Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of Sparse Suffix Trees and LCE Indexes in Optimal Time and Space

The notions of synchronizing and partitioning sets are recently introduced variants of locally consistent parsings with great potential in problem-solving. In this paper we propose a deterministic algorithm that constructs for a given readonly string of length $n$ over the alphabet $\{0,1,\ldots,n^{\mathcal{O}(1)}\}$ a variant of $\tau$-partitioning set with size $\mathcal{O}(b)$ and $\tau = \frac{n}{b}$ using $\mathcal{O}(b)$ space and $\mathcal{O}(\frac{1}{\epsilon}n)$ time provided $b \ge n^\epsilon$, for $\epsilon>0$. As a corollary, for $b \ge n^\epsilon$ and constant $\epsilon>0$, we obtain linear construction algorithms with $\mathcal{O}(b)$ space on top of the string for two major small-space indexes: a sparse suffix tree, which is a compacted trie built on $b$ chosen suffixes of the string, and a longest common extension (LCE) index, which occupies $\mathcal{O}(b)$ space and allows us to compute the longest common prefix for any pair of substrings in $\mathcal{O}(n/b)$ time. For both, the $\mathcal{O}(b)$ construction storage is asymptotically optimal since the tree itself takes $\mathcal{O}(b)$ space and any LCE index with $\mathcal{O}(n/b)$ query time must occupy at least $\mathcal{O}(b)$ space by a known trade-off (at least for $b \ge \Omega(n / \log n)$). In case of arbitrary $b \ge \Omega(\log^2 n)$, we present construction algorithms for the partitioning set, sparse suffix tree, and LCE index with $\mathcal{O}(n\log_b n)$ running time and $\mathcal{O}(b)$ space, thus also improving the state of the art.
Use of the Ion Exchange Method for the Determination of Stability Constants of Thorium with Humic and Fulvic Acids

Ardakani-Stevensons ion exchange method was modified to determine stability constants of Th with humic and fulvic acids at pH 6 and ionic strength / = 0.1 mol/1. It was found that 1:1 and 1:2 type complexes, respectively, are formed at different concentration ratios of Th to the humic substances.
Practical Parallel Self-testing of Bell States via Magic Rectangles

Self-testing is a method to verify that one has a particular quantum state from purely classical statistics. For practical applications, such as device-independent delegated verifiable quantum computation, it is crucial that one self-tests multiple Bell states in parallel while keeping the quantum capabilities required of one side to a minimum. In this work, we use the $3 \times n$ magic rectangle games (generalizations of the magic square game) to obtain a self-test for $n$ Bell states where the one side needs only to measure single-qubit Pauli observables. The protocol requires small input sizes [constant for Alice and $O(\log n)$ bits for Bob] and is robust with robustness $O(n^{5/2} \sqrt{\varepsilon})$, where $\varepsilon$ is the closeness of the ideal (perfect) correlations to those observed. To achieve the desired self-test, we introduce a one-side-local quantum strategy for the magic square game that wins with certainty, we generalize this strategy to the family of $3 \times n$ magic rectangle games, and we supplement these nonlocal games with extra check rounds (of single and pairs of observables).
Using Joint Display as an Analytic Process: An Illustration Using Bar Graphs Joint Displays From a Mixed Methods Study of How Beliefs Shape Secondary School Teachers’ Use of Technology

