Miracle-dz/UniFAR-FTUs huggingface dataset:
MBAIL: Multi-Batch Best Action Imitation Learning utilizing Sample Transfer and Policy Distillation

Batch reinforcement learning (RL) aims to learn a good control policy from a previously collected dataset without requiring additional interactions with the environment. Unfortunately, in the real world, we may only have a limited amount of training data for tasks we are interested in. Most batch RL methods are intended to learn a policy over one ﬁxed dataset, and are not intended to learn a policy that can perform well over other tasks. How can we leverage the advantages of batch RL while dealing with limited training data is another challenge in real world. In this work, we propose to add sample transfer and policy distillation to a leading Batch RL approach. The proposed methods are evaluated on multiple control tasks to showcase their effectiveness.
ISO-based Annotated Multilingual Parallel Corpus for Discourse Markers

Discourse markers carry information about the discourse structure and organization, and also signal local dependencies or epistemological stance of speaker. They provide instructions on how to interpret the discourse, and their study is paramount to understand the mechanism underlying discourse organization. This paper presents a new language resource, an ISO-based annotated multilingual parallel corpus for discourse markers. The corpus comprises nine languages, Bulgarian, Lithuanian, German, European Portuguese, Hebrew, Romanian, Polish, and Macedonian, with English as a pivot language. In order to represent the meaning of the discourse markers, we propose an annotation scheme of discourse relations from ISO 24617-8 with a plug-in to ISO 24617-2 for communicative functions. We describe an experiment in which we applied the annotation scheme to assess its validity. The results reveal that, although some extensions are required to cover all the multilingual data, it provides a proper representation of discourse markers value. Additionally, we report some relevant contrastive phenomena concerning discourse markers interpretation and role in discourse. This first step will allow us to develop deep learning methods to identify and extract discourse relations and communicative functions, and to represent that information as Linguistic Linked Open Data (LLOD).
Analytical Solution of Dynamical Transmission of Malaria disease Model Using Differential Transform Method

: The aim of this work is to compare analytical solution of Differential Transformation Method (DTM) with in-built solutions of Runge-Kutta Method (RK4) of MAPLE 18 software on dynamical transmission of malaria disease model. Mathematical model is developed in terms of nonlinear first order ordinary differential equations. Existence and uniqueness of model solution was determined and analytical solution of the model was obtained using Differential Transform Method. In order to show the efficiency of the method we compare the analytical solutions obtained by Differential Transform Method and fourth-order Runge-Kutta method. The validity of the DTM in solving the model is established by using the computer in-built RK4. We illustrated the profiles of the solutions of each of the compartments, from which we speculate that the DTM and RK4 solutions agreed well. Omoloye et al. (2021) worked on analysis of differential transformation method to obtain power series of Coronavirus disease model and accuracy of DTM is demonstrated against Runge-Kutta method of order four (RKM) numerical solution and it demonstrated high accuracy of the results. Plotted DTM solution is found to be in good agreement with the popular Runge-Kutta solution. Omoloye and Adewale (2021) developed an Ebola- Malaria co-infection model, in their work it shows that malaria is an important mimic or co-infection in potential Ebola virus disease patients. The model was analyzed for stability and it was established that the disease free equilibrium of each model and their co-infections were locally and globally asymptotically stable whenever the basic reproduction number is less than unity or endemic otherwise. Sensitivity analysis on the basic reproduction number reveal the most sensitive index value and the graph shows that by eliminating the co-infection contact rate of Ebola- malaria disease in the population in other to control Ebola-Malaria co-infections. To further extend the work of Omoloye and Adewale (2021) by performed the differential transform method on the malaria sub-model of Ebola- Malaria co-infection model in order to obtain the analytical solution of this model.
Cloud-smart Musical Instrument Interactions

Large online music databases under Creative Commons licenses are rarely recorded by well-known artists, therefore conventional metadata-based search is insufficient in their adaptation to instrument players’ needs. The emerging class of smart musical instruments (SMIs) can address this challenge. Thanks to direct internet connectivity and embedded processing, SMIs can send requests to repositories and reproduce the response for improvisation, composition, or learning purposes. We present a smart guitar prototype that allows retrieving songs from large online music databases using criteria different from conventional music search, which were derived from interviewing 30 guitar players. We investigate three interaction methods coupled with four search criteria (tempo, chords, key and tuning) exploiting intelligent capabilities in the instrument: (i) keywords-based retrieval using an embedded touchscreen; (ii) cloud-computing where recorded content is transmitted to a server that extracts relevant audio features; (iii) edge-computing where the guitar detects audio features and sends the request directly. Overall, the evaluation of these methods with beginner, intermediate, and expert players showed a strong appreciation for the direct connectivity of the instrument with an online database and the approach to the search based on the actual musical content rather than conventional textual criteria, such as song title or artist name.
The effect of group re�ection on the development of Communication Skills in undergraduate nursing students

