Miracle-dz/UniFAR-FTUs huggingface dataset:
Five Decades of Chromospheric Activity in 59 Sun-like Stars and New Maunder Minimum Candidate HD 166620

We present five decades of chromospheric activity measurements in 59 Sun-like stars as time series. These include and extend the 35 yr of stellar chromospheric activity observations by the Mount Wilson Survey (1966–2001), and continued observations at Keck by the California Planet Search (1996–). The Mount Wilson Survey was studied closely in 1995, and revealed periodic activity cycles similar to the Sun’s 11 yr cycle. The California Planet Search provides more than five decades of measurements, significantly improving our understanding of these stars’ activity behavior. We have curated the activity measurements in order to create contiguous time series, and have classified the stellar sample according to a predetermined system. We have analyzed 29 stars with periodic cycles using the Lomb–Scargle periodogram, and present best-fit sinusoids to their activity time series. We report the best-fit periods for each cycling star, along with stellar parameters (T eff, log(g), vsin(i), etc.) for the entire sample. As a first application of these data, we offer a possible Maunder minimum candidate, HD 166620.
Navigation and positioning technology in underground coal mines and tunnels: A review

To reduce the frequency of mining incidents and improve safety and production efficiency, it is important to develop technology for the autonomous positioning and navigation of mining equipment, without the assistance of satellite navigation signals. In this study we review the current technological environment for positioning and navigation underground and present several integrated navigation strategies. Existing underground positioning and navigation technology is described, and the performances of different methods based on this technology analysed and compared. The combination types and positioning effects of underground positioning and navigation strategies are discussed. Recent research directions for filtering methods and positioning estimation algorithms for underground positioning and navigation are reviewed. Finally, we summarize the future direction of underground positioning and navigation technology and related algorithms.
A REVIEW ON FOOD QUALITY DETECTION

This paper proposes an automatic food quality evaluation system for sorting and grading of fresh food and old food detection. The food can affect in any form of spoil that may happen due to storage or chemical changes within the food Here we are using this technique to detect freshness of fruit,milk,meat etc. As a solution we propose the system to ensure freshness in food. The main aim of this system is to replace the manual inspection system. This technique reducing the time, and increasing the efficiency and accuracy The device consists of Raspberry pi and moisture sensor. Image processing is used to detect the freshness of fruits. An android application is developed to select the type of food to be checked. Based on the sensor outputs quality of the food can be detected.
“Aspect Based Sentiment Analysis Of Medical Data,Lemmas,LSTM”

: The study was conducted to examine the determinants of online consumer reviews as electronic word of mouth has become more and more popular to help make buying decisions. The web provides a wide range of customer reviews, but all reviews can be difficult to read in order to properly evaluate a product or service. A text processing framework that summarizes reviews, therefore, would be desirable. The subtask presented by such a framework is to find a range of commonalities in the review sentences, for which this paper presents two methods. Unlike most existing methods, the first method introduced is an unsafe method that applies association rules mining on co-frequency data obtained from the corpus to find a range of these aspects.
The Novel Anti-cancer Agent JNJ-26854165 Induces Cell Death Through Inhibition of Cholesterol Transport and Degradation of ABCA1.

JNJ-26854165 (serdemetan) has previously been reported to inhibit the function of the E3 ligase human double minute-2, and we initially sought to characterize its activity in models of mantle cell lymphoma (MCL) and multiple myeloma (MM). Serdemetan induced a dose dependent inhibition of proliferation in both wild-type ( wt ) and mutant ( mut ) p53 cell lines, with IC 50 ’s from 0.25 μ M/L to 3 μ M/L, in association with an S phase cell cycle arrest. Caspase-3 activation was primarily seen in wtp53 bearing cells, but also occurred in mut p53 bearing cells, albeit to a lesser extent. 293T cells treated with JNJ-26854165, and serdemetan-resistant fibroblasts displayed accumulation of cholesterol within endosomes, a phenotype reminiscent of that seen in the ATP-binding cassette sub-family A member-1 (ABCA1) cholesterol transport disorder, Tangiers disease.
Forking, Imaginaries and other features of ACFG