Qualitative researchers engaged in mixed methods studies need novel approaches for integrating qualitative and quantitative findings during analysis. Joint display visuals have become an integral part of the presentation of the findings of mixed methods studies and can be aptly described as an analytic tool owing to the process involved in building joint displays. In this article, our purpose is to illustrate the iterative process utilized in building joint displays using both bar graphs and observational field notes to understand a specific phenomenon. An explanatory sequential mixed methods study was conducted with Jamaican secondary school teachers that sought to understand how teachers’ beliefs shaped the use of technology. Based on the use of joint display analysis in this study, we illustrate how the insights gleaned and challenges encountered from each iteration of developing successive joint displays helped us to refine our understanding through an assessment of the organizational intent, analytic intent, and effectiveness of the visuals created. By engaging in the process of building successive joint displays, we identified inconsistencies in the findings that would likely not otherwise have been revealed. We learned that the inconsistencies represented two dimensions of the teachers’ beliefs, thus resulting in a more comprehensive understanding of the integrated data. The methodological insights gleaned from each iteration of building joint displays can be used by other researchers engaged in this process and enhance their understanding of the value of joint display as a powerful analytic tool.
Age and prior working experience effect on entrepreneurial intention

How to cite this paper: Sahinidis, A. G. , Xanthopoulou, P. I. , Tsaknis, P. A. , & Vassiliou, E. E. (2021). Age and prior working experience effect on entrepreneurial intention. Corporate & Business Strategy Review, 2(1), 18-26. https://doi.org/10.22495/cbsrv2i1art2 Copyright © 2021 The Authors This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). https://creativecommons.org/licenses/by/4
Cannabis and cannabinoid use in autism spectrum disorder: a systematic review

Abstract Introduction Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by persistent deficits in social communication and social interaction, associated with the presence of restricted and repetitive patterns of behavior, interests, or activities. Cannabis has been used to alleviate symptoms associated with ASD. Method We carried out a systematic review of studies that investigated the clinical effects of cannabis and cannabinoid use on ASD, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA checklist). The search was carried out in four databases: MEDLINE/PubMed, Scientific Electronic Library Online (SciELO), Scopus, and Web of Science. No limits were established for language during the selection process. Nine studies were selected and analyzed. Results Some studies showed that cannabis products reduced the number and/or intensity of different symptoms, including hyperactivity, attacks of self-mutilation and anger, sleep problems, anxiety, restlessness, psychomotor agitation, irritability, aggressiveness perseverance, and depression. Moreover, they found an improvement in cognition, sensory sensitivity, attention, social interaction, and language. The most common adverse effects were sleep disorders, restlessness, nervousness and change in appetite. Conclusion Cannabis and cannabinoids may have promising effects in the treatment of symptoms related to ASD, and can be used as a therapeutic alternative in the relief of those symptoms. However, randomized, blind, placebo-controlled clinical trials are necessary to clarify findings on the effects of cannabis and its cannabinoids in individuals with ASD. Systematic review registration International Prospective Register of Systematic Reviews (PROSPERO), code 164161.
Continual Learning via Bit-Level Information Preserving

Continual learning tackles the setting of learning different tasks sequentially. Despite the lots of previous solutions, most of them still suffer significant forgetting or expensive memory cost. In this work, targeted at these problems, we first study the continual learning process through the lens of information theory and observe that forgetting of a model stems from the loss of information gain on its parameters from the previous tasks when learning a new task. From this viewpoint, we then propose a novel continual learning approach called Bit-Level Information Preserving (BLIP) that preserves the information gain on model parameters through updating the parameters at the bit level, which can be conveniently implemented with parameter quantization. More specifically, BLIP first trains a neural network with weight quantization on the new incoming task and then estimates information gain on each parameter provided by the task data to determine the bits to be frozen to prevent forgetting. We conduct extensive experiments ranging from classification tasks to reinforcement learning tasks, and the results show that our method produces better or on par results comparing to previous state-of-the-arts. Indeed, BLIP achieves close to zero forgetting while only requiring constant memory overheads throughout continual learning1.
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning

Transfer learning has become the dominant paradigm for many natural language processing tasks. In addition to models being pretrained on large datasets, they can be further trained on intermediate (supervised) tasks that are similar to the target task. For small Natural Language Inference (NLI) datasets, language modelling is typically followed by pretraining on a large (labelled) NLI dataset before ﬁne-tuning with each NLI subtask. In this work, we explore Gradient Boosted Decision Trees (GBDTs) as an alternative to the commonly used Multi-Layer Perceptron (MLP) classiﬁcation head. GBDTs have de-sirable properties such as good performance on dense, numerical features and are effective where the ratio of the number of samples w.r. t the number of features is low. We then introduce FreeGBDT, a method of ﬁtting a GBDT head on the features computed during ﬁne-tuning to increase performance without additional computation by the neural network. We demonstrate the effectiveness of our method on several NLI datasets using a strong baseline model (RoBERTa-large with MNLI pretrain-ing). The FreeGBDT shows a consistent improvement over the MLP classiﬁcation head.
The IceCube Pie Chart: Relative Source Contributions to the Cosmic Neutrino Flux

Neutrino events from IceCube have recently been associated with multiple astrophysical sources. Interestingly, these likely detections represent three distinct astrophysical source types: active galactic nuclei (AGNs), blazars, and tidal disruption events (TDEs). Here, we compute the expected contributions of AGNs, blazars, and TDEs to the overall cosmic neutrino flux detected by IceCube based on the associated events, IceCube’s sensitivity, and the source types’ astrophysical properties. We find that, despite being the most commonly identified sources, blazars cannot contribute more than 11% of the total flux (90% credible level), consistent with existing limits from stacked searches. On the other hand, we find that either AGNs or TDEs could contribute more than 50% of the total flux (90% credible level), although stacked searches further limit the TDE contribution to ≲30%. We also find that so-far unknown source types contribute at least 10% of the total cosmic flux with a probability of 80%. We assemble a pie chart that shows the most likely fractional contribution of each source type to IceCube’s total neutrino flux.
Study of the morphological structure and chemical composition of the dentin of intact teeth and teeth with cervical pathology

Introduction: The microstructure and chemical composition of tooth dentin reflect the complex processes associated with pathological conditions. Therefore, the study of their features is relevant in understanding the tactics of diagnosis and prevention of cervical lesions. Aim of the research: A comparative analysis of the morphological structure and chemical composition of the dentin of intact teeth, teeth with cervical caries, and a wedge-shaped defect. Material and methods: The study included 29 clinically extracted teeth of both jaws and their longitudinal sections, from patients aged 25–54 years, using a JSM-6490 LV focused-beam electron microscope (scanning) with system of energy-dispersive X-ray microanalysis. The chemical composition of 235 dentine areas in the incisal region (tubercle), equator, and cervical area was determined as a percentage of the weights of carbon, oxygen, calcium, phosphorus, sodium, magnesium, sulphur, chlorine, zinc, potassium, and aluminium. Results: The differences in the content of oxygen, sodium, and zinc in the dentin of all studied anatomical regions were determined, depending on the state of hard dental tissues (p ≤ 0.05). The teeth with cervical caries had less oxygen and more zinc while the teeth with a wedge-shaped defect had less sodium (p ≤ 0.05). An inverse correlation was revealed between carbon and phosphorus, and carbon and calcium in the area of the incisal region (tubercle); direct correlation between phosphorus and calcium (p ≤ 0.05) at the equator and in the cervical region was also revealed. Conclusions: The heterogeneity of mineral dentin content in the studied zones is probably because of the load on various areas and their morphological characteristics.
Characterization of sleep quality in Brazilian Jiu-Jitsu fighters

Introduction Sleep quality can positively or negatively influences the physical performance of Brazilian Jiu-Jitsu fighters; however, the literature does not indicate whether the sleep quality of these fighters is good or poor. Objective Characterization of the sleep quality of Brazilian Jiu-Jitsu (BJJ) fighters, stratifying by weight category and color belts. Material and Methods A descriptive study that featured with 175 BJJ fighters, the Pittsburgh Sleep Quality Index (PSQI) was applied to assess sleep quality. Mean, standard deviation and 95% confidence interval of mean were used to characterize the data. Results All groups, regardless of weight category or color rank, were classified as poor sleepers (PSQI=5), which indicates a low sleep quality. Conclusion The sleep of BJJ fighters is characterized as poor, which may indicate negative outcomes in technical and physical performance.
Cross-Polarization Control in FSSs by Means of an Equivalent Circuit Approach

