Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified Mann-Kendall trend test for hydrological time series under the scaling hypothesis and its application

ABSTRACT The existence of persistence contaminates the results of the Mann-Kendall (MK) trend test. This paper proposes an improved MK test for hydrological data with both positive and negative persistence. To this end, the variance and distribution patterns of S were modified under the scaling hypothesis. Different estimators of the scaling coefficient were compared. According to the results, the generalized normal distribution is the best approximation to fit the exact distribution of S with both positive and negative persistence. The centred detrending moving average was demonstrated to be the best estimator of the scaling coefficient for series with a trend. The improved MK test was proven to be more advantageous than other trend detection methods for the synthetic time series and observed precipitation data obtained from nine hydrometric stations located in China. With this modification, the application of the MK test can be extended to long-term persistent data.
Intercarrier interference analysis for massive MIMO fractional Fourier transform‐OFDM systems over time‐varying channels

Massive multiple‐input multiple‐output orthogonal frequency division multiplexing (MIMO‐OFDM) system is an approach to increase data rate and spectral efficiency of 5G wireless communication systems. It's easy to analyze the performance of any system with the assumption of immobile users, but if the user is moving with varying velocity, then the communication between user and base station (BS) is very difficult to maintain. Under high mobility, rapidly fading, doubly dispersive, and nonstationary environment, the conventional system fails to respond and leads to the problem of intercarrier interference (ICI). To overcome these problems, fractional Fourier transform (FrFT) has been used in place of Fourier transform (FT) to increase the correlation coefficient, to mitigate effect of ICI, and to improve bit error rate. In this paper, novel formulae for correlation coefficient, average power of ICI, and closed form bit error expression have been derived for the proposed system by considering fast‐fading model and imperfect channel state information. Finally, the comparison of both the systems conventional and proposed proved that the proposed system has improved bit error rate by 40% and intercarrier interference cancelation by 70%.
Secure Communication for Uplink Cellular Networks Assisted with Full-Duplex Device-to-Device User

In this paper, the secure communication based on the full-duplex (FD) device-to-device (D2D) in cellular networks is proposed. For the proposed scheme, the novel model is established, in which a D2D user is played as a relay operating in FD mode to assist in the secure transmission of uplink information. Considering that the D2D user as a relay is untrusted, D2D link rate maximization is formulated with the constraint of secrecy rate, which ensures the security of uplink cellular networks. To cope with the optimization problem, the optimal power allocation factors of the cellular user (CU) and the D2D user are jointly optimized. Firstly, by using the monotonicity of the objective function, the optimal solution of the power allocation factor at the D2D user can be obtained. Subsequently, the closed-form expression of the optimal power allocation factor at the CU is derived and verified that the solution is the global minimum point. Simulation results verify that the proposed scheme has better output performance than the conventional scheme.
Amrita_CEN_NLP @ WOSP 3C Citation Context Classification Task

Identification of the purpose and influence of citation is significant in assessing the impact of a publication. ‘3C’ Citation Context Classification Task in Workshop on Mining Scientific Publication is a shared task to address the abovementioned problems. This working note describes the submissions of Amrita_CEN_NLP team to the shared task. We used Random Forest with cost-sensitive learning for classification of sentences encoded into a vector of dimension 300.
Virtual Citation Proximity (VCP): Empowering Document Recommender Systems by Learning a Hypothetical In-Text Citation-Proximity Metric for Uncited Documents

The relatedness of research articles, patents, court rulings, web pages, and other document types is often calculated with citation or hyperlink-based approaches like co-citation (proximity) analysis. The main limitation of citation-based approaches is that they cannot be used for documents that receive little or no citations. We propose Virtual Citation Proximity (VCP), a Siamese Neural Network architecture, which combines the advantages of co-citation proximity analysis (diverse notions of relatedness / high recommendation performance), with the advantage of content-based filtering (high coverage). VCP is trained on a corpus of documents with textual features, and with real citation proximity as ground truth. VCP then predicts for any two documents, based on their title and abstract, in what proximity the two documents would be co-cited, if they were indeed co-cited. The prediction can be used in the same way as real citation proximity to calculate document relatedness, even for uncited documents. In our evaluation with 2 million co-citations from Wikipedia articles, VCP achieves an MAE of 0.0055, i.e. an improvement of 20% over the baseline, though the learning curve suggests that more work is needed.
Cross-Lingual Domain Adaptation for Dependency Parsing

