Miracle-dz/UniFAR-FTUs huggingface dataset:
TIGA DUNIA DALAM KAIN DODOT PENARI BEDOYO KETAWANG

The motifs found in Dodot Batik are taken from natural elements such as birds, four-legged animals, snakes, flowers, butterflies, buildings, boats, corals, etc. Those motifs (Alasalasan motif) can be classified into three groups based on the natural areas to which they belong. Birds, butterflies, and the other things similar are regarded as the rulers of the upper nature, the place that belongs to Gods. Four-legged animals, flowers, etc. represents the middle nature, the place where human beings live. Meanwhile, snakes, boats, etc. represent the lower nature, in which those who have a bad life live. The three natural areas previously mentioned are inteded to remind human beings that they should remember God The Almighty, and they have to live their lives in surrender. If they live their lives in a wrong way, they would eventually find sorrow. Thus, in order to achieve a happy and peaceful life they have to remember God as well as to respect and appreciate each other. By doing all these, they will be united with God, which is exactly like what is presented in Alas-alasanmotifs in the clothes worn by Bedoyo KetawangDancers.
As funções dos tipos textuais no interior do gênero discurso de propaganda The functions of the te xtual types in the gender advertising speech

This article proposes a reflection about the teaching of reading and writing on the genre approach, focusing on the function that textual types play within the genre advertising discourse. It reflects on issues surrounding the conceptualization of textual genre / discourse genre, textual types / textual forms, observing the most appropriate definition of genre to teach mother language. The studied corpus is constituted by an advertising piece published in Folha de S. Paulo. And the reflections rest on the Functionalism (HALLIDAY, 1985), on the Semantic Pragmatic (VAN DIJK, 1979), on the Discourse Analysis (MAINGUENEAU, 2002).
Competitive Experience Replay

Deep learning has achieved remarkable successes in solving challenging reinforcement learning (RL) problems when dense reward function is provided. However, in sparse reward environment it still often suffers from the need to carefully shape reward function to guide policy optimization. This limits the applicability of RL in the real world since both reinforcement learning and domain-specific knowledge are required. It is therefore of great practical importance to develop algorithms which can learn from a binary signal indicating successful task completion or other unshaped, sparse reward signals. We propose a novel method called competitive experience replay, which efficiently supplements a sparse reward by placing learning in the context of an exploration competition between a pair of agents. Our method complements the recently proposed hindsight experience replay (HER) by inducing an automatic exploratory curriculum. We evaluate our approach on the tasks of reaching various goal locations in an ant maze and manipulating objects with a robotic arm. Each task provides only binary rewards indicating whether or not the goal is achieved. Our method asymmetrically augments these sparse rewards for a pair of agents each learning the same task, creating a competitive game designed to drive exploration. Extensive experiments demonstrate that this method leads to faster converge and improved task performance.
Characterizing IOMT/Personal Area Networks Landscape

The attention garnered by the Internet of Things (IoT) term has taken the world by a storm. IOT concepts have been applied to various domains revolutionizing the way business processes are conducted. Health Informatics is a nascent multidisciplinary field that aims at applying information engineering concepts to healthcare. The information traditionally came from a variety of sources such as healthcare IT systems but recently is being stored in variety of IOT devices. Application of IOT concepts is becoming the norm giving rise to the Internet of Medical Things (IOMT). However, IOMT introduces many challenges such as the real-time nature, security, and privacy of data along with others. Thus, understanding the underlying structure of IOMT is of paramount importance. Given the importance of the underlying networks, we explore various solutions currently employed in developing the Personal Area Networks (PANs) that are the underlying cornerstone of IOMT). PANs allow the sensors to measure the change in various stimuli and transmit the information via LANs and WANs to various stakeholders. This chapter will look at three standards that are currently used by both academia and industry along with Body Area Networks (BANs). The chapter also provides a survey of prevalent IOMT applications along with various vendors that provide such services.
Social Blacklist Prediction using a Heterogeneous Information Network

Blacklists are widely used in society to avoid interactions with inappropriate entities. For example, international organizations issue sanctions lists that are used to prohibit trade with entities that are involved in illegal activities. Financial institutions keep blacklists of inappropriate firms that have financial problems or environmental issues. They create their blacklists by gathering information from various news sources to keep their portfolios profitable as well as green. In the present paper, we focus on the prediction of blacklists in the finance domain. We construct a vast heterogeneous information network that covers the necessary information surrounding each firm, which is assembled using seven professionally curated datasets and two open datasets, which results in approximately 50 million nodes and 400 million edges in total. Exploiting this vast heterogeneous information network, we propose a model that can learn to predict firms that are more likely to be added to a blacklist in the near future. Our approach is tested using the negative news blacklist data of more than 35,000 firms worldwide from January 2012 to May 2018. Comparing with the state-of-the-art methods with and without the network, we show that the predictive accuracy is substantially improved when using the heterogeneous information network. This work suggests new ways to consolidate the diffuse information contained in big data to monitor dominant firms on a global scale for better risk management, more socially responsible investment, and the surveillance of dominant firms.
Measuring Neurobehavioral Disabilities Among Severe Brain Injury Survivors: Reports of Survivors and Proxies in the Chronic Phase