This paper presents an efficient equivalent circuit approach (ECA), based on a Floquet modal expansion, for the study of the co- and cross-polarization in frequency selective surfaces (FSS) formed by periodic arrays of patches/apertures in either single or stacked configurations. The ECA makes it possible the derivation of analytical expressions for the generalized scattering parameters associated with the proposed circuit networks. Furthermore, the proposed circuit approach is an efficient surrogate model that can be combined with optimization techniques and artificial intelligence algorithms for the efficient design of FSS structures, saving efforts in the computation compared to time-consuming full-wave simulators and tedious synthesis (simulation-assisted) techniques. Due to the simplicity of the topology of the involved networks, the ECA can also be advantageously used to gain physical insight. The proposed approach is applied and validated in different FSS configurations where the cross-pol component plays a fundamental role in the design, as in circular polarizers, polarization rotators, and reflectarray cells.
Bridging the Gap Between Global and Full Fluid Models: A Rapid Semi-Analytical Model for Spatially Resolved Descriptions of Electronegative Plasmas.

Scientific investigation of plasma phenomena can be undertaken through either experiments, or numerical and analytical modelling, for which there are a number of well-established options. Global models are quick to implement and have low computation cost, but approximate bulk values. Fluid models can take days to solve, but provide spatial profiles. This work details a different type of model, analytically similar to fluid models, but computationally closer to a global model, and able to give spatially resolved solutions for the challenging environment of electronegative plasmas. Equations are derived to describe the time averaged spatial profiles of densities, fluxes, and temperatures. Through extended analytical work and normalisations, the resulting differential equations can be solved with an initial value type integration scheme. This is found to be hundreds of times faster than boundary value type methods. Results and trends are analysed for a symmetrical capacitively coupled oxygen plasma, and relationships between properties are found to conform to the existing knowledge. The behaviour of the system is found to change depending on whether or not the self-interaction of charged species is significant compared to the interaction with the neutrals. Results from the semi-analytical model agree well with a significantly more detailed and computationally intensive fluid model. In addition to the bulk spatial profiles agreeing both qualitatively and quantitatively, the values of other measured plasma properties agree over a range of system pressures and powers. This comparison is demonstrated to be favourable when contrasted with the results of a global model. The dynamics of the neutral gas are found to be an important consideration for plasma densities greater than around one part per million. In this model the frictional forces from fast moving ions and thermal energy transfer from hot electrons are the leading cause of disturbance to the neutral properties.
Neural Program Repair with Execution-based Backpropagation

Neural machine translation (NMT) architectures have achieved promising results for automatic program repair. Yet, they have the limitation of generating low-quality patches (e.g., not compilable patches). This is because the existing works only optimize a purely syntactic loss function based on characters and tokens without incorporating program-specific information during neural network weight optimization. In this paper, we propose a novel program repair model called RewardRepair. The core novelty of RewardRepair is to improve NMT-based program repair with a loss function based on program compilation and test execution information, rewarding the network to produce patches that compile and that do not overfit. We conduct several experiments to evaluate RewardRepair showing that it is feasible and effective to use compilation and test execution results to optimize the underlying neural repair model. RewardRepair correctly repairs 207 bugs over four benchmarks. we report on repair success for 121 bugs that are fixed for the first time in the literature. Also, RewardRepair produces up to 45.3% of compilable patches, an improvement over the 39% by the state-of-the-art.
Highly Active Chiral Zinc Catalysts for Immortal Polymerization of β-Butyrolactone Form Melt Processable Syndio-Rich Poly(hydroxybutyrate)

