Miracle-dz/UniFAR-FTUs huggingface dataset:
’ s distributional-learning experiment : Infants ’ input , infants ’ perception , and computational modeling Benders

Infants are often said to acquire their language-specific speech perception through the mechanism of distributional learning, but the exact properties of this mechanism are rarely discussed. This paper aims at bringing insight in the mechanism of distributional learning by comparing two types of computational models of distributional learning (Mixture-of-Gaussian models and neural network models) and several learning scenario’s (learning a representation for each individual auditory dimension, or for auditory dimensions combined). All models are trained on the same data, a corpus of /A/s and /a:/s in Dutch infant-directed speech, and compared against Dutch infants’ perception of these same vowels as found in previous studies. Both types of models were more successful in learning the contrast when categories could be formed for multiple auditory cues than when they had to form the categories for individual auditory dimensions. This result suggests that infants might associate their earliest categories with multiple auditory dimensions, which was also found in the earlier speech perception studies. The models differed in the infant perception data they could account for and the robustness of the acquired representations. The paper closes off with an in-depth discussion of the differences between the models, possible extensions, and empirical questions for further experiments with infants.
BMC Systems

Background: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708 , iND750 and iLL672 . All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism. Results: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13 C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800 , we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets. Conclusion: Performing integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states.
Study on stability of asphaltenes in Thai crude oil

The precipitation of asphaltenes from crude oils can lead to serious challenges during oil production and processing due to the change of pressure, temperature and crude oil composition casing the stability of asphaltenes in crude oil. This work was to study of structure or parameters indicating the structure of asphaltenes in order to use the data base to prevent their precipitation and this may help us to understand its function as the stability of asphaltenes. The structural characteristic of asphaltenes from L1 crude oil sources was separated into fractions by using two different procedures; ASTM D2007 and vacuum procedures. Moreover, two light alkanes, including n-hexane (n-C6) and n-heptane (n-C7) were also used to precipitate asphaltenes. The asphaltenes and resins contents of L1, L2, and L3 crude oil sources were also determined. Normally, resins extraction by following ASTM procedure is a time consuming process due to the elution process carried out under gravity force. Therefore, the force of vacuum was applied as the alternative ways. So, these two techniques were used to determine the resins contents of L1. Moreover, Proton Nuclear Magnetic Resonance (H NMR), Carbon Nuclear Magnetic Resonance (C NMR), and Fourier Transform Infrared (FTIR) spectroscopy techniques were used to characterize the elementary molecular of asphaltenes and resins. The results showed that the use of filtration technique caused the higher asphaltenes contents due to wax contamination in that fraction. Moreover, the asphaltenes contents in L1, L2, and L3 were 1.67, 1.36, and 0.82 %wt, respectively. The asphaltenes contents precipitated by n-hexane and n-heptane were 1.69 %wt and 1.67 %wt. Also, the resins contents in L1, L2, and L3 were 14.57, 18.89, and 13.27 %wt,
Evaluation of the Maintainability of Object-Oriented Software Based on UML Class Metrics

-The design quality measure of Object-Oriented (OO) software is a predictable factor in the field of software maintenance research. Therefore, the detection of required external quality attributes such as maintainability and stability in the early phase of software development becomes very important. Unified Modeling Language (UML) class diagrams are essential tools used to represent the software structural complexity through association and generalization relationship. It has been identified that the class diagram quality measurement allows the OO software designers to improve the weak design spots and reduce the maintenance cost. The UML class diagram metrics are examined and they act as predictors of maintainability sub-characteristics (ISO/IEC 25010:11 Quality Model) through which one can measure the maintainability of the software. In this paper, four models for understandability, modifiability, analyzability and maintainability are proposed. These models are specially designed for OO software and are used to predict the sub-characteristics of maintainability through class diagrams. The models were created based on multiple regressions between the related metric as an independent variable and the maintainability sub-characteristic as the dependent variable. In addition, these measures were correlated with the result of the related data set available. The results of these models provide evidence for the impact of UML class diagram metrics on the maintainability of software.
Self-disclosure and SNS Platforms : The Impact of SNS Transparency and Culture Research-in-Progress

