Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Kinetics on Lattices Based on the Kinetic Interaction Principle

Master equations define the dynamics that govern the time evolution of various physical processes on lattices. In the continuum limit, master equations lead to Fokker–Planck partial differential equations that represent the dynamics of physical systems in continuous spaces. Over the last few decades, nonlinear Fokker–Planck equations have become very popular in condensed matter physics and in statistical physics. Numerical solutions of these equations require the use of discretization schemes. However, the discrete evolution equation obtained by the discretization of a Fokker–Planck partial differential equation depends on the specific discretization scheme. In general, the discretized form is different from the master equation that has generated the respective Fokker–Planck equation in the continuum limit. Therefore, the knowledge of the master equation associated with a given Fokker–Planck equation is extremely important for the correct numerical integration of the latter, since it provides a unique, physically motivated discretization scheme. This paper shows that the Kinetic Interaction Principle (KIP) that governs the particle kinetics of many body systems, introduced in G. Kaniadakis, Physica A 296, 405 (2001), univocally defines a very simple master equation that in the continuum limit yields the nonlinear Fokker–Planck equation in its most general form.
The Effect of Adding Authorship Knowledge in Automated Text Scoring

Some language exams have multiple writing tasks. When a learner writes multiple texts in a language exam, it is not surprising that the quality of these texts tends to be similar, and the existing automated text scoring (ATS) systems do not explicitly model this similarity. In this paper, we suggest that it could be useful to include the other texts written by this learner in the same exam as extra references in an ATS system. We propose various approaches of fusing information from multiple tasks and pass this authorship knowledge into our ATS model on six different datasets. We show that this can positively affect the model performance at a global level.
Construction of All-in-Focus Images Assisted by Depth Sensing

Multi-focus image fusion is a technique for obtaining an all-in-focus image in which all objects are in focus to extend the limited depth of field (DoF) of an imaging system. Different from traditional RGB-based methods, this paper presents a new multi-focus image fusion method assisted by depth sensing. In this work, a depth sensor is used together with a colour camera to capture images of a scene. A graph-based segmentation algorithm is used to segment the depth map from the depth sensor, and the segmented regions are used to guide a focus algorithm to locate in-focus image blocks from among multi-focus source images to construct the reference all-in-focus image. Five test scenes and six evaluation metrics were used to compare the proposed method and representative state-of-the-art algorithms. Experimental results quantitatively demonstrate that this method outperforms existing methods in both speed and quality (in terms of comprehensive fusion metrics). The generated images can potentially be used as reference all-in-focus images.
Phase-type distributions in population genetics

Probability modelling for DNA sequence evolution is well established and provides a rich framework for understanding genetic variation between samples of individuals from one or more populations. We show that both classical and more recent models for coalescence (with or without recombination) can be described in terms of the so-called phase-type theory, where complicated and tedious calculations are circumvented by the use of matrices. The application of phase-type theory consists of describing the stochastic model as a Markov model by appropriately setting up a state space and calculating the corresponding intensity and reward matrices. Formulae of interest are then expressed in terms of these aforementioned matrices. We illustrate this by a few examples calculating the mean, variance and even higher order moments of the site frequency spectrum in the multiple merger coalescent models, and by analysing the mean and variance for the number of segregating sites for multiple samples in the two-locus ancestral recombination graph. We believe that phase-type theory has great potential as a tool for analysing probability models in population genetics. The compact matrix notation is useful for clarification of current models, in particular their formal manipulation (calculation), but also for further development or extensions.
Internal Model from Observations for Reward Shaping

Reinforcement learning methods require careful design involving a reward function to obtain the desired action policy for a given task. In the absence of hand-crafted reward functions, prior work on the topic has proposed several methods for reward estimation by using expert state trajectories and action pairs. However, there are cases where complete or good action information cannot be obtained from expert demonstrations. We propose a novel reinforcement learning method in which the agent learns an internal model of observation on the basis of expert-demonstrated state trajectories to estimate rewards without completely learning the dynamics of the external environment from state-action pairs. The internal model is obtained in the form of a predictive model for the given expert state distribution. During reinforcement learning, the agent predicts the reward as a function of the difference between the actual state and the state predicted by the internal model. We conducted multiple experiments in environments of varying complexity, including the Super Mario Bros and Flappy Bird games. We show our method successfully trains good policies directly from expert game-play videos.
Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus

Most Semantic Role Labeling (SRL) approaches are supervised methods which require a significant amount of annotated corpus, and the annotation requires linguistic expertise. In this paper, we propose a Multi-Task Active Learning framework for Semantic Role Labeling with Entity Recognition (ER) as the auxiliary task to alleviate the need for extensive data and use additional information from ER to help SRL. We evaluate our approach on Indonesian conversational dataset. Our experiments show that multi-task active learning can outperform single-task active learning method and standard multi-task learning. According to our results, active learning is more efficient by using 12% less of training data compared to passive learning in both single-task and multi-task setting. We also introduce a new dataset for SRL in Indonesian conversational domain to encourage further research in this area.
Antifungal Activity of Chitosan Nanoparticles Encapsulated With Cymbopogon martinii Essential Oil on Plant Pathogenic Fungi Fusarium graminearum

Application of synthetic fungicides in agricultural commodities has been restricted due to development of fungicide resistance fungi and deleterious impact on environment and health of farm animals and humans. Hence, there is an urge for development of mycobiocides, and the present study was undertaken to determine the antifungal activity of Cymbopogon martinii essential oil (CMEO) on post-harvest pathogen Fusarium graminearum. The CMEO was extracted by hydrodistillation and GC-MS chemical profile revealed the presence of 46 compounds and abundant was geraniol (19.06%). The minimum inhibitory concentration and minimum fungicidal concentration of CMEO were determined as 421.7 ± 27.14 and 618.3 ± 79.35 ppm, respectively. The scanning electron microscopic observation of CMEO exposed macroconidia was exhibited a detrimental morphology with vesicles, craters, protuberance, and rough surfaces related to control fungi. The CMEO induced the death of fungi through elevating intracellular reactive oxygen species and lipid peroxidation, and depleting ergosterol content. Regrettably, essential oils are highly volatile and become unstable and lose their biological features on exposure to light, heat, pH, moisture, and oxygen. To overcome these issues, chitosan encapsulated CMEO nanoparticles (Ce-CMEO-NPs) were prepared. The synthesized Ce-CMEO-NPs have spherical morphology with Zeta potential of 39.3–37.2 mV and their corresponding size was found in range of 455–480 nm. The Fourier transform infrared analysis confirmed that bio-active constituents of CMEO were well stabilized due to chitosan conjugation and successfully formed Ce-CMEO-NPs. The in vitro release assay observed that the release of CMEO is stabilized due to the complex formation with chitosan and thereby, increases the lifetime antifungal activity of CMEO by gradual release of antifungal constituents of Ce-CMEO-NPs. In conclusion, antifungal and antimycotoxin activities of CMEO and Ce-CMEO-NPs against F. graminearum were assessed in maize grains under laboratory conditions over a storage period of 28 days. Interestingly, Ce-CMEO-NPs were presented efficient and enhanced antifungal and antimycotoxin activities related to CMEO, and it could be due to perseverance of antifungal activity by controlled release of antifungal constituents from Ce-CMEO-NPs. The study concluded that Ce-CMEO-NPs could be highly appropriate as mycobiocides in safeguarding the agricultural commodities during storage period in agricultural and food industries.
OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification

This paper describes the collection and compilation of the OneStopEnglish corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification. The corpus consists of 189 texts, each in three versions (567 in total). The corpus is now freely available under a CC by-SA 4.0 license and we hope that it would foster further research on the topics of readability assessment and text simplification.
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python

The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared toward machine learning and reinforcement learning. Our software, called BindsNET1, enables rapid building and simulation of spiking networks and features user-friendly, concise syntax. BindsNET is built on the PyTorch deep neural networks library, facilitating the implementation of spiking neural networks on fast CPU and GPU computational platforms. Moreover, the BindsNET framework can be adjusted to utilize other existing computing and hardware backends; e.g., TensorFlow and SpiNNaker. We provide an interface with the OpenAI gym library, allowing for training and evaluation of spiking networks on reinforcement learning environments. We argue that this package facilitates the use of spiking networks for large-scale machine learning problems and show some simple examples by using BindsNET in practice.
Predicting misreadings from gaze in children with reading difficulties

We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching. Our approach employs several linguistic and gaze-based features to inform an ensemble of different classifiers, including multi-task learning models that let us transfer knowledge about individual readers to attain better predictions. Notably, the data we use in this work stems from noisy readings in the wild, outside of controlled lab conditions. Our experiments show that despite the noise and despite the small fraction of misreadings, gaze data improves the performance more than any other feature group and our models achieve good performance. We further show that gaze patterns for misread words do not fully generalize across readers, but that we can transfer some knowledge between readers using multitask learning at least in some cases. Applications of our models include partial automation of reading assessment as well as personalized text simplification.
Off-Balance Sheet Securitization , Bank Lending , and Corporate Innovation ∗

