Miracle-dz/UniFAR-FTUs huggingface dataset:
A Note on Convolving Refinable Function Vectors

Abstract : When convolving two refinable function vectors which give rise to convergent subdivision schemes, the convolved scheme is again convergent. Moreover, the conditions on the mask symbols which characterize the approximation order of the associated shift invariant spaces show that the order of the convolved space is, essentially, the sum of the order of the two spaces originating from the convolution factors.
An atlas of fish genome evolution reveals delayed rediploidization following the teleost whole-genome duplication

Teleost fishes are ancient tetraploids stemming from an ancestral whole-genome duplication that may have contributed to the impressive diversification of this clade. Whole-genome duplications can occur via self-doubling (autopolyploidy) or via hybridization between different species (allopolyploidy). The mode of tetraploidization conditions evolutionary processes by which duplicated genomes return to a diploid state through meiosis resolution and subsequent genetic divergence (cytological and genetic rediploidization). How teleosts became tetraploid remains unresolved, leaving a fundamental gap to interpret their functional evolution. As legacy of the whole genome duplication, identifying orthologous and paralogous genomic regions across teleosts is challenging, hindering genome-wide investigations into their polyploid history. Here, we combine tailored gene phylogeny methodology together with a state-of-the-art ancestral karyotype reconstruction to establish the first high-resolution comparative atlas of paleopolyploid regions across 74 teleost genomes. We then leverage this atlas to investigate how rediploidization occurred in teleosts at the genome-wide level. We uncover that some duplicated regions maintained tetraploidy for over 60 million years, with three chromosome pairs diverging genetically only after the separation of major teleost families. This evidence suggests that the teleost ancestor was an autopolyploid. Further, we find evidence for biased gene retention along several duplicated chromosomes, contradicting current paradigms that asymmetrical evolution is specific to allopolyploids. Altogether, our results offer novel insights into genome evolutionary dynamics following ancient polyploidizations in vertebrates.
NOVA SAGITTARIUS 1974.

Line identifications and radial velocities ofNova Sgr 1974 (= V3888 Sgr) determined from spectrograms taken in October and November 1974 in Israel and Australia are presented. The spectra and the fading rate of the light curve indicate that Nova Sgr 1974 was discovered approximately 2 or 3 magnitudes beyond maximum light. Provided the initial phases of evolution of the nova were normal, it may have been as bright as m 6 or 7 at maximum light. Both the measured radial velocities and an upper limit for the outburst amplitude indicate that Nova Sgr 1974 was a fast nova with t3 10 days. Alternatively, the nova was not typical of other observed Galactic novae. Key words: nova - radial velocity
Introduction: Comparative Approaches to Data and Methods in Corpus Linguistics

Egbert do not work in a comparative paradigm. Ultimately the comparison between results therefore needs to be made in hindsight, across different individual studies, which, moreover, focus on different phenomena and thus investigate whether the answers to individual questions converge in a more abstract way. Another noteworthy work addressing affordances of current corpus research is the electronic book-length edition by Hiltunen, McVeigh and Säily (2017). As their title suggests, the focus of the discussion is on the size of corpora and text collections as well as the richness of annotation. The statistical problems discussed by contributions revolve primarily around handling and manipulating big and rich data, interfaces to text archives and the question of the quality of data, and thus have a different, though related slant. Each chapter focuses on one dataset and one methodological approach.
Towards Including Instructor Features in Student Grade Prediction

Student grade prediction is a popular task for learning analytics, given grades are the traditional form of student performance. However, no matter the learning environment, student background, or domain content, there are things in common across most experiences in learning. In most previous machine learning models, previous grades are considered the strongest prognosis of future performance. Few works consider the breadth of instructor features, despite the evidence that a great instructor could change the course of a student’s future. We strive to determine the true impact of an instructor by analyzing student data from an undergraduate program and measuring the importance of instructor-related features in comparison with other feature types that may affect state-of-the-art student grade prediction models. We show that adding extensive instructor-related features improves grade prediction, when using the best supervised learning classifier and regressor.
Predicting Cognitive Engagement in Online Course Discussion Forums