Self-disclosure on social networking platforms has attracted attention in Information Systems (IS) research. While studies have connected individual beliefs such as perceived privacy, perceived benefits, and cost to SNS use, less research has examined how characteristics of the social media platform itself shape SNS use. This study extends the literature by examining how the interplay between SNS transparency and individual culture affect user's self-disclosure of personal information as well as factors that shape users’ perceptions on SNS transparency. Drawing on Accountability Theory, Communication Privacy Management Theory, and Culture Theory, we build a comprehensive, integrative model that offers a more holistic view of self-disclosure and the impact of the contextual factors on self-disclosure behaviors. The proposed study will use factorial survey (Vance et al. 2013) to collect data. This study will conceptually develop and operationalize a new construct—SNS transparency—that could help the researchers to gain better understanding of SNS-based self-disclosure and offers insights into how to integrate transparency into social media platforms.
Learning from Active Human Involvement through Proxy Value Propagation

Learning from active human involvement enables the human subject to actively intervene and demonstrate to the AI agent during training. The interaction and corrective feedback from human brings safety and AI alignment to the learning process. In this work, we propose a new reward-free active human involvement method called Proxy Value Propagation for policy optimization. Our key insight is that a proxy value function can be designed to express human intents, wherein state-action pairs in the human demonstration are labeled with high values, while those agents’ actions that are intervened receive low values. Through the TD-learning framework, labeled values of demonstrated state-action pairs are further propagated to other unlabeled data generated from agents’ exploration. The proxy value function thus induces a policy that faithfully emulates human behaviors. Human-in-the-loop experiments show the generality and efficiency of our method. With minimal modification to existing reinforcement learning algorithms, our method can learn to solve continuous and discrete control tasks with various human control devices, including the challenging task of driving in Grand Theft Auto V.
Apollo 15 green glass He-Ne-Ar signatures – In search for indigenous lunar noble gases

Green glasses collected during the Apollo 15 mission represent quenched picritic (17 to 19 wt. % MgO), very-low-Ti (0.2 to 0.4 wt. % TiO2) melts, which explosively erupted ~3.4 Gyrs ago by fire-fountaining (e.g., Podosek and Huneke, 1973; Delano, 1979, 1986; Spangler et al., 1984; Steele et al., 1992). They are among the most primitive material yet collected on the Moon (Green and Ringwood, 1973). The green glasses can be divided into discrete compositional groups, which were formed in distinct magmatic events and/or from different source regions (Delano, 1979; Steele et al., 1992).
Inwards vapor diffusion due to high temperature gradient in wall assemblies

Moisture is the main source of deterioration of building envelope systems. One source of moisture that has not been studied to a great extent is inwards vapor flows, due to high temperature gradients. Such flows occur, for example, when exterior claddings like brick are wetted by rain and then exposed to solar radiation. This thesis investigates this phenomenon, which can bring large amount of undue moisture within in the envelope assembly. This thesis presents in detail the experimental work performed on large-scale wall assembly specimens to document their hygrothermal performance under conditions leading to inwards moisture flow. A large-scale experimental facility was utilized for the purpose of this work which consisted of weighing apparatuses to monitor the change of mass of the cladding and back-wall parts of wall specimens, a spraying array, a radiation array, and a test hut to provide controlled interior conditions. Five insulated wood-framed walls were monitored, four of which were brick cladded and one stucco cladded. Constructions of the backwalls varied by the material utilized for the exterior sheathing and the interior finish and also by presence or lack of an air cavity. The walls and the environment were equipped for electronic monitoring, complemented with manual weighing of gravimetric samples. The results of the experiments demonstrate that the presence of vapor tight interior finishes result in the accumulation of moisture in the interior gypsum board. Furthermore, it is shown that even a vapor tight sheathing does not reduce sufficiently vapor flow to prevent moisture accumulation in the wood studs and the interior gypsum board. The highly positive effect of air space, and its ventilation, in reducing the magnitude of the inward vapor flow is shown. During the process of studying the vapor diffusion through various wall assemblies, a vertical temperature and moisture content gradient was observed along the gypsum board-insulation interfaces. This phenomenon was also further investigated and studied both experimentally and numerically using CFD. The work indicated that stratification of humidity occurs within the insulation cavity of the wall. This thesis demonstrates the mechanism of inward diffusion: under high thermal gradients, large amounts of vapor are transported, inducing high relative humidity against low-permeable layers in assemblies. By sorption, materials exposed to high relative humidity display high moisture content.
EFFECTS OF PARTICLE INTERFERENCE IN MORTARS AND CONCRETES