We investigate how corporate innovation is influenced by banks’ off-balance sheet securitization. Exploiting a recent mandate that removes the off-balance sheet status of some securitized assets, we find a reduction in innovation for firms borrowing from affected banks. The reduction is concentrated among firms whose banks experience more downward pressure on regulatory capital ratios and greater market discipline, and firms more dependent on external financing. Affected banks raised loan spreads and cut loan amounts after the mandate. The results suggest that off-balance sheet treatment of securitization has a real effect on firm innovation through bank lending.
Assessing UML Model Quality by Utilizing Metrics

Utilizing metrics is an efficient mechanism to measure and evaluate the quality of models, since many well-known and accepted software metrics have been successfully transferred from the code level to the model level. In a modeling context, OCL seems to be a natural and qualified choice for expressing them, because of its expressiveness and its availability in modeling tools. The metrics employed in this contribution will be defined on a metamodel level, and their evaluation will be done in an automatically generated metamodel instantiation that reflects the model under consideration. The employed metrics cover both the class scope and the model scope, and software designers can define their own metrics. We introduce a complete process for model quality assessment with pre-defined metrics. In addition, a metrics configuration defined by an experienced chief designer can be translated into OCL invariants and be evaluated to give quality feedback to software designers relieving them from detailed OCL expertise. Our approach has been successfully integrated into a software design tool.
Biocompatible Nanobioglass Reinforced Poly(ε-Caprolactone) Composites Synthesized via In Situ Ring Opening Polymerization

Poly(ε-caprolactone) (PCL) is a bioresorbable synthetic polyester widely studied as a biomaterial for tissue engineering and controlled release applications, but its low bioactivity and weak mechanical performance limits its applications. In this work, nanosized bioglasses with two different compositions (SiO2–CaO and SiO2–CaO–P2O5) were synthesized with a hydrothermal method, and each one was used as filler in the preparation of PCL nanocomposites via the in situ ring opening polymerization of ε-caprolactone. The effect of the addition of 0.5, 1 and 2.5 wt % of the nanofillers on the molecular weight, structural, mechanical and thermal properties of the polymer nanocomposites, as well as on their enzymatic hydrolysis rate, bioactivity and biocompatibility was systematically investigated. All nanocomposites exhibited higher molecular weight values in comparison with neat PCL, and mechanical properties were enhanced for the 0.5 and 1 wt % filler content, which was attributed to extensive interactions between the filler and the matrix, proving the superiority of in situ polymerization over solution mixing and melt compounding. Both bioglasses accelerated the enzymatic degradation of PCL and induced bioactivity, since apatite was formed on the surface of the nanocomposites after soaking in simulated body fluid. Finally, all samples were biocompatible as Wharton jelly-derived mesenchymal stem cells (WJ-MSCs) attached and proliferated on their surfaces.
Making Sense of Asynchrony in Interactive Data Visualizations

Asynchronous interfaces allow users to concurrently issue requests while existing ones are processed. While it is widely used to support non-blocking input when there is latency, it's not clear if people can make use of asynchrony as the data is updating, since the UI updates dynamically and the changes can be hard to interpret. Interactive data visualization presents an interesting context for studying the effects of asynchronous interfaces, since interactions are frequent, task latencies can vary widely, and results often require interpretation. In this paper, we study the effects of introducing asynchrony into interactive visualizations, under different latencies, and with different tasks. We observe that traditional asynchronous interfaces, where results update in place, induce users to wait for the result before interacting, not taking advantage of the asynchronous rendering of the results. However, when results are rendered cumulatively over the recent history, users perform asynchronous interactions and get faster task completion times.
Dynamic Translation of Spreadsheet Formulas to Problem Domain Narratives