Background: Neurobehavioral disability (NBD) has a major influence on long-term psychosocial outcome following acquired brain injury, as it affects not only the survivor of the brain injury, but the whole family. Objectives: To investigate (1) the frequency of NBD among survivors of severe brain injury measured by the Danish version of the St Andrew's-Swansea Neurobehavioural Outcome Scale (SASNOS) rated by patients and proxies, (2) factors associated with NBD, and (3) concordance between reports of NBD completed by patients and proxies. Methods: SASNOS was administered at an outpatient unit as a part of a follow-up assessment after discharge from intensive neurorehabilitation. SASNOS consists of five factors describing the following domains: Interpersonal Behavior, Cognition, Aggression, Inhibition and Communication, and both the patient and a proxy were asked to complete the questionnaire. Data collection was conducted over a period of 2 years, and 32 patients and 31 proxies completed the questionnaire. Mean time since injury was 19.4 months (10.0 SD). Most patients were male (68.8%), and most proxies were female (58.1%). Most of the patients had suffered a traumatic brain injury (68.8%). Results: A fourth of this patient group reported themselves below the normal range on the major domains of Interpersonal Behavior and Cognition. Significant associations between proxies' reports and time since injury, cohabitant status, and the patient's score on the Extended Glasgow Outcome Scale were found. Furthermore, significant differences were found between patient and proxy ratings. Proxies rated patients as having fewer problems on the Interpersonal Behavior domain, and more problems in relation to Cognition. Cognition was the only domain, where patients rated themselves higher indicating fewer problems, compared with their proxies. On both the Aggression and Communication domains, proxies rated patients higher indicating fewer problems than the patients themselves. Conclusion: Danish brain injury survivors experienced NBD as measured by SASNOS. Differences were found between patient and proxy ratings in relation to Cognition and Interpersonal Behavior. The NBDs identified can affect the survivor's ability to reintegrate and participate in activities of daily living, emphasizing how a systematic assessment is required.
FurcaNet: An end-to-end deep gated convolutional, long short-term memory, deep neural networks for single channel speech separation

Deep gated convolutional networks have been proved to be very effective in single channel speech separation. However current state-of-the-art framework often considers training the gated convolutional networks in time-frequency (TF) domain. Such an approach will result in limited perceptual score, such as signal-to-distortion ratio (SDR) upper bound of separated utterances and also fail to exploit an end-to-end framework. In this paper we present an integrated simple and effective end-to-end approach to monaural speech separation, which consists of deep gated convolutional neural networks (GCNN) that takes the mixed utterance of two speakers and maps it to two separated utterances, where each utterance contains only one speaker's voice. In addition long short-term memory (LSTM) is employed for long term temporal modeling. For the objective, we propose to train the network by directly optimizing utterance level SDR in a permutation invariant training (PIT) style. Our experiments on the public WSJ0-2mix data corpus demonstrate that this new scheme can produce more discriminative separated utterances and leading to performance improvement on the speaker separation task.
DVID: Distributed Versioned Image-Oriented Dataservice

Open-source software development has skyrocketed in part due to community tools like github.com, which allows publication of code as well as the ability to create branches and push accepted modifications back to the original repository. As the number and size of EM-based datasets increases, the connectomics community faces similar issues when we publish snapshot data corresponding to a publication. Ideally, there would be a mechanism where remote collaborators could modify branches of the data and then flexibly reintegrate results via moderated acceptance of changes. The DVID system provides a web-based connectomics API and the first steps toward such a distributed versioning approach to EM-based connectomics datasets. Through its use as the central data resource for Janelia's FlyEM team, we have integrated the concepts of distributed versioning into reconstruction workflows, allowing support for proofreader training and segmentation experiments through branched, versioned data. DVID also supports persistence to a variety of storage systems from high-speed local SSDs to cloud-based object stores, which allows its deployment on laptops as well as large servers. The tailoring of the backend storage to each type of connectomics data leads to efficient storage and fast queries. DVID is freely available as open-source software with an increasing number of supported storage options.
Differences in N2O Fluxes and Denitrification Gene Abundance in the Wet and Dry Seasons Through Soil and Plant Residue Characteristics of Tropical Tree Crops