BACKGROUND Communication skills are one of the main capabilities expected from nursing students and the core of nursing care. This study was conducted with the aimed of the effect of group re�ection on the development of communication skills. Methods The study was a two-group quasi-experimental pre-test-post-test that was conducted at Fasa University of Medical Sciences in the south-west of Iran. The study population consisted of 48 second-semester nursing students enrolled in clinical fundamental of nursing course in February 2018. Group re�ection based on Borton's Development Framework was used in learning communication skills to the test groups for 6 sessions. Conventional method was used for control groups. Results The mean score of communication skills among students in both control and test groups, before and after the group re�ection sessions, indicated a signi�cant difference . 0.001).
Fast Interference in Lipid Profile: A Systematic Review

RESUMO Objective: To critically evaluate, through a careful review of the scientific literature, whether there are significant differences in the lipid profile dosage in fasted and non-fasting blood samples. Methods: Studies originally published in the English language and indexed in the last 5 years were analyzed. The inclusion and exclusion criteria were applied freely and independently by a reviewer, who judged the selected studies from the points raised in each item exposed (Table 1). Results: The scope of this review included 5 studies that met the selection criteria. The studies used were analyzed for the values of total cholesterol, high density lipoproteins (HDL), low density lipoproteins (LDL) and triglycerides dosed in the fasting and non-fasting states. In the studies analyzed, HDL and total cholesterol remained constant, LDL decreased in concentration and triglycerides increased in concentration between fasting and non-fasting state. Conclusions: This review confirms the premise presented by recent worldwide guidelines and consensus on the fact that fasting is not mandatory for the lipid profile, considering that no significant differences were found between fasting and non-fasting dosages, and even for the parameters that presented some divergence, it does not imply relevant clinical alterations, being necessary only to make a small adjustment in the reference values for the state without fasting.
A Deterministic Approach to the Synchronization of Cellular Automata

In this work we introduce a deterministic scheme of synchronization of linear and nonlinear cellular automata (CA) with complex behavior, connected through a master-slave coupling. By using a definition of Boolean derivative, we use the linear approximation of the automata to determine a function of coupling that promotes synchronization without perturbing all the sites of the slave system.
Temporally compounding heatwave–heavy rainfall events in Australia

Natural hazards often occur in combination with other natural hazards rather than as isolated events. While some combinations of hazards are well studied and their physical connection is increasingly understood, other combinations have received considerably less attention. High temperatures are known to be an important component for conditions that lead to heavy rainfall; however, sequences of heatwaves followed by heavy rainfall are not well understood, especially in a compound event context. Here, we analyse heatwave–heavy rainfall events across Australia using rainfall observations at hourly resolution. Our results show that heavy rainfall is more likely to occur if preceded by a heatwave, demonstrating that heatwave–heavy rainfall sequences should be seen as temporally compounding events. In particular, many regions in Australia experience both more frequent and more extreme wet days immediately following a heatwave. This behaviour is strongest in coastal regions, especially on the Australian east coast. These findings highlight the need for heatwave–heavy rainfall sequences to be studied as compounding events, as future changes in either hazard is likely to have impacts on related risks such as flash flooding.
Deciphering the association between Covid vaccine side effects and menstrual cycle

Introduction and Aim: In light of the ongoing pandemic Covid -19 and vaccination drive, our study intended to observe the association of reported side effects of Covid vaccine to menstrual cycle. There is no research done about covid vaccine effects on menstrual cycle. Materials and Methods: A cross-sectional online questionnaire-based study was conducted on females aged between 18-45 years who had received at least one dose of the Covid vaccine. The google form containing the questionnaire adhered to the CHERRIES guidelines for online surveys, was circulated for 10 days and had 14 questions on the menstrual cycle, Covid vaccines and their side effects. Out of the 886 responses obtained, 129 did not meet our inclusion criteria and hence, 757 responses were considered for statistical analysis. The data were subjected to descriptive, Chi-square tests. Results: The data showed a significant (p=0.027) association of age with side effects of the Covid vaccine, with the younger age group experiencing more side effects than the older age group. The side effects of the Covid vaccine did not show association (p=0.34) to menstrual cycle phases nor there was significant (p= 0.87) disturbance to the menstrual cycle post-vaccination. Conclusion: As there were no association of Covid vaccine effects to menstrual cycle, our study may allay apprehension in women who are going to take Covid vaccine and update guidance to health professionals during vaccination drive.
Demographic resilience of brook trout populations subjected to experimental size‐selective harvesting

