Miracle-dz/UniFAR-FTUs huggingface dataset:
Observed CN and HCN intensity ratios exhibit subtle variations in extreme galaxy environments

We use both new and archival ALMA data of three energy lines each of CN and HCN to explore intensity ratios in dense gas in NGC 3256, NGC 7469, and IRAS 13120-5453. The HCN (3-2)/HCN (1-0) intensity ratio varies in NGC 3256 and NGC 7469, with superlinear trends of 1.53 ± 0.07 and 1.55 ± 0.05, respectively. We find an offset to higher HCN (3-2)/HCN (1-0) intensity ratios (∼ 0.8) in IRAS 13120-5453 compared to NGC 3256 (∼ 0.3 − 0.4) and NGC 7469 (∼ 0.3 − 0.5). The HCN (4-3)/HCN (3-2) intensity ratio in NGC 7469 has a slope of 1.34±0.05. We attribute the variation within NGC 3256 to excitation associated with the northern and southern nuclei. In NGC 7469, the variations are localized to the region surrounding the active galactic nucleus. At our resolution (∼ 700 pc), IRAS 13120-5453 shows little variation in the HCN intensity ratios. Individual galaxies show nearly constant CN (2-1)/CN (1-0) intensity ratios. We find an offset to lower CN (2-1)/CN (1-0) intensity ratios (∼ 0.5) in NGC 3256 compared to the other two galaxies (∼ 0.8). For the CN (3-2)/CN (2-1) intensity ratio, NGC 7469 has a superlinear trend of 1.55 ± 0.04, with the peak localized toward the active galactic nucleus. We find high (∼ 1.7) CN (1-0)/HCN (1-0) intensity ratios in IRAS 13120-5453 and in the northern nucleus of NGC 3256, compared to a more constant ratio (∼ 1.1) in NGC 7469 and non-starbursting regions of NGC 3256.
Faithfully Explainable Recommendation via Neural Logic Reasoning

Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of the derived explanations to justify the decision-making process. To the best of our knowledge, this is the first work that models and evaluates faithfully explainable recommendation under the framework of KG reasoning. Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the path-reasoning process for explanation generation. We experiment on three large-scale datasets in the e-commerce domain, demonstrating the effectiveness of our method in delivering high-quality recommendations as well as ascertaining the faithfulness of the derived explanation.
Efficient QAM Signal Detector for Massive MIMO Systems via PS-ADMM Approach

In this paper, we design an efficient quadrature amplitude modulation (QAM) signal detector for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM). Its main content is as follows: first, we formulate QAM-MIMO detection as a maximum-likelihood optimization problem with bound relaxation constraints. Decomposing QAM signals into a sum of multiple binary variables and exploiting introduced binary variables as penalty functions, we transform the detection optimization model to a non-convex sharing problem; second, a customized ADMM algorithm is presented to solve the formulated non-convex optimization problem. In the implementation, all variables can be solved analytically and in parallel; third, it is proved that the proposed PS-ADMM algorithm converges under mild conditions. Simulation results demonstrate the effectiveness of the proposed approach.
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale

General-purpose robotic systems must master a large repertoire of diverse skills to be useful in a range of daily tasks. While reinforcement learning provides a powerful framework for acquiring individual behaviors, the time needed to acquire each skill makes the prospect of a generalist robot trained with RL daunting. In this paper, we study how a large-scale collective robotic learning system can acquire a repertoire of behaviors simultaneously, sharing exploration, experience, and representations across tasks. In this framework new tasks can be continuously instantiated from previously learned tasks improving overall performance and capabilities of the system. To instantiate this system, we develop a scalable and intuitive framework for specifying new tasks through user-provided examples of desired outcomes, devise a multi-robot collective learning system for data collection that simultaneously collects experience for multiple tasks, and develop a scalable and generalizable multi-task deep reinforcement learning method, which we call MT-Opt. We demonstrate how MT-Opt can learn a wide range of skills, including semantic picking (i.e., picking an object from a particular category), placing into various fixtures (e.g., placing a food item onto a plate), covering, aligning, and rearranging. We train and evaluate our system on a set of 12 real-world tasks with data collected from 7 robots, and demonstrate the performance of our system both in terms of its ability to generalize to structurally similar new tasks, and acquire distinct new tasks more quickly by leveraging past experience. We recommend viewing the videos at https://karolhausman.github. io/mt-opt/
Modeling, Corpus Construction, and Analysis of User Internal State using Movie Recommendation Dialogue System