We show how we can adapt parsing to low-resource domains by combining treebanks across languages for a parser model with treebank embeddings. We demonstrate how we can take advantage of in-domain treebanks from other languages, and show that this is especially useful when only out-of-domain treebanks are available for the target language. The method is also extended to low-resource languages by using out-of-domain treebanks from related languages. Two parameter-free methods for applying treebank embeddings at test time are proposed, which give competitive results to tuned methods when applied to Twitter data and transcribed speech. This gives us a method for selecting treebanks and training a parser targeted at any combination of domain and language.
Characterization of the Anti-Fungal Activity of Waste Ornamental Flowers (Calendula Officinalis and Chrysanthemum) Against Plant Pathogens

Calendula officinalis and Chrysanthemum are widely used for their medicinal values throughout the world especially in Europe, China and India. These traditional medicines have found usage especially in wound healing, jaundice control, blood purification, and as an antisplasmodic. Chemical studies have established the presence of various classes of compounds, with major proportion of triterpenoids, flavonoids, alkaloids, coumarines, quinones, volatile oil, carotenoids and amino acids. In the present investigation, the extracts of these flowers were isolated and checked for their anti-fungal activity against plant pathogen with variable concentrations using disc diffusion technique and alkaloid contents were also analyzed. The extracts were effective against all the tested fungi at 80% concentration
Synergistic Effect of Plasmonic Gold Nanoparticles Decorated Carbon Nanotubes in Quantum Dots/TiO2 for Optoelectronic Devices

Here, a facile approach to enhance the performance of solar‐driven photoelectrochemical (PEC) water splitting is described by means of the synergistic effects of a hybrid network of plasmonic Au nanoparticles (NPs) decorated on multiwalled carbon nanotubes (CNTs). The device based on TiO2–Au:CNTs hybrid network sensitized with colloidal CdSe/(CdSexS1−x)5/(CdS)1 core/alloyed shell quantum dots (QDs) yields a saturated photocurrent density of 16.10 ± 0.10 mA cm−2 [at 1.0 V vs reversible hydrogen electrode (RHE)] under 1 sun illumination (AM 1.5G, 100 mW cm−2), which is ≈26% higher than the control device. The in‐depth mechanism behind this significant improvement is revealed through a combined experimental and theoretical analysis for QDs/TiO2–Au:CNTs hybrid network and demonstrates the multifaceted impact of plasmonic Au NPs and CNTs: i) hot‐electron injection from Au NPs into CNTs and TiO2; ii) near‐field enhancement of the QDs absorption and carrier generation/separation processes by the plasmonic Au NPs; iii) enhanced photoinjected electron transport due to the highly directional pathways offered by CNTs. These results provide fundamental insights on the properties of QDs/TiO2–Au:CNTs hybrid network, and highlights the possibility to improve the performance of other solar technologies.
Emergence of Human Amygdala Functional Networks: 3 Months to 5 Years of Age

Although the amygdala’s role in shaping social behavior is especially important during early post-natal development, very little is known of human amygdala functional development before childhood. To address this important gap, this study used resting-state fMRI to examine early functional network development of the amygdala and its subregions in 80 participants from 3-months to 5-years of age. Whole brain functional connectivity with the whole amygdala and its laterobasal and superficial nuclear groups were largely similar to those seen in older children and adults, and functional distinctions between subregion networks exist already. These patterns suggest many amygdala functional circuits are intact from infancy, especially those that are part of larger motor, visual, auditory and subcortical (basal ganglia especially) networks. Notably, these observed robust amygdala functional networks in infancy precede reports to date of elicited amygdala reactivity in development. Developmental changes in connectivity were observed between the laterobasal nucleus and bilateral ventral temporal and motor cortex as well as between the superficial nuclei and medial thalamus, occipital cortex and a different region of motor cortex. These results show amygdala-subcortical and sensory-cortex connectivity begins refinement prior to childhood, though connectivity changes with associative and frontal cortical areas, seen after early childhood, were not evident in this age range. These findings represent early steps in understanding amygdala network dynamics across infancy through early childhood, an important period of emotional and cognitive development.
Genomic Analysis of IgG Antibody Response to Common Pathogens in Commercial Sows in Health-Challenged Herds