Sustainable management of exploited populations benefits from integrating demographic and genetic considerations into assessments, as both play a role in determining harvest yields and population persistence. This is especially important in populations subject to size‐selective harvest, because size selective harvesting has the potential to result in significant demographic, life‐history, and genetic changes. We investigated harvest‐induced changes in the effective number of breeders ( N̂b ) for introduced brook trout populations (Salvelinus fontinalis) in alpine lakes from western Canada. Three populations were subject to 3 years of size‐selective harvesting, while three control populations experienced no harvest. The N̂c decreased consistently across all harvested populations (on average 60.8%) but fluctuated in control populations. There were no consistent changes in N̂b between control or harvest populations, but one harvest population experienced a decrease in N̂b of 63.2%. The N̂b / N̂c ratio increased consistently across harvest lakes; however we found no evidence of genetic compensation (where variance in reproductive success decreases at lower abundance) based on changes in family evenness ( FÊ ) and the number of full‐sibling families ( N̂fam ). We found no relationship between FÊ and N̂c or between N̂fam / N̂c and FÊ . We posit that change in N̂b was buffered by constraints on breeding habitat prior to harvest, such that the same number of breeding sites were occupied before and after harvest. These results suggest that effective size in harvested populations may be resilient to considerable changes in Nc in the short‐term, but it is still important to monitor exploited populations to assess the risk of inbreeding and ensure their long‐term survival.
Privacy enhanced key management protocol for handling remote data using deep learning and evolutionary models

AI is becoming more common because of expert automated systems and modern technology's ability to process information. This makes our lives easier and more exciting. It's better for privacy, reliability, and network resource use if AI inference algorithms run on a smart device rather than in the cloud, so this is how most smart devices use them. For a long time, on-device intelligence has been more common [1]. Cloud and big data have become important tools for pooling resources and training equipment data on the device [2]. Using the important technologies, AI spreads and humans become better at their jobs. Basic Communication technology like gesture recognition is based on data and uses ANN (Artificial Neural Network) algorithms to do periodic tracking and 3D hand modelling [3]. It has been used to run multimedia apps as well as handheld devices by gestures. Security in touch-enabled devices is also important because of authenticating users, which is provided by a learned display contact data set and classifier based on the K-means algorithm. AI-based algorithms are also used in industrial applications to combine live tracking, predictive maintenance, virtual support, and ground truth. This is done by using the cloud and AI-based algorithms.
On the X-ranks associated to a linear subspace

Let X ⊂ ℙN be an integral non-degenerate variety. For any q ∈ ℙN the X-rank rX(q) of q is the minimal cardinality of a set S ⊂ X whose linear span contains q. For any r-dimensional linear subspace V ⊂ ℙN the X-rank rX(V) of V is the minimal cardinality of a set S ⊂ X whose linear span contains V. We define several invariants related to the X-rank, e.g. the open rank of V, r+1 integers rX (qi), 0 ≤ i ≤ r, with rX(qi) ≤ rX(qj) for all i < j and q0, . , qr a basis of V and minimal ηX(V) := rX(q0) + . + rX(qr). We always have ηX(V) ≥ rX(V) and we give an upper bound for ηX(V), which ensures that we may compute rX(V) with knowledge of q0, . , qr and in particular check if ηX(V) = rX(V) (the general case, but not always). The integer rX(qr) is the minimal integer t such that V is spanned by points of X-rank ≤ t. Many of these invariants of V give lower bounds for the invariants of any linear subspace of V.
Characterization of neutrino signals with radiopulses in dense media through the Landau-Pomeranchuk-Migdal effect

We discuss the possibilities of detecting radio pulses from high energy showers in ice, such as those produced by PeV and EeV neutrino interactions. It is shown that the rich radiation pattern structure in the 100 MHz to few GHz allows the separation of electromagnetic showers induced by photons or electrons above 100 PeV from those induced by hadrons. This opens up the possibility of measuring the energy fraction transmitted to the electron in a charged current electron neutrino interaction with adequate sampling of the angular distribution of the signal. The radio technique has the potential to complement conventional high energy neutrino detectors with flavor information.
Knockdown of interleukin‐6 plays a neuroprotective role against hypoxia‐ischemia in neonatal rats via inhibition of caspase 3 and Bcl‐2‐associated X protein signaling pathway