: An intelligent dialogue system is expected as a new human-machine interface. In order to construct a system that cooperatively interacts with human users, it is essential to model the user’s internal states appropriately. In this study, we model and analyze the internal state of the human user based on dialogue data. We collected a large scale multi-turn human-machine dialogue corpus by using a carefully-designed movie recommendation dialogue system. The collected dialogues were annotated with a user’s internal state as well as the naturalness score of the system utterance. Our experiment shows that we can predict the user’s internal state from the dialogue. Also, we show that the user’s internal state is useful to detect the unnaturalness of the system utterance.
Relationship of Islamic Lifestyle with Depression and Anxiety among the Students of Qom University of Medical Sciences in 2018

Introduction ifestyle reflects peoples' specific or distinct way of life as they come under the influence of values, institutions, and laws (1). In other words, lifestyle encompasses all the routine activities of daily life that a person is used to doing. Self-relationship (observance of the principles of nutrition and mental states), as well as the relationship with God (devotion) and society (positive interactions with others and the environment), are properly defined in a healthy lifestyle (2). Today, one of the priorities of every society is the devotion of special attention to physical, mental, social, and cultural health, as well as the provision of appropriate context for the realization of a dynamic and healthy life that guarantees people's health. In this regard, the prevention of emotional disorders, anxiety, and depression is indispensable for attaining this worthwhile goal. Apart from personal negative L Abstract
Remote night-time lights sensing: Investigation and econometric application

Orientation: Some recent studies have been published that demonstrated the value of remote sensing night-time lights as descriptors and/or proxies for human activity. Research purpose: This article investigated the association between night-time light emissions and gross domestic product (GDP) estimates for South Africa. Motivation for the study: Satellite night-lights data seemed to be a useful proxy for economic activity at temporal and geographic scales for which traditional data are of poor quality, are unavailable or only available with a large time lag. Research approach/design and method: The article primarily used the remote sensing of night-time light emissions using satellite technologies. The methodology employed in this study involved estimating both a vector error correction modelling (VECM) and autoregressive distributed lag (ARDL) models that map light growth into a proxy for GDP growth. Main findings: Both the VECM and ARDL models confirmed a long-term co-integrating relationship between GDP (per capita) and night-time lights (total light intensity), a statistically significant short-term error correction term could, however, not be established through the VECM, but indeed through the ARDL model. Practical/managerial implications: The results of the study suggested that satellite remote sensing technologies held much promise and opportunities in terms of the field of Economics and Development. Contribution/value-add: This study contributes to our understanding of the spatial and temporal behaviour and trends in economic activity. It also suggested the use of satellite remote sensing technologies as part of official statistical frameworks and methodologies.
Reindeer population size and trend on EdgeGya Svalbard

Two censuses made of the population of Svalbard reindeer on Edgedya in 1983 and 1996 resulted in 1586 and 2431 individuals respectively. Together with data from earlier population surveys (1969-771, this indicates, numerically, a population growth during the period 1969-96. Because of the weaknesses in the different census methods used. we cannot conclude that in reality a population growth has taken place even though the iiumerical data is correct. Therefore an average population size of 1730 ( i451 SD) individuals for the period 1969-96 is suggested. Earlier estimates of vegetated area indicate that the present data corresponds to reindeer densities averaging 1.05 and ranging from 0.79 to 1.47 individuals/km2.
Density inversion of selected microgravity anomalies using L2-smoothing and minimum support focusing stabilizers