Losses due to infectious diseases are one of the main factors affecting productivity in the swine industry, motivating the investigation of disease resilience-related traits for genetic selection. However, these traits are not expected to be expressed in the nucleus herds, where selection is performed. One alternative is to use information from the commercial level to identify and select nucleus animals genetically superior for coping with pathogen challenges. In this study, we analyzed the genetic basis of antibody (Ab) response to common infectious pathogens in health-challenged commercial swine herds as potential indicator traits for disease resilience, including Ab response to influenza A virus of swine (IAV), Mycoplasma hyopneumoniae (MH), porcine circovirus (PCV2), and Actinobacillus pleuropneumoniae (APP; different serotypes). Ab response was measured in blood at entry into gilt rearing, post-acclimation (∼40 days after entering the commercial herd), and parities 1 and 2. Heritability estimates for Ab response to IAV, MH, and PCV2 ranged from 0 to 0.76. Ab response to APP ranged from 0 to 0.40. The genetic correlation (rG) of Ab response to IAV with MH, PCV2, PRRSV, and APPmean (average Ab responses for all serotypes of APP) were positive (>0.29) at entry. APPmean was negatively correlated with PCV2 and MH at entry and parity 2 but positively correlated with MH at post-acclimation and parity 1. Genomic regions associated with Ab response to different APP serotypes were identified on 13 chromosomes. The region on chromosome 14 (2 Mb) was associated with several serotypes of APP, explaining up to 4.3% of the genetic variance of Ab to APP7 at entry. In general, genomic prediction accuracies for Ab response were low to moderate, except average Ab response to all infectious pathogens evaluated. These results suggest that genetic selection of Ab response in commercial sows is possible, but with variable success depending on the trait and the time-point of collection. Future work is needed to determine genetic correlations of Ab response with disease resilience, reproductive performance, and other production traits.
Multi-objective Pareto front and particle swarm optimization algorithms for power dissipation reduction in microprocessors

The progress of microelectronics making possible higher integration densities, and a considerable development of on-board systems are currently undergoing, this growth comes up against a limiting factor of power dissipation. Higher power dissipation will cause an immediate spread of generated heat which causes thermal problems. Consequently, the system's total consumed energy will increase as the system temperature increase. High temperatures in microprocessors and large thermal energy of computer systems produce huge problems of system confidence, performance, and cooling expenses. Power consumed by processors are mainly due to the increase in number of cores and the clock frequency, which is dissipated in the form of heat and causes thermal challenges for chip designers. As the microprocessor’s performance has increased remarkably in Nano-meter technology, power dissipation is becoming non-negligible. To solve this problem, this article addresses power dissipation reduction issues for high performance processors using multi-objective Pareto front (PF), and particle swarm optimization (PSO) algorithms to achieve power dissipation as a prior computation that reduces the real delay of a target microprocessor unit. Simulation is verified the conceptual fundamentals and optimization of joint body and supply voltages (Vth-VDD) which showing satisfactory findings.
Health complexity assessment in primary care: A validity and feasibility study of the INTERMED tool

