Miracle-dz/UniFAR-FTUs huggingface dataset:
First Selectively Imitate Responses, Then Associate With Function

Imitation is ubiquitous: human adults imitate, human infants, preschoolers, and adolescents all imitate, some animals too imitate. Moreover, imitation may be automatic, spontaneous, or deliberate and humans may specifically select their imitation models as well as the imitated target feature or object. Finally, language, gestures, motor patterns, and high-level behaviors can all be imitated at varying degrees of detail and in a variety of modulating contexts. These are some major aspects of the study of the broad class of cognitive processes called imitation, whether and how it has evolved and its relation to empathy, mind-reading, language, culture, and social learning in general (Hurley and Chater, 2005; Heyes et al., 2009; Jones, 2009, Shea, 2009; Claidière and Sperber, 2010; Gerrans, 2013; Hodges, 2014). Because of this ubiquity, imitation is sometimes used with diversified or more restricted meanings, such as mimicry and emulation (van Baaren et al., 2009; Whiten et al., 2009), and research efforts often center on the neural and behavioral organization that allows imitation to happen (Brass and Heyes, 2005). We are focusing on the relation of imitation with association, as a basic mechanism of behavioral emergence, and with communication, as the general function of imitation (communication need not be thought as purely linguistic, though1). Our goal is to discuss the nexus of the three concepts (imitation, association, communicative function) and to propose a plausible view of the initial steps taken by evolution in the development of general social and communicative behavior. Thus, we propose an incremental process of generative and selective imitation starting from proto-imitation that replicates external signals without associating with target objects or functionality, and proceeding to proto-association that relates to higher-order imitation and attributes “meaning” or function to external signals. We are therefore discussing a model that allows proto-imitation outside explicit communication but may allow emergence of communication in the medium or long term ontogenetically. The idea is to separate response imitation from response association to external meaning so as to make room both for species that can imitate but not associate meaning and for disabled humans that cannot associate well or even proto-imitate well. Thus, we regard and model association as a generic functional concept, initially Hebbian at the neural level (“what fires together, wires together”), but more intricate at higher levels (Cooper et al., 2013). In what follows, we present in order the proto-imitation concept and model, the predictions about its function in regular configurations or in cognitively extreme cases, the basic meaning association model and, lastly, further behavioral high-level predictions at organism level. We have verified computationally some of the predictions elsewhere and all of them match actual evidence obtained experimentally. We conclude with further thoughts about the proposed model.
A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells

Shale gas production prediction and horizontal well parameter optimization are significant for shale gas development. However, conventional reservoir numerical simulation requires extensive resources in terms of labor, time, and computations, and so the optimization problem still remains a challenge. Therefore, we propose, for the first time, a new gas production prediction methodology based on Gaussian Process Regression (GPR) and Convolution Neural Network (CNN) to complement the numerical simulation model and achieve rapid optimization. Specifically, through sensitivity analysis, porosity, permeability, fracture half-length, and horizontal well length were selected as influencing factors. Second, the n-factorial experimental design was applied to design the initial experiment and the dataset was constructed by combining the simulation results with the case parameters. Subsequently, the gas production model was built by GPR, CNN, and SVM based on the dataset. Finally, the optimal model was combined with the optimization algorithm to maximize the Net Present Value (NPV) and obtain the optimal fracture half-length and horizontal well length. Experimental results demonstrated the GPR model had prominent modeling capabilities compared with CNN and Support Vector Machine (SVM) and achieved the satisfactory prediction performance. The fracture half-length and well length optimized by the GPR model and reservoir numerical simulation model converged to almost the same values. Compared with the field reference case, the optimized NPV increased by US$ 7.43 million. Additionally, the time required to optimize the GPR model was 1/720 of that of numerical simulation. This work enriches the knowledge of shale gas development technology and lays the foundation for realizing the scale-benefit development for shale gas, so as to realize the integration of geological engineering.
Environmental Analysis and the Dual Grand Challenge of COVID-19 and Sustainable Development