DEVIATIONS OF CONCRETE STRENGTH BY THE WATER-CEMENT RATIO LAW DEPEND UPON THE GRADING OF ITS SOLID MATERIALS, CEMENT PLUS AGGREGATES. THIS STUDY SHOWS THAT VARIATIONS IN STRENGTH AND WORKABILITY ARE DUE TO A CHARACTERISTIC OF THE GRADING WHICH IS CALLED PARTICLE INTERFERENCE. THIS IS THE RESULT OF AN EXCESS OF ANY SIZE GROUP OF PARTICLES, WHETHER AMONG THE FINER OR THE COARSER SIZES. NON-UNIFORM VOID POCKETS AND SEGREGATION OF SIZES ARE ALWAYS THE EFFECT OF PARTICLE INTERFERENCE. THIS WAS CONCLUDED FROM SIEVE ANALYSIS TESTS AND VISUAL INSPECTION. WHERE INTERFERENCE IS PRESENT WHERE A CORRECTION COULD BE MADE FOR THE SAKE OF ECONOMY, THE MATERIAL REQUIRED IS FIGURED IN THE SIMPLE MATHEMATICAL ANALYSIS. EFFORTS TO SET UP THE IDEAL GRADING MUST TAKE INTO ACCOUNT WATER-CEMENT RATIO AND CONSISTENCY. THE LAYER MUST BE UNIFORM THROUGHOUT AND ALL THE VOID POCKETS WILL BE OF ABOUT THE SAME SIZE. PARTICLE INTERFERENCE RESULTS WHENEVER THE PARTICLES OF ANY SIZE GROUP ARE IN SUCH EXCESS THAT THE AVERAGE CLEAR DISTANCE BETWEEN ADJACENT PARTICLES OF THIS GROUP IS LESS THAN THE AVERAGE DIAMETER OF THE PARTICLES OF THE NEXT SMALLER GROUP SIZE, PLUS THE EFFECT OF A CEMENT FILM, IF THE PASTE IS STIFF. PARTICLE INTERFERENCE MAY OCCUR BETWEEN ANY TWO GROUPS OF SUCCESSIVE SIZES FROM THE FINEST TO THE COARSEST AGGREGATE WHEN THE LARGER DIAMETERS ARE IN EXCESS. IT MAY OCCUR IN THE CEMENT AND THE LOW STRENGTH IN CEMENT MAY RESULT FROM GRINDING CLINKERS SO BADLY THAT PARTICLE INTERFERENCE FOLLOWS. MEASUREMENT AND CALCULATION METHODS FOR PARTICLE INTERFERENCE ARE PRESENTED. ANY MIXTURE MAY BE EXAMINED FOR PARTICLE INTERFERENCE FROM RESULTS OF THE SIEVE ANALYSES OF THE AGGREGATES AND THE PROPORTION IN WHICH THEY ARE COMBINED. IT IS CONCLUDED THAT PARTICLE INTERFERENCE CAN BE ELIMINATED, WITHOUT UNDUE COST, BY THE REDUCTION OF THE AMOUNTS OF THOSE GROUPS THAT MAY BE PRESENT IN CRITICAL EXCESS, EITHER BY BLENDING, OR BY THE USE OF VARIOUS CLASSIFIERS IN THE PREPARATION.
Design and Performance Analysis of Frequency Hopping OFDM Based Noise Reduction DCSK System

