and Cooperation

We present experimental evidence consistent with statistical discrimination in a public good and group formation game. We find that behavior is correlated with race and gender, and people use race and gender to predict behavior when no other information is available. When information on behavior is provided, people disregard personal characteristics completely. These characteristics are also disregarded when individual behavior is induced to break the correlation between characteristics and behavior. That is, people disregard race and gender even when observed behavior is unusual but relevant to payoffs. Finally, our experiments show that sorting into groups has dramatic implications on cooperation. Overall payoffs are higher when sorting is possible than when groups are randomly assigned. This only occurs, however, when personal characteristics are known. Higher payoffs are attained at the cost of an equitable distribution.
The Osmoprotective Function of the NFAT5 Transcription Factor in T Cell Development and Activation1

The NFAT5/TonEBP transcription factor, a recently identified rel/NF-κB family member, activates transcription of osmocompensatory genes in response to extracellular hyperosmotic stress. However, the function of NFAT5 under isosmotic conditions present in vivo remains unknown. Here we demonstrate that NFAT5 is necessary for optimal T cell development in vivo and allows for optimal cell growth ex vivo under conditions associated with osmotic stress. Transgenic mice expressing an inhibitory form of NFAT5 in developing and mature T cells exhibited a 30% reduction in thymic cellularity evenly distributed among thymic subsets, consistent with the uniform expression and nuclear localization of NFAT5 in each subset. This was associated with a 25% reduction in peripheral CD4+ T cells and a 50% reduction in CD8+ T cells. While transgenic T cells exhibited no impairment in cell growth or cytokine production under normal culture conditions, impaired cell growth was observed under both hyperosmotic conditions and isosmotic conditions associated with osmotic stress. Transgenic thymocytes also demonstrated increased sensitivity to osmotic stress. Consistent with this, the system A amino acid transporter gene ATA2 exhibited NFAT5 dependence under hypertonic conditions but not in response to amino acid deprivation. Expression of the TNF-α gene, a putative NFAT5 target, was not altered in transgenic T cells. These results not only demonstrate an osmoprotective function for NFAT5 in primary cells but also show that NFAT5 is necessary for optimal thymic development in vivo, suggesting that developing thymocytes within the thymic microenvironment are subject to an osmotic stress that is effectively countered by NFAT5-dependent responses.
Morphological attribute filters for the analysis of very high resolution remote sensing images

This paper proposes the use of morphological attribute profiles as an effective alternative to the conventional morphological operators based on the geodesic reconstruction for modeling the spatial information in very high resolution images. Attribute profiles, used in multilevel approaches, result particularly effective in terms of computational complexity and capabilities in characterizing the objects in the image. In addition they are more flexible than operators by reconstruction, thanks to the definition of possible different attributes. Experimental results obtained on a Quickbird panchromatic very high resolution image proved the effectiveness of the presented attribute filters and pointed out their main properties.
Curriculum based discriminative language model training

Discriminative language modeling is a technique used for correcting automatic speech recognition errors, and can be handled as a classification or a ranking problem. The aim of curriculum learning is to train the model with examples or concepts of gradually increasing level of difficulty. In this work, we use the classification and ranking versions of the perceptron algorithm and investigate three different curriculum learning approaches based on selection, ordering and clustering of the training examples. The results show that curriculum learning can help increase the performance of a classifying perceptron system, and with the ranking perceptron, it is possible achieve similar system performance with a shorter training time.
Symbolic AI versus Connectionism in Music Research

In cognitive science and research on artificial intelligence, there are two central paradigms: the symbolic and the analogical. Within the analogical paradigm, interest in artificial neural networks, or connectionism, has experienced a resurgence during the last decade; this change has also been reflected in the field of musical modeling. This article provides a general survey of the relationship between symbolic AI and connectionism, both on a general level and from the point of view of music research. This is followed by a short introduction to artificial neural networks, which includes a description the main principles of their structure and function as well as a presentation examples of their use in the field of music.