In 2015, the United Nations (UN) adopted the 2030 Agenda with 17 Sustainable Development Goals (SDGs) to improve people’s lives and the natural world by 2030 (United Nations, 2015). The outbreak of Coronavirus disease 2019 (COVID-19) occurred in 2020, right at the beginning of the “decade of actions” aiming to cover implementation gaps on delivering SDGs. The pandemic affected the core focus of the SDGs by triggering an economic crisis of large proportions and restricting mobility and migration (Shulla et al., 2021). COVID-19 and the ensuing implications have demonstrated the fragility of SDGs and their interconnections. As the world recovers from this pandemic, the importance of environmental health and resilience as a critical complement to public health is underscored. Rebuilding requires countries to succeed in transitioning to green economies and protection against future disruption from global stressors. From an environmental perspective, the pandemic created both problems and opportunities, each time emphasizing the importance of delivering the SDGs. They also highlighted the need for a multidisciplinary system-thinking approach to explore interconnections between the environment, wildlife, and humans. The COVID-19 pandemic and the 2030 Agenda for Sustainable Development are a dual grand challenge that can only be addressed by everyone as part of a transition to an inclusive and sustainable future. On this basis, environmental analysis is critical in understanding and mitigating current environmental changes at a global level. In a less physically-connected world and an unstable setting where new types of pollution rise fast, environmental analysis must find the pace and tackle this dual grand challenge. Admittedly, this unprecedented time represents an opportunity for environmental analysis to examine the impact of human activities on the natural world and gather information that will protect human health, biodiversity efforts, and help towards combating climate change.
The Role of the VEGF Family in Coronary Heart Disease

The vascular endothelial growth factor (VEGF) family, the regulator of blood and lymphatic vessels, is mostly investigated in the tumor and ophthalmic field. However, the functions it enjoys can also interfere with the development of atherosclerosis (AS) and further diseases like coronary heart disease (CHD). The source, regulating mechanisms including upregulation and downregulation, target cells/tissues, and known functions about VEGF-A, VEGF-B, VEGF-C, and VEGF-D are covered in the review. VEGF-A can regulate angiogenesis, vascular permeability, and inflammation by binding with VEGFR-1 and VEGFR-2. VEGF-B can regulate angiogenesis, redox, and apoptosis by binding with VEGFR-1. VEGF-C can regulate inflammation, lymphangiogenesis, angiogenesis, apoptosis, and fibrogenesis by binding with VEGFR-2 and VEGFR-3. VEGF-D can regulate lymphangiogenesis, angiogenesis, fibrogenesis, and apoptosis by binding with VEGFR-2 and VEGFR-3. These functions present great potential of applying the VEGF family for treating CHD. For instance, angiogenesis can compensate for hypoxia and ischemia by growing novel blood vessels. Lymphangiogenesis can degrade inflammation by providing exits for accumulated inflammatory cytokines. Anti-apoptosis can protect myocardium from impairment after myocardial infarction (MI). Fibrogenesis can promote myocardial fibrosis after MI to benefit cardiac recovery. In addition, all these factors have been confirmed to keep a link with lipid metabolism, the research about which is still in the early stage and exact mechanisms are relatively obscure. Because few reviews have been published about the summarized role of the VEGF family for treating CHD, the aim of this review article is to present an overview of the available evidence supporting it and give hints for further research.
N6-Methyladenosine Writer Gene ZC3H13 Predicts Immune Phenotype and Therapeutic Opportunities in Kidney Renal Clear Cell Carcinoma

