Miracle-dz/UniFAR-FTUs huggingface dataset:
Species diversity and phenetic relationship among accessions of api-api (Avicennia spp.) in Java based on morphological characters and ISSR markers

Abstract. Sabdanawaty FP, Purnomo, Daryono BS. 2021. Species diversity and phenetic relationship among accessions of api-api (Avicennia spp.) in Java based on morphological characters and ISSR markers. Biodiversitas 22: 193-198. Api-api (Avicennia spp.) from mangrove groups is rich in economic and ecological benefits. Research on the potential of api-api has been extensively done, but no research is specifically focused on the identification of api-api. This study aims to identify species diversity and phenetic relationships of api-api in Java based on morphological and molecular characters. The morphological analysis was based on 35 characters and referred to the descriptor. The PCA analysis on the morphological characters of Avicennia spp. showed a high diversity. Morphological characters have a high effect on grouping patterns such as habitat, leaf shape, leaf tip, leaf base, and petal color. Molecular observations were done on the polymorphism of DNA bands. The ISSR primers used were ISSR02, ISSR04, and ISSR10. PCR amplification of DNA was separated and visualized using a doc gel electrophoresis. The results showed a high diversity based on the percentage of DNA polymorphism. The research resulted in three species of Avicennia spp. in Java, namely Avicennia officinalis, A. alba, and A. marina. Phenetic relationships between Avicennia spp. form was based on similarity in character, not based on the area of origin. The environmental conditions in this study slightly opposed the character of Avicennia spp. , which presumably due to the similarity in various marine areas of Java such as temperature, pH, and the required salinity.
Simulator for Interactive and Effective Organization of Things in Edge Cluster Computing

Edge computing is intended to process events that occur at the endpoint of the Internet of Things (IoT) network quickly and intelligently. Edge regions must be organized effectively to facilitate cooperation so that the intention of edge computing can be realized. However, inevitably, many human and material resources are required in the process of arranging things in the edge area to confirm the appropriateness of the thing operation. To address this problem, we proposed a simulator that created a virtual space for edge computing and provided an interactive role and effective organization for edge things. The proposed simulator was aimed at Raspberry Pi as the physical hardware target. To prove the accuracy of the proposed simulator, the similarity between the proposed simulator and the physical target Raspberry Pi was evaluated based on three metrics while executing several applications. In the experiment, several edge-computing service applications were performed in various cluster architecture types formed by the proposed simulator. To support effective resource usage and fast real-time response for edge computing, the proposed simulator identified a suitable number of things in forming the edge cluster.
Near-Infrared-Spectroscopy for Determination of Carbon and Nitrogen in Indian Soils

ABSTRACT The present study aims to evaluate the potential of near-infrared reflectance (NIR) spectroscopy to determine the carbon and nitrogen content in soils and also to assess the effectiveness of NIR spectroscopy to predict carbon and nitrogen content in freshly collected soil samples. Soil samples (n = 179) were collected from different locations in India. Soil carbon and nitrogen contents were successfully predicted (R2 = 0.90 for carbon and R2 = 0.85 for nitrogen) by NIR spectroscopy. The root mean square error (RMSE) and ratio performance deviation (RPD) for the validation of predicted equations for carbon and nitrogen were 0.83 and 2.83 and 0.01 and 6.98, respectively. The efficacy of NIR spectroscopy on the prediction of carbon and nitrogen content in Indian soils is highly reliable. Water content in soil samples could affect the NIR absorbance spectra and in turn affect the quantification of carbon and nitrogen.
Urinary Medium-Chained Acyl-Carnitines Sign High Caloric Intake whereas Short-Chained Acyl-Carnitines Sign High -Protein Diet within a High-Fat, Hypercaloric Diet in a Randomized Crossover Design Dietary Trial