We study the generic theory of algebraically closed ﬁelds of ﬁxed positive characteristic with a predicate for an additive subgroup, called ACFG . This theory was introduced in [18] as a new example of NSOP 1 non simple theory. In this paper we describe more features of ACFG , such as imaginaries. We also study various independence relations in ACFG , such as Kim-independence or forking independence, and describe interactions between them. , , , Closure , , Base , Transitivity , Full Existence ,
Explaining Point Processes by Learning Interpretable Temporal Logic Rules

We propose a principled method to learn a set of human-readable logic rules to explain temporal point processes. We assume that the generative mechanisms underlying the temporal point processes are governed by a set of first-order temporal logic rules, as a compact representation of domain knowledge. Our method formulates the rule discovery process from noisy event data as a maximum likelihood problem, and designs an efficient and tractable branch-and-price algorithm to progressively search for new rules and expand existing rules. The proposed algorithm alternates between the rule generation stage and the rule evaluation stage, and uncovers the most important collection of logic rules within a fixed time limit for both synthetic and real event data. In a real healthcare application, we also had human experts (i.e., doctors) verify the learned temporal logic rules and provide further improvements. These expert-revised interpretable rules lead to a point process model which outperforms previous state-of-the-arts for symptom prediction, both in their occurrence times and types. 1
Signal Domain Learning Approach for Optoacoustic Image Reconstruction from Limited View Data

Multi-spectral optoacoustic tomography (MSOT) relies on optical excitation of tissues with subsequent detection of the generated ultrasound waves. Optimal image quality in MSOT is achieved by detection of signals from a broad tomographic view. However, due to physical constraints and other cost-related considerations, most imaging systems are implemented with probes having limited tomographic coverage around the imaged object, such as linear array transducers often employed for clinical ultrasound (US) imaging. MSOT image reconstruction from limited-view data results in arc-shaped image artifacts and disrupted shape of the vascular structures. Deep learning methods have previously been used to recover MSOT images from incomplete tomographic data, albeit poor performance was at-tained when training with data from simulations or other imaging modalities. We propose a two-step method consisting of i) style transfer for domain adaptation between simulated and experimental MSOT signals
Aberrant neuron morphology in a nonhuman primate model of maternal immune activation

Maternal infection during pregnancy increases the risk for neurodevelopmental disorders in offspring. Rodent models have played a critical role in establishing maternal immune activation (MIA) as a causal factor for altered brain and behavioral development in offspring. We recently extended these findings to a species more closely related to humans by demonstrating that rhesus monkeys (Macaca mulatta) prenatally exposed to MIA also develop abnormal behaviors. Here, for the first time, we present initial evidence of underlying brain pathology in this novel nonhuman primate MIA model. Pregnant rhesus monkeys were injected with a modified form of the viral mimic polyI:C (poly ICLC) or saline at the end of the first trimester. Brain tissue was collected from the offspring at 3.5 years and blocks of dorsolateral prefrontal cortex (BA46) were used to analyze neuronal dendritic morphology and spine density using the Golgi-Cox impregnation method. For each case, 10 layer III pyramidal cells were traced in their entirety, including all apical, oblique and basal dendrites, and their spines. We further analyzed somal size and apical dendrite trunk morphology in 30 cells per case over a 30μm section located 100±10μm from the soma. Compared to controls, apical dendrites of MIA-treated offspring were smaller in diameter and exhibited a greater number of oblique dendrites. These data provide the first evidence that prenatal exposure to MIA alters dendritic morphology in a nonhuman primate MIA model, which may have profound implications for revealing the underlying neuropathology of neurodevelopmental disorders related to maternal infection. Corresponding author (for editorial process), Ruth Weir Ph.D. Department of Psychiatry & Behavioral Sciences, University of California, Davis, CA, USA ; The MIND Institute, University of California, Davis, 2825 50th Street, Sacramento, CA 95817, USA. rkweir@ucdavis.edu 916-703-0341. Corresponding author (for publication), Melissa Bauman Ph.D. Department of Psychiatry & Behavioral Sciences, University of California, Davis, CA, USA ; The MIND Institute, University of California, Davis, 2825 50th Street, Sacramento, CA 95817, USA. mdbauman@ucdavis.edu 916-703-0377. #Co-senior author, †Deceased HHS Public Access Author manuscript Brain Behav Immun. Author manuscript; available in PMC 2017 November 05. Published in final edited form as: Brain Behav Immun. 2015 August ; 48: 139–146. doi:10.1016/j.bbi. 2015.03. 009. A uhor M anscript
Comment on cp-2022-26 Anonymous Referee # 2 Referee comment on " Threshold in orbital forcing for Saharan greening lowers with rising levels of greenhouse gases "