Background: While considerable attention has been devoted to patients health complexity epidemiology, comparatively less attention has been paid to tools to identify and describe, in a personalized and comprehensive way, complex patients in primary health care (PHC). Objective: To evaluate INTERMED tools validity and feasibility to assess health complexity in PHC. Design: Cross-sectional psychometric study. Setting: Three Brazilian PHC Units. Participants: 230 patients above 18 years of both sexes. Measurements: Spearmans rho assessed concurrent validity between the whole INTERMED and their four domains (biological, psychological, social, health system) with other well-validated instruments. Pearsons X2 measured associations of the sum of INTERMED current state items with use of PHC, other health services and medications. Cronbach Alpha assessed internal consistency. INTERMED acceptability was measured through patients views on questions and answers, understanding and application length as well as objective application length. Applicability was measured through patients views on its relevance to describe health aspects essential to care and INTERMEDs items-related information already existing in patients health records. Results: 18.3% of the patients were complex (INTERMED 20/21 cut-off). Spearman correlations located between 0.44 - 0.65. Pearson coefficients found were X2 = 26.812 and X2 = 26.883 (both p = 0.020) and X2 = 28.270 (p = 0.013). Cronbach Alpha was 0.802. All patients views were very favorable. Median application time was 7 minutes and 90% of the INTERMED interviews took up to 14 minutes. Only the biological domain had all its items described in more than 50% of the health records. Limitations: We utilized the cutoff point used in all previous studies, found in research performed in specialized health services. Conclusion: We found good feasibility (acceptability and applicability), and validity measures comparable to those found from specialized health services. Further investigations of INTERMED predictive validity and suitability for routine PHC use are worthwhile.
Overview of the 2020 WOSP 3C Citation Context Classification Task

The 3C Citation Context Classification task is the first shared task addressing citation context classification. The two subtasks, A and B, associated with this shared task, involves the classification of citations based on their purpose and influence, respectively. Both tasks use a portion of the new ACT dataset, developed by the researchers at The Open University, UK. The tasks were hosted on Kaggle, and the participated systems were evaluated using the macro f-score. Three teams participated in subtask A and four teams participated in subtask B. The best performing systems obtained an overall score of 0.2056 for subtask A and 0.5556 for subtask B, outperforming the simple majority class baseline models, which scored 0.11489 and 0.32249, respectively. In this paper we provide a report specifying the shared task, the dataset used, a short description of the participating systems and the final results obtained by the teams based on the evaluation criteria. The shared task has been organised as part of the 8th International Workshop on Mining Scientific Publications (WOSP 2020) workshop.
Fine-Grained Morpho-Syntactic Analysis for the Under-Resourced Language Chaghatay

We investigate part of speech (POS) tagging for Chaghatay, a historical language with a considerable amount of morphology but few available resources such as POS annotated corpora. In a situation where we have little training data but a large POS tagset, it is not obvious which method will be best to obtain an accurate POS tagger. We experiment with a conditional random field and a Recurrent Neural Network, augmenting the models with coarse grained POS tag information, and by utilizing additional data, either additional unannotated data used to train a language model or annotated data from a modern relative, Uyghur. Our results show that the combination of an RNN and pretraining with coarse grained POS tags reaches the highest accuracy of 76.17%.
Scubed at 3C task B - A simple baseline for citation context influence classification

We present our team Scubed’s approach in the 3C Citation Context Classification Task, Subtask B, citation context influence classification. Our approach relies on text based features transformed via tf-idf features followed by training a variety of simple models resulting in a strong baseline. Our best model on the leaderboard is a random forest classifier using only the citation context text. A replication of our analysis finds logistic regression and gradient boosted tree classifier to be the best performing model. Our submission code can be found at: https://github.com/napsternxg/Citation_Context_Classification.
Clause-Level Tense, Mood, Voice and Modality Tagging for German

We present a language-independent clausizer (clause splitter) based on Universal Dependencies (Nivre et al., 2016), and a clause-level tagger for grammatical tense, mood, voice and modality in German. The paper recapitulates verbal inﬂection in German—always juxtaposed with its close relative English—and transforms the linguistic theory into a rule-based algorithm. We achieve state-of-the-art accuracies of 92.6% for tense, 79.0% for mood, 93.8% for voice and 79.8% for modality in the literary domain. Our implementation is available at https://gitlab.gwdg. de/tillmann.doenicke/tense-tagger .
Dependency Relations for Sanskrit Parsing and Treebank