The western dietary pattern is known for its frequent meals rich in saturated fat and protein, resulting in a postprandial state for a large part of the day. Therefore, our aim was to investigate the postprandial glucose and lipid metabolism in response to high (HP) or normal (NP) protein, high-fat hypercaloric diet and to identify early biomarkers of protein intake and hepatic lipid accumulation. In a crossover design, 17 healthy subjects were randomly assigned to consume a HP or NP hypercaloric diet for two weeks. In parallel, a control group (CD; n = 10) consumed a weight-maintaining control diet. Biomarkers of postprandial lipid and glucose metabolism were measured in 24 h urine and in plasma before and following a meal challenge. The metabolic profile of urine but not plasma, showed increased excretion of 13C, carnitine and short chain acyl-carnitines after adaptation to the HP diet. Urinary excretion of decatrienoylcarnitine and octenoylcarnitine increased after adaptation to the NP diet. Our results suggest that the higher excretion of short-chain urinary acyl-carnitines could facilitate the elimination of excess fat of the HP diet and thereby reduce hepatic fat accumulation previously reported, whereas the higher excretion medium-chains acyl-carnitine could be early biomarkers of hepatic lipid accumulation.
DO TRANSFORMERS UNDERSTAND POLYNOMIAL SIMPLIFICATION?

Recently researchers have demonstrated that Transformers can be trained to learn symbolic tasks such as solving integration and differential equations in an end-toend fashion. In these setups, for an input symbolic expression, the Transformer predicts the final solution in a single step. Since such tasks may consist of a sequence of logical steps, question remains whether such networks have understood and learnt individual steps to reach the solution. To take a deeper look, we consider the task of polynomial simplification. Polynomials can be written in a simple normal form as a sum of monomials which are ordered in a lexicographic order. For a polynomial which is not necessarily in this normal form, a sequence of simplification steps is applied to reach the fully simplified (i.e., in the normal form) polynomial. For this task, we describe a synthetic Polynomial dataset generation algorithm which generates polynomials with unique proof steps. Then, we conduct an extensive analysis of the Transformer’s abilities to learn the polynomial simplification task along different dimensions.
Effect of Early Mobilization Intervention on Controlling Acquired Muscle Weakness among Pediatric Critically Ill Patients

Persistent immobility is linked with short and long-term disabilities in children admitted to critical area. Aim: the aim of the study was to assess the effect of early mobilization intervention on controling acquired muscle weakness among pediatric critically Ill patients. Randomized control trail was carried out on 80 pediatric patients divided equally into the study and the control groups in pediatric intensive care unit at Aswan University Hospital. Tools: Three tools were used to collect data: Tool I, included personal data , medical data and assessment scale for monitoring children's muscle strength . Tool II: Assessment delirium scale (Richmond Agitation-Sedation Scale. Tool III: Assess signs of intolerance. Results: after early mobilization intervention, mean of muscle strength in the study group (4.63 ± 0.59) was higher than the control group (3.52 ± 1.89) with highly statistically significant difference (p<0.001). Majority of children in the study group their ambulation distance at ICU discharge were increased. The mean time of moving out of bed was 2.90 ± 1.66 days for children in the study group vs. 3.20 ± 1.42 of them in the control group. Mean length of hospital stay was 4.28 ± 1.59 days for children in the study group vs. 6.38 ± 2.10 day of them in the control group with statistically significant difference between the two groups (P=0.005). Conclusion: Ambulation distance at ICU discharge was increased among studied group. There was significance difference between the two groups regarding length of hospital stay. Implementation of early mobilization intervention improves the children' muscle strength. Recommendation: Early mobilization protocol should be incorporated into daily clinical practice of pediatric intensive care unit.
Inter-choice dependent super-network weights

The automatic design of architectures for neural networks, Neural Architecture Search, has gained a lot of attention over the recent years, as the thereby created networks repeatedly broke state-of-the-art results for several disciplines. The network search spaces are often ﬁnite and designed by hand, in a way that a ﬁxed and small number of decisions constitute a speciﬁc architecture. Given these circumstances, inter-choice dependencies are likely to exist and affect the network search, but are unaccounted for in the popular one-shot methods. We extend the Single-Path One-Shot search-networks with additional weights that depend on combinations of choices and analyze their effect. Experiments in NAS-Bench 201 and SubImageNet based search spaces show an improved super-network performance in only-convolutions settings and that the overhead is nearly negligible for sequential network designs.
The Role of Dust, UV Luminosity and Large-scale Environment on the Escape of Lyα Photons: A Case Study of a Protocluster Field at z = 3.1