Background Although the RNA modification N6-methyladenosine ZC3H13 has been found to play vital regulatory roles in many types of cancers, its role in predicting the tumor immune microenvironment (TME) and response to immune checkpoint blockade (ICB) in kidney renal clear cell carcinoma (KIRC) remains unclear. Methods We comprehensively analyzed the expression, prognostic significance and immunological role of ZC3H13 in pan-cancers and systematically correlated ZC3H13 with TME cell-infiltration, ICB response and targeted therapy in KIRC. The data were collected from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Genotype-Tissue Expression (GTEx), Broad Institute Cancer Cell Line Encyclopedia (CCLE) and DrugBank database. Also, we performed RNA sequencing (RNA-seq) of 46 renal cell carcinoma tissues and 11 adjacent normal tissues to validate our result. All analyses were implemented using R software, version 3.6. 3. Results ZC3H13 was significantly differentially expressed in most tumors. However, its expression profiles and prognostic significance were consistent only in KIRC, regardless of overall survival, progression-free survival and cancer-specific survival. Additionally, ZC3H13 expression was correlated with clinicopathological factors in KIRC. Furthermore, we found that ZC3H13 might shape a noninflamed phenotype and could predict a lower response to ICB in KIRC. These results could be validated in our own RNA-seq data. Tumor mutation burden (TMB) was significantly higher in the low ZC3H13 group. Finally, we found that ZC3H13 could predict the sensitivity of targeted therapy for KIRC. Conclusions ZC3H13 might shape a noninflamed phenotype in KIRC. Moreover, ZC3H13 could predict the prognosis and clinical response of ICB and the sensitivity to targeted therapies in KIRC.
Hydrochemical and Isotopic Difference of Spring Water Depending on Flow Type in a Stratigraphically Complex Karst Area of South Korea

Characterizing the subsurface flow in karstic areas is challenging due to distinct flow paths coexisting, and lithologic heterogeneity makes it more difficult. A combined use of hydrochemical, environmental isotopic, and hydrograph separation study was performed to understand the subsurface flow in a karst terrain where Ordovician carbonate rocks overlie Jurassic sandstone and shale along thrusts. Spring water collected was divided into Type Ⅰ (n = 11) and Ⅱ (n = 30) based on flow patterns (i.e., low and high discharge, respectively). In addition, groundwater (n = 20) was examined for comparison. Three Type Ⅱ springs were additionally collected during a storm event to construct hydrographs using δ18O and δD. As a result, Type Ⅱ had higher electrical conductivity, Mg2+, HCO3 −, and Ca2+/(Na+ + K+) than Type Ⅰ and was mostly saturated with calcite, similar to deep groundwater. The hydrochemical difference between Types Ⅰ and Ⅱ was opposite to the expectation that Type Ⅱ would be undersaturated given fast flow and small storage, which could be explained by the distinct geology and water sources. Most Type Ⅱ springs and deep groundwater occurred in carbonate rocks, whereas Type Ⅰ and shallow groundwater occurred in various geological settings. The carbonate rocks seemed to provide conduit flow paths for Type Ⅱ given high solubility and faults, resulting in 1) relatively high tritium and NO3 − and Cl− via short-circuiting flow paths and 2) the similar hydrochemistry and δ18O and δD to deep groundwater via upwelling from deep flow paths. The deep groundwater contributed to 83–87% of the discharge at three Type Ⅱ springs in the dry season. In contrast, Type Ⅰ showed low Ca2+ + Mg2+ and Ca2+/(Na+ + K+) discharging diffuse sources passing through shallow depths in a matrix in mountain areas. Delayed responses to rainfall and the increased concentrations of contaminants (e.g., NO3 −) during a typhoon at Type Ⅱ implied storage in the vadose zone. This study shows that hydrochemical and isotopic investigations are effective to characterize flow paths, when combined with hydrograph separation because the heterogenous geology affects both flow paths and the hydrochemistry of spring water passing through each pathway.
Improving Cold-start Item Advertisement For Small Businesses

In this paper, we study cold-start item advertisements for small businesses on a real-world E-commerce website. From analysis, we found that the existing cold-start Recommender Systems (RSs) are not helpful for small businesses with a few sales history. Training samples in RS models can be extremely biased towards popular items or shops with sufficient sales history, and can decrease advertising performance for small shops with few or zero sales history. We propose two solutions to improve advertising performance for small shops: negative sampling and Meta-shop . Negative sampling focuses on changing the data distribution and Meta-shop focuses on building novel meta-learning models. By including sales information in the training of both methods, we are able to learn better cold-start item representations from small shops while keeping the same or better overall recommendation performance. We conducted experiments on a real-world E-commerce dataset and demonstrated that the proposed methods outperformed a production baseline. Specifically, we achieved up to 19.6% relative improvement of Re-call@10k using Meta-shop compared to the traditional cold-start RS model.
How Purely Wind-driven Long PG Waves may be Energized in the Western Part of Ocean Subtropical Gyres