This study presents modeling results of the last 190,000 years of African rainfall and vegetation history, classifying certain thresholds as “African humid periods” and commenting on the strength, duration, and rates of change of these differing AHPs. The authors find that orbital forcing is the primary driver for changes in rainfall and vegetation extent during past AHPs, but that the sensitivity threshold of AHPs to orbital forcing is modulated by GHG concentrations. Future modeling experiments are also conducted that show future AHPs are more likely to occur with higher concentrations of GHGs, as future orbital insolation thresholds are too low to induce AHPs without GHG increases. This study is well motivated and provides novel findings with respect to previously unknown factors contributing to the strength, duration, and rates of change of past AHP. paper clearly results believe one area some added discussion on the uncertainties present within the very coarse model resolution of CLIMBER-2. It is important to show that the authors have considered all of the uncertainties involved in using this specific model and conclude that these uncertainties do not impact the conclusions of this paper – i.e., this is the perfect fit for with this question.
An Approach to Processing News Text Messages Based on Markeme Analysis

The complexity problem of automatic filtering of messages retrieved from online media platforms and social networks is discussed. The review of approaches in document representation, feature weighting schemes and feature selection techniques is provided. In the paper an approach to the text messages processing based on the markeme analysis is suggested. Markemes identification is based on calculating the Index of Textual Markedness (InTeM). Markemes are words most important for a particular text and occur with the frequency, which is higher than that of the words of the same length. Preliminary results of the exploratory study of the proposed approach, applied to the news messages classification and clustering, are presented and discussed.
Research on In-Loop Simulator System for ABS Controller

During the research and development processes of ABS controller's test bench,by using a PC-based Hardware-in-the-Loop Simulator(HILS) system for ABS that has been developed in Matlab environment.The advantages of the system include shortening the period of developing,low cost and improving performance of the simulation.This system achieves to test and evaluation of ABS control under the different road condition,and provides a fairly well test bench for the development of ABS controller.
The Effect of Person-Specific Biometrics in Improving Generic Stress Predictive Models

—Because stress is subjective and is expressed differently from one person to another, generic stress prediction models (i.e., models that predict the stress of any person) perform crudely. Only person-speciﬁc ones (i.e., models that predict the stress of a preordained person) yield reliable predictions, but they are not adaptable and costly to deploy in real-world environments. For illustration, in an ofﬁce environment, a stress monitoring system that uses person-speciﬁc models would require collecting new data and training a new model for every employee. Moreover,once deployed,the models would deteriorate and need expensive periodic upgrades because stress is dynamic and depends on unforeseeable factors. We propose a simple, yet practical and cost-effective calibration technique that derives an accurate and personalized stress prediction model from physiological samples collected from a large population. We validate our approach on two stress datasets. The results show that our technique performs much better than a generic model. For instance, a generic model achieved only a 42.5% ± 19.9% accuracy. However, with only 100 calibration samples, we raised its accuracy to 95.2% ± 0.5% . We also propose a blueprint for a stress monitoring system based on our strategy, and we debate its merits and limitation. Finally, we made public our source code and the relevant datasets to allow other researchers to replicate our ﬁndings.
Sequential recovery processes of tungsten from hubnerite mineral concentrate of Qash Amir mineralized quartz veins, South Eastern Desert, Egypt