The conversion of forest to agricultural soils is a widespread activity in tropical systems, and its link to nitrous oxide (N2O) fluxes and nitrogen cycling gene abundance is relevant to understand environmental drivers that may interact with climate change. A current challenge to estimating N2O emissions from land use conversion is an incomplete understanding of crop-specific impacts on denitrifier communities and the N2O fluxes driven by differences in the above- and below-ground inputs with crop type. To address this knowledge gap in tree crops, we evaluated N2O fluxes and denitrification gene abundance and their relationships with soil and plant residue characteristics in citrus and eucalyptus plantations in the field and in soil incubations. We found that the accumulated N2O fluxes from soil were lower for the two agricultural field sites than those for their adjacent forest sites in dry and wet seasons. The N2O fluxes were higher in the wet season, and this seasonal difference persisted even when the soils collected from both seasons were incubated under the same moisture and temperature conditions in the lab for 30 days. Increased N2O fluxes in the wet season were accompanied by an increase in soil nirK and nosZ gene abundance, the dissolved organic carbon (DOC) concentration, and the total soil carbon (C) and nitrogen (N) content. In turn, the abundance of denitrifiers, as indicated by nirK, nirS and nosZ gene copy numbers, showed a low but significant positive correlation with soil bulk density. Our results suggest that soil moisture, leaf litter and crop residues influence the seasonal differences in both N2O fluxes and abundance of denitrifiers in citrus- and eucalyptus-cultivated soils, likely through effects on soil physicochemical characteristics. These findings highlight the overwhelming role of environmental drivers that can make investigating microbial drivers difficult in the field and open the possibility for a better understanding of N cycling processes in tropical soils based on paired field- and incubation-based experimentation.
Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions

Deep learning approaches have been established as the main methodology for video classification and recognition. Recently, 3-dimensional convolutions have been used to achieve state-of-the-art performance in many challenging video datasets. Because of the high level of complexity of these methods, as the convolution operations are also extended to an additional dimension in order to extract features from it as well, providing a visualization for the signals that the network interpret as informative, is a challenging task. An effective notion of understanding the network’s innerworkings would be to isolate the spatio-temporal regions on the video that the network finds most informative. We propose a method called Saliency Tubes which demonstrate the foremost points and regions in both frame level and over time that are found to be the main focus points of the network. We demonstrate our findings on widely used datasets for thirdperson and egocentric action classification and enhance the set of methods and visualizations that improve 3D Convolutional Neural Networks (CNNs) intelligibility. Our code 1 and a demo video 2 are also available.
Nanomaterials Exhibiting Enzyme-Like Properties (Nanozymes): Current Advances and Future Perspectives

Biological enzymes are macromolecular catalysts that catalyze the biochemical reactions of the natural systems. Although each enzyme performs a particular function, however, holds several drawbacks, which limits its utilization in broad-spectrum applications. Natural enzymes require strict physiological conditions for performing catalytic functions. Their limited stability in harsh environmental conditions, the high cost of synthesis, isolation, and purification are some of the significant drawbacks. Therefore, as an alternative to natural enzymes, recently several strategies have been developed including the synthesis of molecules, complexes, and nanoparticles mimicking their intrinsic catalytic properties. Nanoparticles exhibiting the properties of an enzyme are termed as “nanozymes. ” Nanozymes offer several advantages over natural enzymes, therefore, a rapid expansion of the development of artificial biocatalysts. These advantages include simple methods of synthesis, low cost, high stability, robust catalytic performance, and smooth surface modification of nanomaterials. In this context, nanozymes are tremendously being explored to establish a wide range of applications in biosensing, immunoassays, disease diagnosis and therapy, theranostics, cell/tissue growth, protection from oxidative stress, and removal of pollutants. Considering the importance of nanozymes, this article has been designed to comprehensively discuss the different enzyme-like properties, such as peroxidase, catalase, superoxide dismutase, and oxidase, exhibited by various nanoparticles.
Volatile Compounds in Citrus Essential Oils: A Comprehensive Review