Dependency relations are needed for the development of a dependency treebank and a dependency parser. The guidelines1 for the development of treebank for Sanskrit proposed a set of dependency relations. Use of these relations for the development of a sentence generator and a dependency parser for Sanskrit demanded a need for an enhancement as well as a revision of these relations. In this paper, we discuss the revised version of these relations and discuss the cases where there is a possibility of multiple tagging either due to the ellipsis of certain arguments or due to the possible derivational morphological analysis. This led us to arrive at specific instructions for handling such cases during the tagging. A treebank with around 4000 sentences has been developed following these guidelines. Finally we evaluate a grammar based dependency parser for Sanskrit on this treebank and report its performance.
Electrically compensated, tattoo-like electrodes for epidermal electrophysiology at scale

We demonstrate sizable, unencapsulated e-tattoos with transformable serpentine design and Cartan curve–inspired transfer. Epidermal electrophysiology is widely carried out for disease diagnosis, performance monitoring, human-machine interaction, etc. Compared with thick, stiff, and irritating gel electrodes, emerging tattoo-like epidermal electrodes offer much better wearability and versatility. However, state-of-the-art tattoo-like electrodes are limited in size (e.g., centimeters) to perform electrophysiology at scale due to challenges including large-area fabrication, skin lamination, and electrical interference from long interconnects. Therefore, we report large-area, soft, breathable, substrate- and encapsulation-free electrodes designed into transformable filamentary serpentines that can be rapidly fabricated by cut-and-paste method. We propose a Cartan curve–inspired transfer process to minimize strain in the electrodes when laminated on nondevelopable skin surfaces. Unwanted signals picked up by the unencapsulated interconnects can be eliminated through a previously unexplored electrical compensation strategy. These tattoo-like electrodes can comfortably cover the whole chest, forearm, or neck for applications such as multichannel electrocardiography, sign language recognition, prosthetic control or mapping of neck activities.
Chronic Alcohol Exposure Induces Aberrant Mitochondrial Morphology and Inhibits Respiratory Capacity in the Medial Prefrontal Cortex of Mice

Alcohol use disorder (AUD) is characterized as a chronic, relapsing disease with a pattern of excessive drinking despite negative consequences to an individual’s life. Severe chronic alcohol use impairs the function of the medial prefrontal cortex (mPFC), which contributes to alcohol-induced cognitive and executive dysfunction. The mPFC contains more mitochondria compared to other cortical areas, which suggests mitochondrial damage may occur in AUD and trigger subsequent behavior change. Here, we identified morphological and functional changes in mitochondria in the mPFC in C57BL6/J mice after 8 h of withdrawal from chronic intermittent alcohol (CIA) exposure. Three-dimensional serial block-face scanning electron microscopy (SBFSEM) reconstruction revealed that CIA exposure elongated mPFC mitochondria and formed mitochondria-on-a-string (MOAS). Furthermore, alcohol significantly affected mitochondrial bioenergetics, including oxidative phosphorylation and electron transport, with inhibited aerobic respiration in mPFC mitochondria after CIA exposure. We also found decreased expression of fusion (mitofusin 2, Mfn2) and increased fission (mitochondrial fission 1 protein, Fis1) proteins in the mPFC of alcohol-treated mice. In sum, our study suggests that CIA exposure impairs mitochondrial dynamics and function in the mPFC.
Implementing an End-to-End Treebank-Informed Pipeline for Bulgarian

The paper reports on the implementation of an NLP pipeline for Bulgarian, developed within the spaCy framework and based on BulTreeBank as main source for training and test data. This new end-to-end pipeline aims to ensure easier technical maintenance and synchronization between modules, superior processing speeds – for use in real applications, and greater ﬂexibility of adaptation. We discuss the challenges encountered in the implementation process and the solutions adopted, including the architecture itself, as well as its quantitative and qualitative evaluation