Interpretation and inversion of microgravity anomalies belong to important tasks of near-surface geophysics, mostly in cavities detection in engineering, environmental and archaeological applications. One of the mostly used concepts of inversion in applied gravimetry is based on the approximation of the model space by means of 2D or 3D elementary sources with the aim to estimate their densities by means of the solution of a corresponding linear equation system. There were published several approaches trying to obtain correct and realistic results, which describe real parameters of the sources. In the proposed contribution we analyse the properties of two additional functionals, which describe additional properties of the searched solution – namely so-called L2-smoothing and minimum support focusing stabilizers. For the inversion itself, we have used the regularized conjugate gradient method. We have studied properties of these two stabilizers in the case of one synthetic model and one real-world dataset (microgravity data from St. Nicholas church in Trnava). Results have shown that proposed algorithm with the minimum support stabilizer can generate satisfactory model results, from which we can describe real geometry, dimensions and physical properties of interpreted cavities.
Atmospheric CO2 promoted synthesis of N-containing heterocycles over B(C6F5)3 catalyst

B(C6F5)3 combined with atmospheric CO2 was found to be highly effective for the cyclization of ortho-substituted aniline derivatives with N,N-dimethylformamide (DMF), and a series of N-containing heterocycles including benzothiazoles, benzimidazoles, quinazolinone and benzoxazole were obtained in good to excellent yields.
Allopurinol for fibromyalgia pain in adults: A randomized controlled trial

Allopurinol is a potent inhibitor of the enzyme xanthine oxidase used in the treatment of hyperuricemia and gout. Because it is well known that purines exert multiple affects on pain transmission, we hypothesized that the inhibition of xanthine oxidase by allopurinol could be a valid strategy to treat pain in humans. This study aimed to compare the analgesic efficacy of oral allopurinol versus placebo as an adjuvant therapy in patients displaying fibromyalgia.
Repressors of anthocyanin biosynthesis.

Anthocyanins play a variety of adaptive roles in both vegetative tissues and reproductive organs of plants. The broad functionality of these compounds requires sophisticated regulation of the anthocyanin biosynthesis pathway to allow proper localization, timing, and optimal intensity of pigment deposition. While it is well-established that the committed steps of anthocyanin biosynthesis are activated by a highly conserved MYB-bHLH-WDR (MBW) protein complex in virtually all flowering plants, anthocyanin repression seems to be achieved by a wide variety of protein and small RNA families that function in different tissue types and in response to different developmental, environmental, and hormonal cues. In this review, we survey recent progress in the identification of anthocyanin repressors and the characterization of their molecular mechanisms. We find that these seemingly very different repression modules act through a remarkably similar logic, the so-called "double negative logic". Much of the double-negative regulation of anthocyanin production involves signal-induced degradation or sequestration of the repressors from the MBW protein complex. We discuss the functional and evolutionary advantages of this logic design compared with simple or sequential positive regulation. These advantages provide a plausible explanation to why plants have evolved so many anthocyanin repressors.
Self‐organization of apical membrane protein sorting in epithelial cells

Polarized epithelial cells are characterized by the asymmetric distribution of proteins between apical and basolateral domains of the plasma membrane. This asymmetry is highly conserved and is fundamental to epithelial cell physiology, development, and homeostasis. How proteins are segregated for apical or basolateral delivery, a process known as sorting, has been the subject of considerable investigation for decades. Despite these efforts, the rules guiding apical sorting are poorly understood and remain controversial. Here, we consider mechanisms of apical membrane protein sorting and argue that they are largely driven by self‐organization and biophysical principles. The preponderance of data to date is consistent with the idea that apical sorting is not ruled by a dedicated protein‐based sorting machinery and relies instead on the concerted effects of oligomerization, phase separation of lipids and proteins in membranes, and pH‐dependent glycan interactions.
Evaluation of ten sugar beet varieties in terms of growth , yield and quality under different soil salinity levels