A hubnerite (MnWO4) mineral concentrate was prepared from the mineralized quartz veins of Gabal Qash Amir muscovite granite. This Wconcentrate was chemically processed to obtain the more economic highly pure tungsten oxide. To accomplish this goal, two successive atmospheric agitation leaching processes were conducted. The first includes HCl acid agitation leaching to breakdown hubnerite mineral structure. The purpose was also to get rid of the associated Mn and Fe contents from one hand, and to prepare the more important intermediate tungstic acid from the other hand. From the latter, a sodium tungstate (Na2HWO4) leach liquor was adequately prepared after applying the second atmospheric NaOH agitation leeching process using the optimum conditions of 40% caustic soda concentration at solid/ liquid ratio of 1/3, 180oC for 2h. Finally, 7% Alamine 336 in kerosene was used for extracting 93.7% of W content under the studied optimum conditions of O/A ratio of 1/1, contact time 7 min at room temperature (20±5oC) and pH of 8.
Assessment of Community Radio on Social and Economic Development: A Case Study of Spider Radio FM 1027 Kaduna Polytechnic Nigeria

The main hope of a community radio is to improve the living conditions of the community it serves by dispensing knowledge to the community through its programmes. The same process is taking place in the conventional radios stations. Despite this, we find that the community radio faces a lot of challenges of poor management due to lack of professionalism with exception of campus radio stations that are purposely meant to train students on road casting. Community radio plays a vital role in the communications of a country. Community radio mainly serves a definite community and is a form of public-service broadcasting. It reflects the culture, ideology and thoughts, norms and values of a particular community (Khan, Khan, Hassan Ahmed and Haque, 2017). It is the media of citizens of a particular community that has become popular and new opportunity for both the media practitioner and grassroots people. Fraser (2001) posited that community radio helps to avail utilities and amenities for various development aspects of our society like education, health, water and sanitation, protection from natural disasters, address social issues at the community level and connect rural population with the government. It is because of all these glaring functions of community radio that this study intends to assess the impact of Spider Radio FM, 102.7 in its role of developing the social and economic development of its listeners.
Green AI: Do Deep Learning Frameworks Have Different Costs?

The use of Artificial Intelligence (AI), and more specifically of Deep Learning (DL), in modern software systems, is nowadays widespread and continues to grow. At the same time, its usage is energy de-manding and contributes to the increased CO2 emissions, and has a great financial cost as well. Even though there are many studies that examine the capabilities of DL, only a few focus on its green aspects, such as energy consumption. This paper aims at raising awareness of the costs incurred when using different DL frameworks. To this end, we perform a thorough empirical study to measure and compare the energy consumption and run-time performance of six different DL models written in the two most popular DL frameworks, namely PYTORCH and TENSORFLOW. We use a well-known benchmark of DL models, Deep LEARNINGEXAMPLES, created by NVIDIA, to compare both the training and inference costs of DL. Finally, we manually investigate the functions of these frameworks that took most of the time to execute in our experiments. The results of our empirical study reveal that there is a statistically significant difference between the cost incurred by the two DL frameworks in 94% of the cases studied. While Tensorflow achieves significantly better energy and run-time performance than PYTORCH, and with large effect sizes in 100% of the cases for the training phase, PYTORCH instead exhibits significantly better energy and run-time performance than TENSORFLOW in the inference phase for 66% of the cases, always, with large effect sizes. Such a large difference in performance costs does not, however, seem to affect the accuracy of the models produced, as both frameworks achieve comparable scores under the same configurations. Our manual analysis, of the documentation and source code of the functions examined, reveals that such a difference in performance costs is under-documented, in these frameworks. This suggests that developers need to improve the documentation of their DL frameworks, the source code of the functions used in these frameworks, as well as to enhance existing DL algorithms.
EFFECT OF DIETARY SOLUBLE FIBER ON GUT MICROBIOTA IN THE SUGAR GLIDER (PETAURUS BREVICEPS): A PILOT STUDY