The essential oil fraction obtained from the rind of Citrus spp. is rich in chemical compounds of interest for the food and perfume industries, and therefore has been extensively studied during the last decades. In this manuscript, we provide a comprehensive review of the volatile composition of this oil fraction and rind extracts for the 10 most studied Citrus species: C. sinensis (sweet orange), C. reticulata (mandarin), C. paradisi (grapefruit), C. grandis (pummelo), C. limon (lemon), C. medica (citron), C. aurantifolia (lime), C. aurantium (bitter orange), C. bergamia (bergamot orange), and C. junos (yuzu). Forty-nine volatile organic compounds have been reported in all 10 species, most of them terpenoid (90%), although about half of the volatile compounds identified in Citrus peel are non-terpenoid. Over 400 volatiles of different chemical nature have been exclusively described in only one of these species and some of them could be useful as species biomarkers. A hierarchical cluster analysis based on volatile composition arranges these Citrus species in three clusters which essentially mirrors those obtained with genetic information. The first cluster is comprised by C. reticulata, C. grandis, C. sinensis, C. paradisi and C. aurantium, and is mainly characterized by the presence of a larger abundance of non-terpenoid ester and aldehyde compounds than in the other species reviewed. The second cluster is comprised by C. junos, C. medica, C. aurantifolia, and C. bergamia, and is characterized by the prevalence of mono- and sesquiterpene hydrocarbons. Finally, C. limon shows a particular volatile profile with some sulfur monoterpenoids and non-terpenoid esters and aldehydes as part of its main differential peculiarities. A systematic description of the rind volatile composition in each of the species is provided together with a general comparison with those in leaves and blossoms. Additionally, the most widely used techniques for the extraction and analysis of volatile Citrus compounds are also described.
Function of Brassica napus BnABI3 in Arabidopsis gs1, an Allele of AtABI3, in Seed Development and Stress Response

Abscisic acid (ABA) has been implicated in plant adaptation to various environmental stresses in addition to the regulation of seed dormancy and leaf senescence. ABI3 is a B3 domain-containing family protein and functions in the ABA signaling pathway during seed development. To date, the ABI3 orthologous have not been studied in Brassica napus. The aim of this study is to investigate the function of BnABI3 in plant development and stress response. Here, we identified an Arabidopsis line (gs1) from a population of mutagenized seeds and showed that GS1 is a new allele of AtABI3. When the Arabidopsis gs1 mutant was transformed with the BnABI3 gene, the transformed plants produced seeds that turned yellow and acquired desiccation tolerance. Moreover, BnABI3 regulates seed coat development and mucilage secretion by directly targeting the AtMUM1 and AtGATL5 genes. In addition, we showed that BnABI3 expression rescued gs1 freezing-induced green seed coloration by targeting AtSGR1/2 in transgenic Arabidopsis. BnABI3 is also involved in lateral root development and conferred a novel interaction between ABA and auxin signaling in roots. The potential role of ABI3 protein in endoplasmic reticulum homoeostasis was also tested. Altogether, our results indicated that BnABI3 mediates both plant development and the stress response.
Novel Cytonuclear Combinations Modify Arabidopsis thaliana Seed Physiology and Vigor

Dormancy and germination vigor are complex traits of primary importance for adaptation and agriculture. Intraspecific variation in cytoplasmic genomes and cytonuclear interactions were previously reported to affect germination in Arabidopsis using novel cytonuclear combinations that disrupt co-adaptation between natural variants of nuclear and cytoplasmic genomes. However, specific aspects of dormancy and germination vigor were not thoroughly explored, nor the parental contributions to the genetic effects. Here, we specifically assessed dormancy, germination performance and longevity of seeds from Arabidopsis plants with natural and new genomic compositions. All three traits were modified by cytonuclear reshuffling. Both depth and release rate of dormancy could be modified by a changing of cytoplasm. Significant changes on dormancy and germination performance due to specific cytonuclear interacting combinations mainly occurred in opposite directions, consistent with the idea that a single physiological consequence of the new genetic combination affected both traits oppositely. However, this was not always the case. Interestingly, the ability of parental accessions to contribute to significant cytonuclear interactions modifying the germination phenotype was different depending on whether they provided the nuclear or cytoplasmic genetic compartment. The observed deleterious effects of novel cytonuclear combinations (in comparison with the nuclear parent) were consistent with a contribution of cytonuclear interactions to germination adaptive phenotypes. More surprisingly, we also observed favorable effects of novel cytonuclear combinations, suggesting suboptimal genetic combinations exist in natural populations for these traits. Reduced sensitivity to exogenous ABA and faster endogenous ABA decay during germination were observed in a novel cytonuclear combination that also exhibited enhanced longevity and better germination performance, compared to its natural nuclear parent. Taken together, our results strongly support that cytoplasmic genomes represent an additional resource of natural variation for breeding seed vigor traits.
Fine-Grain Checkpointing with In-Cache-Line Logging

Non-Volatile Memory offers the possibility of implementing high-performance, durable data structures. However, achieving performance comparable to well-designed data structures in non-persistent (transient) memory is difficult, primarily because of the cost of ensuring the order in which memory writes reach NVM. \@ Often, this requires flushing data to NVM and waiting a full memory round-trip time. In this paper, we introduce two new techniques: Fine-Grained Checkpointing, which ensures a consistent, quickly recoverable data structure in NVM after a system failure, and In-Cache-Line Logging, an undo-logging technique that enables recovery of earlier state without requiring cache-line flushes in the normal case. We implemented these techniques in the Masstree data structure, making it persistent and demonstrating the ease of applying them to a highly optimized system and their low (5.9-15.4%) runtime overhead cost.