Most errors in spreadsheets are formula-based. Referenced cells in formulas are normally presented using the traditional A1 cell referencing style. A spreadsheet user has to therefore mentally map referenced cells to their corresponding labels in order to comprehend a formula in the context of the problem domain. In this paper, we give a detailed description of an algorithm that can be used to dynamically translate traditional spreadsheet formulas to their problem domain equivalents which are easier to understand. The translation is done as one accesses a formula cell in a spreadsheet. The formula translation is based on inferred labels of referenced cells in the formula. The aim of the translation is to ease the cognitive load on the spreadsheet user and hence improving the error-prone spreadsheet development process. The paper also highlights some factors that need to be taken into consideration when dynamically translating spreadsheet formulas. The number of referenced cells in a formula and the distance of labels from referenced cells will determine the speed of the translation process hence affecting system responsiveness as one navigates through a spreadsheet. Unpredictable spatial arrangement of data in spreadsheets (spreadsheet layout) can also pose a challenge to the translation algorithm which may lead to mis-translation of spreadsheet formulas. These challenges might increase the cognitive load on the spreadsheet user hence negating the purpose of dynamically translating spreadsheet formulas.
Fast Similarity Matrix Profile for Music Analysis and Exploration

Most algorithms for music data mining and retrieval analyze the similarity between feature sets extracted from the raw audio. A conventional approach to assess similarities within or between recordings is to create similarity matrices. However, this method requires quadratic space for each comparison and typically requires costly post-processing of the matrix. We have recently proposed SiMPle, a powerful representation based on subsequence similarity join, which is applicable in several music analysis tasks. In this paper, we propose SiMPle-Fast a highly efficient method for exact computation of SiMPle that is up to one order of magnitude faster than SiMPle. Furthermore, we demonstrate the utility of SiMPle-Fast in cover music recognition and thumbnailing tasks and show that our method is significantly faster and more accurate than the state-of-the-art.
Predictive Values of Early Parental Loss and Psychopathological Risk for Physical Problems in Early Adolescents

Background: Several studies have suggested that the early loss of parents is a potentially traumatic experience, exposing adolescents to a higher risk for the onset of psychopathological symptoms. Furthermore, research has shown an association between the loss of a parent in childhood and subsequent physical illnesses, but much less attention has been given to the predictive role of loss in the development of physical illness in adolescence. Methods: From a larger normative sample, we selected 418 early adolescents (and their surviving parents) each of whom had lost a parent in their first 3 years of life. We evaluate the offspring’s and parents’ psychopathological symptoms, dissociation, and physical problems over a 6-year period. Univariate and multivariate Cox proportional hazard regression analyses with time-dependent variables were used to examine the predictive values of the adolescents’ and surviving parents’ psychopathological symptoms, and youths’ demographic characteristics (sex and age) for the occurrence of physical illness during a 6-year period of follow-up. Results: Independently of sex, the psychopathological risk of the surviving parents’ and adolescents’ affective problems and dissociation has been found to predict the occurrence of physical illnesses. Furthermore, dissociation was the most significant predictor of significant physical problems. Conclusion: These results may be relevant and an addition to the previous literature, opening up new possibilities for prevention and intervention that are oriented toward greater support for children who have experienced the loss of one parent and for surviving parents.
Selection Processes in Prison-Based Treatment Referrals: A Street-Level Bureaucracy Perspective

Studies indicated that detainees are not always allocated to treatment programs based on official guidelines. Street-level bureaucracy theory suggests that this is because government employees do not always perform policies as prescribed. This study aimed to assess whether this also applies to the allocation of offenders to treatment in Dutch penitentiary institutions, and aimed to determine which factors influenced this. The proposed questions were addressed by studying a group of 541 male prisoners who participated in the Dutch prison-based Prevention of Recidivism program. Results showed that official guidelines were, in most cases, not leading when referring detainees to programs. Instead, treatment referrals were influenced by a broad range of risk factors, as well as the length of an offender’s sentence.
Enabling autonomous mobile robots in dynamic environments with computer vision

Autonomous mobile robots are becoming more prevalent in our society as they become more useful. Often the environments we would like to see robots working in will be dynamic, with objects like people moving throughout. This thesis explores methods to make autonomous mobile robots more effective in the presence of dynamic objects. Specifically, two applications are developed on top of existing computer vision techniques where robots interact directly with moving objects. The first application involves multiple robots collaboratively sensing and following an arbitrary moving object. This is the first demonstration of its kind where robots jointly localize themselves and an object of interest while planning motion that is sympathetic to the vision system. Live robot experiments are conducted demonstrating the efficacy of the proposed system. The second application is a novel human-robot interaction system based on face engagement applied to an unmanned aerial vehicle (UAV). A unique use of facial recognition software enables an uninstrumented user to command a UAV with only their face. A series of experiments demonstrate the effectiveness of the interaction system for sending UAVs on a variety of flight trajectories.