Introduction The sugar glider (Petaurus breviceps) is an exudativore in nature, eating plant gums, saps, resins, manna, and nectars as well as insect-based honeydew and lerp, with proportions of various ingredients highly dependent on seasonality and locale (Smith, 1982; Howard, 1989). Despite a well-developed cecum that could, in theory, harbor microbial populations with fermentative capabilities (Hume, 1999), a majority of captive gliders are fed diets comprising a high proportion of simple sugars such as nectars and domestic fruits (Dierenfeld et al., 2006). This investigation was undertaken to examine effects of added soluble dietary fiber on gut microbial populations in sugar gliders.
Human Capital and Economic Opportunity : A Global Working Group Working Paper Series

Parental beliefs, recognised by child psychologists as a causal influence on early development, are incorporated into a two-period model of human capital accumulation. In the first period parents transfer their beliefs, distinct from genes, to their child by signalling their „belief in a just world‟ or the perceived return to effort. The child responds by choosing effort, irrespective of the real world returns, which combines with their genes to create early their ability. This ability determines the rate of return to second period investment and final attainment. This is an ontological model, in the sense that children‟s beliefs influence the attainment they achieve. The identifying assumption is that parent beliefs are slow-moving and they not conditioned on the child. If parents are credit constrained, then both beliefs and income determine attainment. Empirical analysis using the NCDS shows that beliefs are a strong predictor of early attainment and reduces the importance of parental income or wealth. This justifies effective mentoring programmes for children with problem parents. 1 This paper is based on a chapter of my Doctoral thesis entitled “How Parent Transfers Affect children‟s Incentives, with Incomplete Capital Markets” submitted in September, 2010. The views and findings expressed herein do not in any way reflect the views of HM Treasury and are a personal assessment of the author.
Computer-assisted analysis of polysomnographic recordings improves inter-scorer associated agreement and scoring times

Study Objectives: To evaluate the current levels of human-expert performance and to assess the possible benefits of computer-assisted analysis for the scoring of polysomnographic sleep recordings (PSGs). Methods: A group of 12 expert scorers independently reviewed 5 PSGs for each of the following tasks: (i) sleep stating, (ii) detection of EEG arousals, (iii) analysis of the respiratory activity, and (iv) identification of leg movements, thus amounting to a total of 20 recordings. For each one, rescoring was repeated separately, using the classical visually-based manual approach and a computer-assisted semi-automatic procedure. Resulting inter-scorer agreement and scoring times were examined and compared among the two methods. Results: Computer-assisted sleep scoring showed a consistent and statistically relevant effect toward less time required for the completion of each of the PSG scoring tasks. Gain factors ranged from 1.26 (EEG arousals) to 2.41 (limb movements). Inter-scorer kappa agreement was also consistently increased with the use of supervised semi-automatic scoring. Specifically, from K = 0.76 to K = 0.80 (sleep stages), K = 0.72 to K = 0.91 (limb movements), K = 0.55 to K = 0.66 (respiratory activity), and K = 0.58 to K = 0.65 (EEG arousals). Inter-scorer agreement on the examined set of diagnostic indices did also show a trend toward higher Interclass Correlation Coefficient scores when using the semi-automatic scoring approach. Conclusions: Computer-assisted analysis can improve inter-scorer agreement and scoring times associated with the review of PSG studies resulting in higher efficiency and overall quality in the diagnosis sleep disorders.