We present a detailed characterization of the Lyα properties for 93 Lyα emitters (LAEs) at z ∼ 3.1 selected from the D1 field of the Canada–France–Hawaii Telescope Legacy Survey, including 24 members of a massive protocluster. The median-stacked Lyα image shows an extended Lyα halo (LAH) surrounding the galaxy with the exponential scale length 4.9 ± 0.7 kpc, which accounts for roughly half of the total line flux. Accounting for the LAH contribution, the total Lyα escape fraction, f esc, is 40% ± 26%. Combining the data set with existing measurements, we find a dependence of f esc on the galaxy’s UV slope (β) and UV luminosity (L UV). The simultaneous use of both parameters allows prediction of f esc within 0.18 dex, a substantial improvement over 0.23 dex when only β is used. The correlation between f esc and E(B − V) suggests that Lyα photons undergo interstellar dust attenuation in a similar manner to continuum photons. Yet, Lyα transmission is typically higher than that expected for continuum photons at a similar wavelength by a factor, which depends on UV luminosity, up to 2 in the samples we studied. These results hint at complex geometries and physical conditions of the interstellar medium, which affect the Lyα transmission or production. Alternatively, the dust law may change with luminosity leading to an over- or underestimation of f esc. Finally, we report that protocluster LAEs tend to be bluer and more UV luminous than their field cousins, resulting in systematically higher f esc values. We speculate that it may be due to the widespread formation of young low-mass galaxies in dense gas-rich environments.
GuideBP: Guiding Backpropagation Through Weaker Pathways of Parallel Logits

Convolutional neural networks often generate multiple logits and use simple techniques like addition or averaging for loss computation. But this allows gradients to be distributed equally among all paths. The proposed approach guides the gradients of backpropagation along weakest concept representations. A weakness scores defines the class specific performance of individual pathways which is then used to create a logit that would guide gradients along the weakest pathways. The proposed approach has been shown to perform better than traditional column merging techniques and can be used in several application scenarios. Not only can the proposed model be used as an efficient technique for training multiple instances of a model parallely, but also CNNs with multiple output branches have been shown to perform better with the proposed upgrade. Various experiments establish the flexibility of the learning technique which is simple yet effective in various multi-objective scenarios both empirically and statistically.
Claim Detection in Biomedical Twitter Posts

Social media contains unfiltered and unique information, which is potentially of great value, but, in the case of misinformation, can also do great harm. With regards to biomedical topics, false information can be particularly dangerous. Methods of automatic fact-checking and fake news detection address this problem, but have not been applied to the biomedical domain in social media yet. We aim to fill this research gap and annotate a corpus of 1200 tweets for implicit and explicit biomedical claims (the latter also with span annotations for the claim phrase). With this corpus, which we sample to be related to COVID-19, measles, cystic fibrosis, and depression, we develop baseline models which detect tweets that contain a claim automatically. Our analyses reveal that biomedical tweets are densely populated with claims (45 % in a corpus sampled to contain 1200 tweets focused on the domains mentioned above). Baseline classification experiments with embedding-based classifiers and BERT-based transfer learning demonstrate that the detection is challenging, however, shows acceptable performance for the identification of explicit expressions of claims. Implicit claim tweets are more challenging to detect.
Passive soft-reset controllers for nonlinear systems