Highly crystalline poly(hydroxybutyrate) suffers from high melting point and entanglement molecular weight. This leads to low melt strength, limits processing through regular techniques, and precludes many applications. In this work we report a series of racemic and enantiopure zinc catalysts supported by variously substituted diaminophenolate ancillary ligands which form high melt strength PHBs with different molecular weights and microstructure. These complexes are active for the highly controlled polymerization of β-butyrolactone (BBL); some can polymerize 2000 equiv of BBL in less than 30 min. Changing the steric bulk of the ligand forms PHBs of varied syndiotacticity (Pr = 0.75 to 0.55). These are highly robust systems capable of polymerizing an unprecedented 20000 equiv of BBL in the presence of 5000 equiv of benzyl alcohol. Thermorheological investigations reveal that the synthesized PHBs have surprisingly high melt strength at above the melting point. For processable PHBs, high density of entangle.
Performance of quality improvement teams and associated factors in selected regional referral hospitals in Tanzania: a cross-sectional study

Introduction quality improvement teams facilitate improvement in the performance of the health facilities and simultaneously improving the quality of health services. There is scanty information on the factors associated with performance of quality improvement teams. This study aimed to assess the perceptions of members of the quality improvement teams on the factors influencing the performance of quality improvement teams in regional referral hospitals in Tanzania. Methods a cross-sectional study was conducted in four regional referral hospitals in Tanzania. We used self-administered questionnaires to collect data from 61 members of quality improvement teams. Descriptive statistics were used to assess the perceived factors influencing team performance. Bivariate and multivariate logistic regression was used to test the association between perceptions of the team members and factors associated with team performance. Results the overall mean perception score on team performance was high at 27.51 ± 4.62. Five factors namely: training (83.6%); communication (75.1%); team cohesiveness (71.5%); clarity of roles and responsibility (70.2%); team size and composition (65.5%); and self-assessment and learning (56.2%) were considered as the main drivers of team performance. Inadequate management support obtained the lowest score (36.1%). Multivariable regression analysis established a significant association between training, communication, clarity of roles and responsibilities, team size and composition, self-assessment and learning, management support and team performance. Conclusion inadequate management support to the team was found to be a barrier to team performance. Managerial interventions should focus on provision of coaching and mentoring to the team while addressing resource challenges affecting the team performance.
Regulation of DNA methyltransferase gene expression by short peptides in nicotiana tabacum regenerants

Methylation of cytosine DNA residues is the most studied and stable of all epigenetic modifications. Methylation of eukaryotic DNA is carried out by DNA methyltransferases. DNA methylation is an active mechanism for controlling gene transcription and is usually associated with prolonged silencing of DNA. The effect of peptides AEDG and AEDL on the growth of calluses of tobacco (Nicotiana tabacum) at low concentrations (10−7 M) is similar to the effect of phytohormones, has a regulatory character, and is possibly epigenetic in nature. Peptides increase the expression of DNA methyltransferase genes. One of the possible mechanisms of regulation of DNA methyltransferase genes by AEDG and AEDL is their ability to bind to free DNA regions at certain CNG sites, which are also methylation sites of plant cytosine methyltransferases. The AEDG peptide preferably binds to the CAG site and the AEDL peptide to the CTG site. By binding to the same sites as DNA methyltransferase, peptides block methylation sites, thereby reducing the level of DNA methylation. The specific binding of peptides to different sites that we discovered can be of great importance in gene regulation, since peptides with different structures can block different DNA regions for methylation of certain genes, thereby activating or silencing their expression.
Kinetic analysis of karyopherin-mediated transport through the nuclear pore complex

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Effects of intraspecific cooperative interactions in large ecosystems

We analyze the role of the Allee effect - a positive correlation between population density and mean individual fitness - for ecological communities formed by a large number of species. Our study is performed using the generalized Lotka-Volterra model with random interactions between species. We obtain the phase diagram and analyze the nature of the multiple equilibria phase. Remarkable differences emerge with respect to the logistic growth case, thus revealing the major role played by the functional response in determining aggregate behaviors of large ecosystems.