کچ ی هد مویافلا ناتسا رد هژیو هب ،دنقردنغچ دشر هلمج زا ،هایگ دیلوت رب کاخ یروش روشک یم ریثأت رصم ؜ دراذگ . زگ شنی ماقرا لوادتم زا یکی یروش هب لمحتم ؜ شور نیرت ؜ هب طسوت هک تسا ییاه ؜ یم هدافتسا دنقردنغچ نارگداژن ؜ دوش . یا قحت ن رد قی مویافلا ناتسا رد عقاو )رصم( 29 هجرد و 17 یلامش هجرد ؛ 30 هجرد و 53 یقرش هجرد لوط رد ، لصف یعارز 2017 / 2018 و 2018 / 2019 دش ماجنا . فده قیقحت نیا زا درکلمع و تیفیک ،دشر یور رب کاخ یروش رثا هعلاطم 10 یتلوم مقر ؜ سیرنیس تاشنم هقطنم هس رد دنقردنغچ مرژ ( 1 S ) ، 57 / 3 1 dSm ینب تاشنم ، ؜ نامثع ( 2 S ) ، 6 / 8 1 dSm تاشنم و یواتنت ( 3 S ( ، 84 / 11 1 dSm ؜ وب . د رارکت هس اب یفداصت لماک کولب حرط تروص هب شیامزآ دش ماجنا . ناشن جیاتن د هک دا درکلمع و هشیر درکلمع رکش هب دهاش رامیت اب هسیاقم رد کاخ یروش حطس شیازفا اب هشیر هزادنا نینچمه و ؜ وط ر ینعم ؜ تفای شهاک یراد . ،کاخ دیدش یروش تحت و هشیر درکلمع رکش مقر Florima هب ؜ بیترت ربارب 64 / 32 و 33 / 4 ت ن مقر ،رگید فرط زا ؛دوب راتکه رد Euklid بیترت هب ار ریداقم نیرتمک ربارب 10 / 27 و 31 / 3 رد نت راتکه تشاد . دروم رد یب ،دنقردنغچ ماقرا و کاخ یروش حطس لباقتم رثا و هشیر درکلمع رادقم نیرتش رکش ماقرا طسوت Florima ، Toro ،
Effective Instructional Leadership can Enhance Teachers’ Motivation and Improve Students’ Learning Outcomes

School principals as instructional leaders set clear goals, manage the instruction and curriculum, observe lesson plans, dispense resources and assess teachers consistently. They focus to improve the quality of teaching and aim to nurture student learning. The present study was conducted to investigate the impact of instructional leadership on teachers’ motivation level and students learning outcomes at the secondary school. The research will create awareness among the instructional leaders that how important their actions are in making a conducive school. The research will create awareness among the instructional leaders that how important their actions are in making the school culture more effective and conducive. The sample size consisted of 400 teachers from public and private secondary schools. A questionnaire was used as a tool to collect data. The research was quantitative. A survey design was used to see the impact of instructional leadership on the teachers’ motivation level and student achievement at the Secondary School Level. The questionnaire contained 35 items which were arranged into three factors, such as Instructional leadership, teacher motivation, and student learning. It was based on a five-point Likert scale. The findings of the research showed that both the male and female teachers of the public and private schools agreed that the instructional leaders encouraged teamwork developed positive relationships between parents and school staff. They agreed that the school principal tried to change the school climate according to the new trends.
Adaptasi dan Kesejahteraan Pekerja di Era COVID-19:

Abstract Alert and always adaptive is the key for an organization or company to survive in the current pandemi COVID-19. This pandemic becomes an unexpected external crisis and is beyond anyone's prediction, making high uncertainty not only for the sustainability of the organization also becomes uncertainty for the workforce/individuals within it. Organizations are required to have directions and ways to deal with pandemics that have never happened before, so that many new solutions are found in dealing with impacts that arise when the organization's operations run in the middle of a pandemic. This article focuses on discussing some of the challenges and implications of the impact caused by COVID-19 in human resource (HR) science. The workforce must be assisted by the organization in adjusting and adapting to the new work environment. This research also directs some suggestions for future research so as to create sustainability and integrated research to overcome the challenges that are further investigated. Keywords: Adaptation, Pandemic COVID-19, workers, organizations
Synergistic Anti-oxidant Property of Seed Extracts of Trigonella foenum-graecum, Nigella sativa and Linum usitatissimum