Soft-reset controllers are introduced as a way to approximate hard-reset controllers. The focus is on implementing reset controllers that are (strictly) passive and on analyzing their interconnection with passive plants. A passive hard-reset controller that has a strongly convex energy function can be approximated as a soft-reset controller. A hard-reset controller is a hybrid system whereas a soft-reset controller corresponds to a differential inclusion, living entirely in the continuous-time domain. This feature may make soft-reset controllers easier to understand and implement. A soft-reset controller contains a parameter that can be adjusted to better approximate the action of the hard-reset controller. Closed-loop asymptotic stability is established for the interconnection of a passive soft-reset controller with a passive plant, under appropriate detectability assumptions. Several examples are used to illustrate the efficacy of soft-reset controllers.
Symplectic cohomology of compound Du Val singularities

We compute symplectic cohomology for Milnor fibres of certain compound Du Val singularities that admit small resolution by using homological mirror symmetry. Our computations suggest a new conjecture that the existence of a small resolution has strong implications for the symplectic cohomology and conversely. We also use our computations to give a contact invariant of the link of the singularities and thereby distinguish many contact structures on connected sums of $S^2 \times S^3$.
Restoring degraded speech via a modified diffusion model

There are many deterministic mathematical operations (e.g. compression, clipping, downsampling) that degrade speech quality considerably. In this paper we introduce a neural network architecture, based on a modification of the DiffWave model, that aims to restore the original speech signal. DiffWave, a recently published diffusion-based vocoder, has shown state-of-the-art synthesized speech quality and relatively shorter waveform generation times, with only a small set of parameters. We replace the mel-spectrum upsampler in DiffWave with a deep CNN upsampler, which is trained to alter the degraded speech mel-spectrum to match that of the original speech. The model is trained using the original speech waveform, but conditioned on the degraded speech mel-spectrum. Post-training, only the degraded mel-spectrum is used as input and the model generates an estimate of the original speech. Our model results in improved speech quality (original DiffWave model as baseline) on several different experiments. These include improving the quality of speech degraded by LPC-10 compression, AMR-NB compression, and signal clipping. Compared to the original DiffWave architecture, our scheme achieves better performance on several objective perceptual metrics and in subjective comparisons. Improvements over baseline are further amplified in a out-of-corpus evaluation setting.
A Gaussian Process Model of Cross-Category Dynamics in Brand Choice

Understanding individual customers’ sensitivities to prices, promotions, brand, and other aspects of the marketing mix is fundamental to a wide swath of marketing problems, including targeting and pricing. Companies that operate across many product categories have a unique opportunity, insofar as they can use purchasing data from one category to augment their insights in another. Such cross-category insights are especially crucial in situations where purchasing data may be rich in one category, and scarce in another. An important aspect of how consumers behave across categories is dynamics: preferences are not stable over time, and changes in individual-level preference parameters in one category may be indicative of changes in other categories, especially if those changes are driven by external factors. Yet, despite the rich history of modeling cross-category preferences, the marketing literature lacks a framework that flexibly accounts for correlated dynamics, or the cross-category interlinkages of individual-level sensitivity dynamics. In this work, we propose such a framework, leveraging individual-level, latent, multi-output Gaussian processes to build a non-parametric Bayesian choice model that allows information sharing of preference parameters across customers, time, and categories. We apply our model to grocery purchase data, and show that our model detects interesting dynamics of customers’ price sensitivities across multiple categories. Managerially, we show that capturing correlated dynamics yields substantial predictive gains, relative to benchmarks. Moreover, we find that capturing correlated dynamics can have implications for understanding changes in consumers preferences over time, and developing targeted marketing strategies based on those dynamics.
Speed Planning Using Bezier Polynomials with Trapezoidal Corridors

To generate safe and real-time trajectories for an autonomous vehicle in dynamic environments, path and speed decoupled planning methods are often considered. This paper studies speed planning, which mainly deals with dynamic obstacle avoidance given the planning path. The main challenges lie in the decisions in non-convex space and the trade-off between safety, comfort and efficiency performances. This work uses dynamic programming to search heuristic waypoints on the S-T graph and to construct convex feasible spaces. Further, a piecewise Bezier polynomials optimization approach with trapezoidal corridors is presented, which theoretically guarantees the safety and optimality of the trajectory. The simulations verify the effectiveness of the proposed approach.
The supF assay for understanding DNA adduct-induced mutagenesis : traditional application and development of a site-specific version