The reference chaotic signal in the differential chaos shift keying (DCSK) system causes both bit error rate (BER) and security performance degradation. In this paper, a frequency hopping orthogonal frequency division multiplexing based noise reduction DCSK (FH-OFDM-NR-DCSK) system is proposed to enhance both the BER and security performance of the OFDM-DCSK system. This system combines the advantages of both FH-OFDM-DCSK and NR-DCSK. Rather than creating β separate chaotic samples to utilize as a reference sequence, β/P chaotic samples are created and then replicated P times. Moving average filters of size P are applied after the frequency hopper to average the repeated chips of the reference and data-bearing signals. A new frequency hopping pattern is designed depending on the chaotic map on which the matrix pattern is designed efficiently. The performance of the proposed system is examined, and bit error rate analytic equations of the FH-OFDM-NR-DCSK system over an AWGN and two-path Rayleigh fading channel are derived. The simulation results show that the theoretical BER expressions and simulation performance match. The findings show that for the same spreading factor, the proposed system outperforms the DCSK and FH-OFDM-DCSK when P1. This method decreases noise variance and improves BER without adding to the system's complexity.
Application of GIS for Urban Traffic Accidents: A Critical Review

The analysis of huge data is a complex task that cannot be executed without a proper system. Geographic information systems (GISs) have been used by many transportation agencies and police departments to analyze and manage urban traffic accident (UTA) data and for decision making aimed at decreasing accident rates and increasing safety. The exact location of accidents and environmental characteristics must be analyzed as UTAs occur in specific locations with specific characteristics. ArcGIS software is the best choice for obtaining meaningful information and analysis results from UTAs in an observational time span. GIS technology is a fundamental element for investigating and evaluating the complex spatial relationship among different components and urban traffic accident is one of them. Micro or macro analysis of UTAs through the spatial prospective within the geographical environment and urban structure can make a deep micro understanding of UTAs patterns in addition to assisting in decision making. UTAs can be considered complex events that occur in two aspects which are spatial and temporal or space and time in other word. A GIS can integrate more than two different and unrelated databases. The evaluation among different spatial objects in a geographical environment and associated factors in urban structure which are included but not limited to land use category, road transportation network qualification, population density, etc., is one of the GIS specification. Traffic safety organizations and UTA researchers use GISs as a key technology to support their research and operational needs. In particular, GIS-T is an often-used GIS application used for planning and decision-making in transportation.
Doppler and Pseudorange Measurements as Prediction Features for Multi-Constellation GNSS LoS/NLoS Signal Classification

Precise positioning and timing are key challenges for safety & security critical applications in autonomous and trusted vehicles (ATVs). A global navigation satellite system (GNSS) receiver is one of the sensors used in ATVs to provide a satellite-based positioning, navigation and timing solution. GNSS robustness in built-up areas like urban canyons and in sheltered locations like multi-storey car parks is severely impaired due to non-line-of-sight and multi-path signal propagation. In this paper, an optimised decision tree classifier is used for the discrimination of GNSS signals into line-of-sight and non-line-of-sight signals. In addition to using the carrier-to-noise ratio and the satellite elevation angle as prediction features, adding the pseudoranges and the Doppler measurements enhances the classifier performance by 12 %. As a result, an overall prediction accuracy of 97.8 % is obtained. For the studied data-set, the pseudorange measurements have a higher prediction importance than the Doppler measurements. Also evaluated in this study, is the use of a GPS L1 signal prediction regression model on other GNSS constellations. Analysis of the investigated scenario shows that the use of the current model is not adequate for non-GPS constellations.
Laboratory Investigations on Stone Matrix Asphalt Using Sisal Fibre for Indian Roads