The need to identify student cognitive engagement in online-learning settings has increased with our use of online learning approaches because engagement plays an important role in ensuring student success in these environments. Engaged students are more likely to complete online courses successfully, but this setting makes it more difficult for instructors to identify engagement. In this study, we developed predictive models for automating the identification o f cognitive engagement in online discussion posts. We adapted the Interactive, Constructive, Active, and Passive (ICAP) En-gagement theory [15] by merging ICAP with Bloom’s taxonomy. We then applied this adaptation of ICAP to label student posts ( N = 4,217), thus capturing their level of cognitive engagement. To investigate the feasibility of automatically identifying cognitive engagement, the labelled data were used to train three machine learning classifiers (i.e., decision tree, random forest, and support vector machine). Model inputs included features extracted by applying Coh-Metrix to student posts and non-linguistic contextual features (e.g., number of replies). The support vector machine model outperformed the other classifiers. Our findings suggest it is feasible to automatically identify cognitive engagement in online learning environments. Subsequent analyses suggest that new language features (e.g., AWL use) should be included because they support the identification of cognitive engagement. Such detectors could be used to help identify students who are in need of support or help adapt teaching practices and learning materials.
Using a Randomized Experiment to Compare the Performance of Two Adaptive Assessment Engines

Knowledge space theory (KST) is a mathematical framework for modeling and assessing student knowledge. While KST has successfully served as the foundation of several learning systems, recent advancements in machine learning provide an opportunity to improve on purely KST-based approaches to assessing student knowledge. As such, in this work we compare the performance of an existing KST-based adaptive assessment to that of a newly developed version— with this new version combining the predictive power of a neural network model with the strengths of existing KST-based approaches. Using a cluster randomized experiment containing data from approximately 140,000 assessments, we show that the new neural network assessment engine improves on the performance of the existing KST version, both on standard classiﬁcation metrics, as well as on measures more speciﬁc to the student learning experience.
Regiospecific Grafting of Chitosan Oligomers Brushes onto Silicon Wafers

: The functionalization of surfaces using chitosan oligomers is of great interest for a wide range of applications in biomaterial and biomedical fields, as chitosan oligomers can provide vari ‐ ous functional properties including biocompatibility, wetting, adhesion, and antibacterial activity. In this study, an innovative process for the regiospecific chemical grafting of reducing ‐ end ‐ modified chitosan oligomers brushes onto silicon wafers is described. Chitosan oligomers (COS) with well ‐ defined structural parameters (average DP ~19 and DA ~0%) and bearing a 2,5 ‐ anhydro ‐ D ‐ manno ‐ furanose (amf) unit at the reducing end were obtained via nitrous acid depolymerization of chi ‐ tosan. After a silanization step where silicon wafers were modified with aromatic amine derivatives, grafting conditions were studied to optimize the reductive amination between aldehydes of amf ‐ terminated COS and aromatic amines of silicon wafers. Functionalized surfaces were fully charac ‐ terized by AFM, ATR ‐ FTIR, ellipsometry, contact angle measurement, and ToF ‐ SIMS techniques. Smooth surfaces were obtained with a COS layer about 3 nm thick and contact angle values between 72° and 76°. Furthermore, it was shown that the addition of the reducing agent NaBH 3 CN could positively improve the COS grafting density and/or led to a better stability of the covalent grafting to hydrolysis. Finally, this study also showed that this grafting process is also efficient for chitosan oligomers of higher DA (i.e., ~21%).
No Meaning Left Unlearned: Predicting Learners' Knowledge of Atypical Meanings of Words from Vocabulary Tests for Their Typical Meanings

Language learners are underserved if there are unlearned meanings of a word that they think they have already learned. For example,“circle”as a noun is well known, whereas its use as a verb is not. For artificial-intelligence-based support systems for learning vocabulary, assessing each learner’s knowledge of such atypical but common meanings of words is desirable. However, most vocabulary tests only test the typical meanings of words, and the texts used in the test questions are too short to apply readability formulae. We tackle this problem by proposing a novel dataset and a flexible model. First, we constructed a reliable vocabulary test in which learners answered questions regarding typical and atypical meanings of words. Second, we proposed a simple but powerful method for applying flexible and context-aware masked language models (MLMs) to learners’ answers in the above-mentioned vocabulary test results. This is a personalized prediction task, in which the results vary among learners for the same test question. By introducing special tokens that represent each learner, our method can reduce the personalized prediction task to a simple sequence classification task in which MLMs are applicable. In the evaluation, item response theory (IRT)-based methods, which cannot leverage the semantics of test questions, were used as baselines. The experimental results show that our method consistently and significantly outperformed the IRT-based baselines. More-over, our method is highly interpretable because one can obtain the learners’ language abilities from the first principal component scores of the token embeddings representing each learner.
Using Machine Learning Explainability Methods to Personalize Interventions for Students