Satellite observations and idealized numerical studies reveal intensification of long period (order one cycle per year) waves in the western part of ocean basins. We here explore the idea that the intensification is associated with the spatial growth of purely time periodic but baroclinically unstable motions. The framework is a simple idealized 2.5 layer model where only the upper layer is directly forced by the wind, a setting similar to the shadow zone of the LPS model. The upper two layers participate in the wave motion, which is driven by a large scale wind stress fluctuating with the annual period, representing the seasonal cycle. Although possibly unstable solutions exist everywhere in the subtropical gyre on account of the non-zero meridional background flow, they are not seen in the eastern part of the basin in satellite observations nor excited there by model gyre scale annual period winds. Instead, energy injected into the model ocean at a fixed frequency and with zonal and meridional wavenumbers such that the resulting flow perturbation is locally stable, refracts westward as it propagates through the spatially varying background flow without change of frequency and reaches distant regions where the spatial wavenumber becomes complex so that spatial growth occurs. This process results in spatially growing solutions of annual or near-annual frequency only in the southwestern part of the model subtropical gyre, thus explaining why the intensification is preferentially manifested in the southwestern subtropical gyre in published numerical model results. The paper concludes with a discussion of relevant satellite and in situ observations.
A review of hyperspectral imaging for nanoscale materials research

ABSTRACT As an emerging technology, hyperspectral imaging (HSI), which combines both advanced spectroscopy and imaging techniques, provides sufficient information for spectral and spatial analysis and is thus suitable for distribution and property investigation of nanoscale materials. Considering the applications of HSI have spread from remote sensing to quality control of macro products such as food and milk, this article reviews recent research of HSI in a new field of nanoscale materials. On the basis of fundamental parts of a HSI system, new techniques fitting specifically for nanoscale materials imaging such as dark field and Raman spectroscopy are introduced. Nanoscale materials, including metal nanoparticles, carbon nanotubes and graphene, biological components in cells and tissues, as well as multi-layer nanoscale materials, are the research hotspots utilizing HSI technology. Related research reports of the above materials are reviewed based on the physical distinction of these nanoscale materials. It is believed that HSI technology is a strongly potential technique for property investigation and manipulation of nanomaterial for various applications.
FEW-SHOT BIOACOUSTIC EVENT DETECTION: A GOOD TRANSDUCTIVE INFERENCE IS ALL YOU NEED Technical Report

In this technical report, we describe our few-shot bioacoustic event detection methods submitted to Detection and Classiﬁcation of Acoustic Scenes and Events Challenge 2021 Task 5. We analyze the reason why Prototypical networks cannot perform well, and propose to use transductive inference for few shot learning. Our method maximizes the mutual information between the query features and their label predictions for a given few-shot task, in conjunction with a supervision loss based on the support set. Furthermore, we propose a mutual learning framework, which makes feature extractor and classiﬁer to help each other. Experimental re-sults indicate our transductive inference method get better performance than baseline, and F1 score is about 50.8 % on evaluation set. Furthermore, our mutual learning framework brings about 5 % improvement over the transductive inference method. We will release our code on https://github.com/yangdongchao/ DCASE2021Task5
A Natural Language Model for Generating PDDL

Language generation in various domains has drawn a large amount of interest in recent years. This paper studies language generation in the context of generating planning spec-iﬁcations in the syntax typically used for this task: the Planning Domain Deﬁnition Language (PDDL). The goal of this preliminary work is to predict the next completion in PDDL code, based on previous and surrounding text. Generating valid PDDL code is a key component in creating robust planners. Thus, the ability to generate PDDL code will be extremely useful to PDDL practitioners for the purpose of solving planning problems. It further opens the door to providing a source of inspiration for the modeller. The main contribution of our approach is a language model built using Recurrent Neural Networks (RNNs) that is trained on existing PDDL domains, which can be used to generate PDDL-like code. We train our model on a corpus of publicly available PDDL ﬁles from api.planning. domains, and evaluate our approach in the setting of PDDL auto-prediction for some of the more common domains. We found that code-like generation is possible, although ﬂuency can be improved.
Inflammasome can Affect Adult Neurogenesis: A Review Article