The technology of asphalt materials and mixtures is discovered and mostly used in Europe and North America. The SMA (stone matrix asphalt) mixture is a gap-graded mix. In this present study comparison of strength of pavement wearing coat made with SMA mix with fibre and without fibre was done. This research was done to evaluate the viability of sisal fibres as stabilising agent in the mixture by laboratory tests in which a flow parameter was analyzed, as well as the mechanical properties of the mixture. For the SMA mix the aggregate gradation was taken as per the MoRTH specification and the binder content was 4%, 4.5%. 5%, 5.5%, 6%, 6.5%, 7% by weight of aggregate and fibre used was 0.3% by weight of aggregate. Here we used cement as filler and 60/70 grade bitumen as binder.
The Effects of Water Hyacinth Invasion on Smallholder Farming along River Tano and Tano Lagoon, Ghana

Abstract Water hyacinth invasion and its associated effects have been reported to be a source of problems to riparian people, posing challenges to activities like fishing and farming along invaded waterbodies. Based on cross-sectional research with 55 farmers who were sampled using the snowballing sampling technique, this study assesses the effects of water hyacinth invasion on smallholder farming activities in the Jomoro District. Four communities along the River Tano and Tano Lagoon in the district were purposively selected for the study. Individual surveys and Focus Group Discussions were used as data collection methods to assess the situation. Among the problems posed by water hyacinth invasion to smallholder farming in the study area were the destruction of farm produce due to the blockade caused by water hyacinth on the water bodies, reduced profit from farm output and economic hardship. We recommend that interactive participation involving farmers, district and traditional authorities, and rural development agencies should be adopted to control the spread of the water hyacinth to a level where it will cease to impede the activities of the riparian people.
Protocol Based Similarity Evaluation of Publicly Available Synthetic and Real Fingerprint Datasets

Several attempts have been made recently to generate synthetic fingerprint data. This has become necessary after legal changes in Europe and some US states in order to allow and continue long-term developments in the field of fingerprint biometrics. Apart from utilizing traditional methods (often based on Gabor filters), deep convolutional neural networks are widely used to generate synthetic fingerprint samples. The current study aims at comparing several publicly available synthetic fingerprint datasets with several datasets that consist of imprints taken from real people. To enable a comparison, first a detailed description of these datasets is carried out. Secondly, an available 4-level protocol is used, which is supposed to show similarities and/or differences between real and synthetic fingerprint samples in terms of quality assessment and non-mated as well as mated comparison scores’ behavior. Furthermore, a new synthetic FP dataset composed of 50k samples is created and made publicly available in the course of this study.
Multi-Modal Cross Attention Network for Predicting Pathological Complete Response in Breast Cancer MRI

Predicting pathological complete response (pCR) to chemotherapy in breast cancer patients is a critical clinical endeavor, as it provides insights into individual patient responses and potential treatment outcomes. Despite the wealth of information available from MRI techniques such as DCE-MRI, pMRI, and SER, gleaning actionable insights from such complex data remains a challenging feat due to inherent variations in tumor biology, size, location, and blood flow patterns. Addressing this challenge, our study introduces the Multi-modal Cross Attention Network (MMCA), a novel deep learning approach designed to synergistically harness and emphasize the complementary information present across different MRI modalities. MMCA leverages an attention mechanism to adeptly weigh and integrate critical features, ensuring a more comprehensive and accurate pCR prediction. Comparative experiments against conventional deep learning architectures, including ResNet and DenseNet, showcased the prowess of MMCA. Specifically, through a 5-fold cross-validation, MMCA achieved an accuracy of 0.797 and an AUC of 0.758, outperforming other methods. This research not only demonstrates the potential of advanced deep learning algorithms combined with multimodal fusion techniques, but also sets a new benchmark for non-invasive pCR prediction using complex MRI data.