Abstract This study aimed to investigate the role of interleukin‐6 (IL‐6) in the pathogenesis of neonatal hypoxic‐ischemic encephalopathy (NHIE). Sprague‐Dawley (SD) rats were used for the establishment of hypoxic‐ischemic (HI) model. The Zea‐Longa scoring was used to evaluate the extent of the neurological deficits. Triphenyl tetrazolium chloride (TTC) staining was used to measure the volume of infarction in the brain following HI protocol. The expression of IL‐6 in the cortex and/or hippocampus at multiple time points after HI was examined by immunohistochemistry, western blotting and immunofluorescence. Moreover, small interfering RNAs (siRNA) were used to inhibit the expression of IL‐6 in‐vitro and in‐vivo, and the concomitant expression of the Bcl‐2 associated X protein (BAX) and caspase 3 was also measured. HI induced a significant brain damage, and these pathological changes were accompanied by IL‐6 upregulation which was found localized in cortical neurons. The inhibition of IL‐6 expression fostered neuronal and axonal growth, and a reduction in cellular apoptosis in cortical neuronal cultures, and cortex and hippocampus of neonatal rats. The expression of apoptotic markers such as BAX and caspase 3 was closely associated with IL‐6. Downregulation of IL‐6 could ameliorate HI‐induced deficiencies by mediating the expression of caspase 3 and BAX.
Adaptive Fuzzy Modelling and Control for Non-Linear Systems Using Interval Reasoning and Differential Evolution

Fuzzy systems have been developed to a major scientific domain since fuzzy set theory was introduced by Zadeh about four decades ago (Zadeh, 1965). There are certain particular properties of fuzzy systems that offer them better performance for specific applications. In general, fuzzy systems are suitable for uncertain or approximate reasoning, allow decision making with estimated values under incomplete information and represent descriptive or qualitative expressionswhich are easily incorporatedwith symbolic statements (Klir & Folger, 1987). However, under the general framework of typical fuzzy systems, some kinds of uncertainty cannot be handled, particularly in practical applications (Mendel & John, 2002; Ross, 2004; Hagras, 2004). Therefore, further flexibility can be obtained by considering the uncertainty in fuzzy systems which occur from qualitative knowledge and stochastic information. As mentioned in (Mendel & John, 2002; Hagras, 2004; Liu & Li, 2005a), most of uncertainties in fuzzy systems can be embodied by the information of fuzzy membership functions. In order to expand fuzzy systems to solve more complex uncertainty, some novel methods have been proposed during recent decade. Type-2 fuzzy logic system (T2FLS) was proposed to model and control further uncertainties in typical fuzzy systems by using the secondary fuzzy membership functions (Karnik & Liang, 1999; Liang & Mendel, 2000a). The T2FLS was originally inspired by the fact that the typical FLS limits introducing uncertain factors from linguistic rules through predefined membership functions. The type-2 fuzzy methods can be roughly described that their fuzzy sets are further defined by the typical fuzzy membership functions, i.e., the membership degree of belonging for each element of these sets are fuzzy sets, not a crisp number (Liang & Mendel, 2000b; Karnik & Mendel, 2001; Wu &Mendel, 2009). In comparison with the typical FLS, a type-2 FLS has the two-fold advantages as follows. Firstly, it has the capability of directly handling the uncertain factors of fuzzy rules caused by expert experience or linguistic description. Secondly, it is efficient to employ a
Initial condition of the string relaxation equation of the string model for glass transition: part-I

The string relaxation equation (SRE) of the string model for the glass transition contains the well-known Debye and Rouse-Zimm relaxation equations. However, its initial condition, necessary to the model predictions of glassy dynamics, such as the mechanism of the universal primary alpha- and Johari-Goldstein beta-relaxations in glassformers, has not been solved. In this paper, the special initial condition (SIC) of the SRE of straight strings for dielectric spectrum technique, which is one of the most common methods to measure the glassy dynamics, was tentatively calculated by a direct calculation method, finding that the method has not any practical feasibility. However, a recursive calculation method was developed that allows to obtain the SIC exactly. It should be expected that the obtained SIC would benefit the thorough solution of the general initial condition of the SRE of the string model for stochastically spatially configurating strings, as will be described in separate publications.
Semantics representation in a sentence with concept relational model (CRM)

The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se. To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefined mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well. CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics. The overall result is better accuracy in text engineering related task like relation extraction.