Adult neurogenesis is the process of producing new neurons in the adult brain and is limited to two major areas: the hippocampal dentate gyrus and the Subventricular Zone (SVZ). Adult neurogenesis is affected by some physiological, pharmacological, and pathological factors. The inflammasome is a major signalling platform that regulates caspase-1 and induces proinflammatory cytokines production such as interleukin-1β (IL1-β) and IL-18. Inflammasomes may be stimulated through multiple signals, and some of these signaling factors can affect neurogenesis. In the current review, “adult neurogenesis and inflammasome” were searched in PubMed, Scopus, and Google Scholar. Reviewing various research works showed correlations between inflammasome and neurogenesis by different intermediate factors, such as interferons (IFN), interleukins (IL), α-synuclein, microRNAs, and natural compounds. Concerning the significant role of neurogenesis in the health of the nervous system and memory, understanding factors inducing neurogenesis is crucial for identifying new therapeutic aims. Hence in this review, we will discuss the different mechanisms by which inflammasome influences adult neurogenesis.
ACTIVITY THEORY IN STUDYING INNOVATIONS: THE INTERCONNECTED PHASES OF ACTIVITY SYSTEM

“Activity Theory or Cultural-Historical Activity Theory is a cross-disciplinary framework for studying how humans purposefully transform natural and social reality, including themselves, as an ongoing culturally and historically situated, materially and socially mediated process” (Roth, Radford and Lacroix 2012, p. 1). Entrenched in the dialectal psychology, it transcends traditional dichotomies of macro and micro, thought and action, intervention and observation, qualitative and quantitative by integrating three perspectives: the objective, the ecological and the sociocultural (Engestrom 1999). AT was founded by Soviet psychologists Vygotsky, Lurija, Rubinstein and Leontev in the 1930s. Their idea was that activity was a fundamental philosophical and psychological concept because it was the essential notion in any viable philosophical anthropology. Hence, the statement that humans were active creatures was not to be simply registered as an empirical observation. It was never denied by any philosopher that humans act. Yet, it was a statement about the very nature of thought and its behaviour on the world. The Soviet scholar who developed this idea was Vygotsky who initially illustrated it in a form of a basic triangle, which consisted of a subject, an object and an artefact. However, he laid bare what he argued as then a problem in psychological investigation that limited experimental research to reductionist laboratory studies separated from the contexts of human lives (Barab, Evans and Baek 2004). Vygotsky’s theoretical endeavour, later, was linked and elaborated by the Finnish scholar Engestrom, who added societal and contextual dimensions to Vygotsky’s model and “[broadened] the process by linking the idea of activity systems to concept of context, stating that contexts are activity systems” (Engestrom 1993 in Esch and John 2004, p. 56). The main concept of this approach is that the individual actions occur in relation to three factors: the available tools, the community and the labour
Structural Equation Modeling (SEM) and measures the success of e-billing implementation at the Palembang

This study discusses the application of Structural Equation Modeling (SEM) and measures the success of e-billing implementation at the Palembang Ilir Timur Pratama Tax Office and the factors that influence its success. E-billing is developed and managed by the Directorate General of Treasury. There are 100 thousand e-billing users registered at the Palembang Ilir Timur Pratama Tax Office. Based on the analysis results obtained standardized residual covariances matrix values no smaller than -2.58 or greater than 2.58 and the probability of showing a value of 0.080 which means that the value meets the interpretation requirements that the model can be accepted. The results of this study indicate that the success rate of e-billing implementation is at the level of 78% ("Success"). This needs to be considered by organizations for the development of e-billing in the future.