Machine learning is a powerful method for predicting the outcomes of interactions with educational software, such as the grade a student is likely to receive. However, a predicted outcome alone provides little insight regarding how a student’s experience should be personalized based on that outcome. In this paper, we explore a generalizable approach for resolving this issue by personalizing learning using explanations of predictions generated via machine learning explain-ability methods. We tested the approach in a self-guided, self-paced online learning system for college-level introductory statistics topics that provided personalized interventions for encouraging self-regulated learning behaviors. The system used explanations generated by SHAP (SHapley Ad-ditive exPlanations) to recommend specific actions for students to take based on features that most negatively influenced predicted learning outcomes; an “expert system” comparison condition provided recommendations based on predefined rules. A randomized controlled trial of 73 participants (37 expert-system condition, 36 explanation condition) revealed similar learning and topic-choosing behavior between conditions, suggesting that XAI-informed interventions facilitated student statistics learning to a similar degree as expert-system interventions.
Engineering Organ-on-a-Chip to Accelerate Translational Research

We guide the use of organ-on-chip technology in tissue engineering applications. Organ-on-chip technology is a form of microengineered cell culture platform that elaborates the in-vivo like organ or tissue microenvironments. The organ-on-chip platform consists of microfluidic channels, cell culture chambers, and stimulus sources that emulate the in-vivo microenvironment. These platforms are typically engraved into an oxygen-permeable transparent material. Fabrication of these materials requires the use of microfabrication strategies, including soft lithography, 3D printing, and injection molding. Here we provide an overview of what is an organ-on-chip platform, where it can be used, what it is composed of, how it can be fabricated, and how it can be operated. In connection with this topic, we also introduce an overview of the recent applications, where different organs are modeled on the microscale using this technology.
Network Shocks and Social Support Among Spanish, Dutch, and Italian WhatsApp Users During the First Wave of the COVID-19 Crisis: An Exploratory Analysis of Digital Social Resilience

This research investigates how people across Europe used WhatsApp to cope with social isolation during the first wave of the COVID-19 pandemic. We conducted 30 semi-structured interviews (10 per country) with young adults (ages 25–49 years) from the urban areas of Barcelona (Spain), Groningen (the Netherlands), and Milan (Italy). Our results reveal that the pandemic shock “turtled up” WhatsApp communications, with individuals falling back on families and close friends (strong ties), from whom they obtained emotional and informational support to cope with the anxieties and fears of the pandemic, thereby increasing their resilience to the crisis. Despite the centrality of strong ties, our data also show that it was the persistence of, above all, humorous communication exchanges with acquaintances (weak ties) that helped individuals feel accompanied during the crisis. Furthermore, we found evidence of the emergence of communication with latent ties (e.g., former partners) as an added source of social support.
Is there Method in Your Mistakes? Capturing Error Contexts by Graph Mining for Targeted Feedback

The value of an instructor is that she exactly recognizes what the learner is struggling with and provides constructive feed-back straight to the point. This work aims at a step towards this type of feedback in the context of an introductory programming course, where students perform program execution tracing to align their understanding of Java instructions with reality. The students’ submissions are analyzed for re-peating mistakes across diﬀerent exercises by representing the context surrounding the error by a graph and applying graph mining techniques to discover their common grounds. The patterns need to be annotated only once and help to address misconceptions of individual students. They may also be used to select follow-up exercises automatically, that contain the same intricacy.
The modification of BCC model using facet analysis

In the most of Data Envelopment Analysis (DEA) models for avoiding the comparison of Decision Making Units (DMUs) with weak part of efficient frontier, usually a non-arachimedean infinitesimal is used as a lower bound for the multiplier. In BCC model these bounds are defined for Vi and Ur corresponding to the inputs and outputs weights. A modification of BCC model in input oriented is presented in this paper, where the bound is required only for u0. It is illustrated through an example.
A Wideband Low-Power Balun-LNA with Feedback and Current Reuse Technique

: This paper presents a low-noise ampliﬁer (LNA) with single to differential conversion (Balun) for multi-standard radio applications. The proposed LNA combines a common-gate (CG) stage for wideband input matching and a common-source (CS) stage to cancel the noise and distortion of the CG stage. Using the proposed technique, a low noise ﬁgure (NF) is achieved while providing a wideband of operation. Furthermore, a feedback connection from the CS stage to the gate of the CG is employed to boost the transconductance of the CG stage ( gm CG ), and an additional complementary transistor is applied at the CS stage using current reuse to increase the overall transconductance of the CS stage ( gm CS ) without increasing the power consumed by the stage. This LNA was designed using TSMC 65 nm technology, and post-layout simulation results show operation across 0.5–5 GHz, a maximum power gain of 20 dB, 4 dB minimum NF, and third-order intercept point (IIP3) of − 10 dBm while consuming only 5 mW of power from a 1.2 V supply.
Using Neural Network-Based Knowledge Tracing for a Learning System with Unreliable Skill Tags