International Journal of Pharmaceutical Sciences Review and Research International Journal of Pharmaceutical Sciences Review and Research Available online at www.globalresearchonline. net ©Copyright protected. Unauthorised republication, reproduction, distribution, dissemination and copying of this document in whole or in part is strictly prohibited. Available online at www.globalresearchonline. net 128 Kavitha G Singh*, Thilagavathy A, Meena V, Jahnavi R, Hiba Basheer, Vishnupriya M, Kriti Bhadani, Umme Umaima Mount Carmel College, Autonomous, Bengaluru, Karnataka, India. *Corresponding author’s E-mail: kavi182@yahoo.co.in
The Dilemma of Social Media: Perceptions and Ethics of AI

Social media networks are extensively using artificial intelligence tools (AI) fed with huge data about users’ preferences, mental state, mood, health, and other. The potential risk of manipulating this “Big Data” to predict users‘ behavior, customize users’ profiles and create a “Virtual social world” for each one is of major importance. Using algorithms to make sense of streams of data, known as the discipline of data analytics, and how it’s applied in social media platforms and decision-making rises ethical concerns about data privacy and data protection. The debate about the negative effects of social media on users’ psychology, how it affects a person’s image of himself and the world will be tackled from the perspective of users‘ perception of the risks of using social media. This research is conducted to answer questions related to users’ perceptions of privacy, risk of sharing personal data on social media and to what extent are people aware of the use of AI and the risks they face while using SNSs, as well as the ethical issues related to the use of artificial intelligence in social media.
The methodological quality of individual participant data meta-analysis on intervention effects: systematic review

Abstract Objective To assess the methodological quality of individual participant data (IPD) meta-analysis and to identify areas for improvement. Design Systematic review. Data sources Medline, Embase, and Cochrane Database of Systematic Reviews. Eligibility criteria for selecting studies Systematic reviews with IPD meta-analyses of randomised controlled trials on intervention effects published in English. Results 323 IPD meta-analyses covering 21 clinical areas and published between 1991 and 2019 were included: 270 (84%) were non-Cochrane reviews and 269 (84%) were published in journals with a high impact factor (top quarter). The IPD meta-analyses showed low compliance in using a satisfactory technique to assess the risk of bias of the included randomised controlled trials (43%, 95% confidence interval 38% to 48%), accounting for risk of bias when interpreting results (40%, 34% to 45%), providing a list of excluded studies with justifications (32%, 27% to 37%), establishing an a priori protocol (31%, 26% to 36%), prespecifying methods for assessing both the overall effects (44%, 39% to 50%) and the participant-intervention interactions (31%, 26% to 36%), assessing and considering the potential of publication bias (31%, 26% to 36%), and conducting a comprehensive literature search (19%, 15% to 23%). Up to 126 (39%) IPD meta-analyses failed to obtain IPD from 90% or more of eligible participants or trials, among which only 60 (48%) provided reasons and 21 (17%) undertook certain strategies to account for the unavailable IPD. Conclusions The methodological quality of IPD meta-analyses is unsatisfactory. Future IPD meta-analyses need to establish an a priori protocol with prespecified data syntheses plan, comprehensively search the literature, critically appraise included randomised controlled trials with appropriate technique, account for risk of bias during data analyses and interpretation, and account for unavailable IPD.
Digital Finance, Green Finance and Social Finance: Is there a Link?

Abstract Identifying the intersection between digital finance, green finance and social finance is important for promoting sustainable financial, social and environmental development. This paper suggests a link between digital finance, green finance and social finance. Using a simple conceptual model, I show that digital finance offers a smooth, efficient and seamless channel for individuals and corporations to fund social projects that deliver a social dividend, and green projects lead to a sustainable environment. The implication is that digital finance is both an enabler and a channel for efficient green financing and social financing.