The supF assay for understanding DNA adduct-induced mutagenesis: traditional application and development of a site-specific version by Evagelos Liapis Understanding the origin of mutations is central to understanding the aetiology of cancer. Several mutagenesis assay systems have been developed to measure the potency and mutagenic specificity of genotoxins and to study mutational mechanisms. A popular mutagenesis system is the supF assay, which employs the pSP189 double stranded shuttle vector plasmid that can be treated with carcinogens in vitro and transfected into human cells. Following replication, recovered plasmids can be screened in indicator bacteria for induced mutations in the supF gene. A primary aim of this study was to establish and utilise the supF assay to investigate the mutagenicity of the cancer drug tamoxifen in target endometrial cells. In particular, the supF assay was employed to ascertain the mutations caused by two model reactive intermediates of tamoxifen, aacetoxytamoxifen and 4-hydroxytamoxifen quinone methide (4-OHtamQM) in methylated pSP189 plasmid. The plasmid was methylated in vitro in order to allow application of the LwPy53 algorithm which enables in silico prediction of the G—►T mutation distribution along the human p53 gene, using aacetoxytamoxifen and 4-OHtamQM-induced mutation data from the supF assay. Relative mutation frequencies increased proportionally with the adduct level for a-acetoxytamoxifen, up to ~18 times the background frequency, while 4OHtamQM failed to raise the mutation frequency above that of the untreated control. The majority of mutations in a-acetoxytamoxifen-treated plasmid were GC ->TA transversions, while GC->AT transitions predominated in both 4OHtamQM-treated plasmid and the untreated control. Based on the p53 G—>T transversion predictions, a-acetoxytamoxifen induced damage resulted in two hotspots at positions 244 and 273; these mutations might be expected to occur in tamoxifen associated endometrial tumours if tamoxifen adducts play a role in cancer development. The second part of the thesis was devoted to the development and validation of a novel site-specific assay in E.coli MBM7070 and human cells to assess the mutagenicity of individual adducts, rather than compounds. The assay, which is a site-specific version of the supF assay, was validated and utilized to investigate the mutagenic potential and types of mutations caused by individual 0 6-MeG adducts situated in intact double stranded or gapped plasmids, within two different sequence contexts, in both E.coli and human cells. The methods successfully established can be easily adapted to enable studies of any stable adduct that can be synthesized and incorporated into the plasmid.
Optimizing small BERTs trained for German NER

Currently, the most widespread neural network architecture for training language models is the so-called BERT, which led to improvements in various NLP tasks. In general, the larger the number of parameters in a BERT model, the better the results obtained in these NLP tasks. Unfortunately, the memory consumption and the training duration drastically increases with the size of these models. In this article, we investigate various training techniques of smaller BERT models: We combine different methods from other BERT variants, such as ALBERT, RoBERTa, and relative positional encoding. In addition, we propose two new fine-tuning modifications leading to better performance: CSE tagging and a modified form of LCRF. Furthermore, we introduce WWA, which reduces BERT memory usage and leads to a small increase in performance compared to classical Multi-Head-Attention. We evaluate these techniques on five public German NER tasks, of which two are introduced by this article.
Online smart charging algorithm with asynchronous electric vehicles demand

The increasing penetration of Electric Vehicles (EVs) and renewable energies into the grid necessitates tools to smooth the demand curve. To this end, this paper suggests an EV charging scheduling algorithm and a smart charging price. As EVs arrive at the charging station and leave at different times, the operator of the station applies at each EV arrival an online scheduling algorithm based on the concept of “water filling”. The EV charging price is guaranteed at their arrival and defined as a function of the online algorithm's output, following the idea of locational marginal pricing. A numerical comparison with the offline version of the algorithm - in which the operator knows in advance all future arrival and departure times - shows the efficiency of the suggested online scheduling algorithm.