Considerable amount of research in educational data mining has focused on developing efficient algorithms for Knowledge Tracing (KT). However, in practice, many real-world learning systems used at scale struggle to implement KT capabilities, especially if they weren’t originally designed for it. One key challenge is to accurately label existing items with skills, which often turns out to be a herculean task. In this paper, we investigate whether an increasingly popular approach to knowledge tracing, the use of neural network models, can be a partial solution to this problem. We conducted a case study within a commercial math blended learning system. Using the data collected from middle school students’ use of the system over two years, we compare the performance of a neural network-based KT model (DKVMN) in three scenarios: 1) with the original (possibly unreliable) system-provided skill tags, 2) with coarser-grained domain tags based on state standards, and 3) without inputting any mappings between content and skills. Our results suggest that including the system-provided skills in the training of the model leads to the worst performance. The best performance is observed when the skills are entirely disregarded. This supports the possibility of bypassing the laborious step of item-skill tagging in real-world learning systems which were not originally designed to work with KT models, especially if the goal is only to predict the performance of a student on future items. We discuss the implications of our findings for practice and future research.
Does Practice Make Perfect? Analyzing the Relationship Between Higher Mastery and Forgetting in an Adaptive Learning System

As outlined by Benjamin Bloom, students working within a mastery learning framework must demonstrate mastery of the core prerequisite material before learning any subsequent material. Since many learning systems in use today adhere to these principles, an important component of such systems is the set of rules or algorithms that determine when a student has demonstrated mastery. A relevant issue when discussing mastery learning is its durability—in particular, we are interested in the relationship between diﬀerent mastery learning thresholds and the forgetting of the learned material. As such, in this study we investigate this question using a large data set from the ALEKS adaptive learning system. Applying a quasi-experimental design, we ﬁnd evidence that, while a higher mastery threshold is initially associated with a higher rate of knowledge retention, after several weeks this diﬀerence has largely disappeared.
Modifying Deep Knowledge Tracing for Multi-step Problems

Previous studies suggest that Deep Knowledge Tracing (or DKT) has fundamental limitations that prevent it from supporting mastery learning on multi-step problems [15, 17]. Although DKT is quite accurate at predicting observed correctness in offline knowledge tracing settings, it often generates inconsistent predictions for knowledge components when used online. We believe this issue arises because DKT’s loss function does not evaluate predictions for skills and steps that do not have an observed ground truth value. To address this problem and enable DKT to better support on-line knowledge tracing, we propose the use of a novel loss function for training DKT. In addition to evaluating predictions that have ground truth observations, our new loss function also evaluates predictions for skills that do not have observations by using the ground truth label from the next observation of correctness for that skill. This approach ensures the model makes more consistent predictions for steps without observations, which are exactly the predictions that are needed to support mastery learning. We evaluated a DKT model that was trained using this updated loss by visualizing its predictions for a sample student learning sequence. Our analysis shows that the modified loss function produced improvements in the consistency of DKT model’s predictions.
A Variant of Performance Factors Analysis Model for Categorization

Many models of categorization focus on how people form and use knowledge of categories and make predictions about human categorization behaviors [19]. However, few (if any) of them implement these theories into item selection algorithms for category training. The performance Factors Analysis (PFA) model is an alternative to the Bayesian Knowledge Tracing model that tracks students’ learning of knowledge components and can be implemented into adaptive practice algorithms [17]. PFA-Difficulty model has been built to select items based on their difficulty level adaptively [4]. This paper describes how we are working to incorporate categorization theories into the PFA model so that it can be used for item selection. We used experiment data of Mandarin tone categorization training to test the model and suggest the implications of the results for item selection.
Stimulus-Evoked Activity Modulation of In Vitro Engineered Cortical and Hippocampal Networks

The delivery of electrical stimuli is crucial to shape the electrophysiological activity of neuronal populations and to appreciate the response of the different brain circuits involved. In the present work, we used dissociated cortical and hippocampal networks coupled to Micro-Electrode Arrays (MEAs) to investigate the features of their evoked response when a low-frequency (0.2 Hz) electrical stimulation protocol is delivered. In particular, cortical and hippocampal neurons were topologically organized to recreate interconnected sub-populations with a polydimethylsiloxane (PDMS) mask, which guaranteed the segregation of the cell bodies and the connections among the sub-regions through microchannels. We found that cortical assemblies were more reactive than hippocampal ones. Despite both configurations exhibiting a fast (<35 ms) response, this did not uniformly distribute over the MEA in the hippocampal networks. Moreover, the propagation of the stimuli-evoked activity within the networks showed a late (35–500 ms) response only in the cortical assemblies. The achieved results suggest the importance of the neuronal target when electrical stimulation experiments are performed. Not all neuronal types display the same response, and in light of transferring stimulation protocols to in vivo applications, it becomes fundamental to design realistic in vitro brain-on-a-chip devices to investigate the dynamical properties of complex neuronal circuits.