Miracle-dz/UniFAR-FTUs huggingface dataset:
RELICS: spectroscopy of gravitationally lensed z ≃ 2 reionization-era analogues and implications for C iii] detections at z > 6

Recent observations have revealed the presence of strong C iii] emission (EW$_{\rm {C\,{\small III}]}}\gt 20$ Å) in z > 6 galaxies, the origin of which remains unclear. In an effort to understand the nature of these line emitters, we have initiated a survey targeting C iii] emission in gravitationally lensed reionization-era analogues identified in Hubble Space Telescope imaging of clusters from the Reionization Lensing Cluster Survey. Here, we report initial results on four galaxies selected to have low stellar masses (2–8 × 107 M⊙) and J125-band flux excesses indicative of intense [O iii] + H β emission (EW$_{\rm {[O\,{\small III}]+H\,\beta }}$ = 500–2000 Å), similar to what has been observed at z > 6. We detect C iii] emission in three of the four sources, with the C iii] EW reaching values seen in the reionization era (EW$_{\rm {C\,{\small III}]}}\simeq 17\! -\! 22$ Å) in the two sources with the strongest optical line emission (EW$_{\rm {[O\,{\small III}]+H\,\beta }}\simeq 2000$ Å). We have obtained a Magellan/FIRE (Folded-port InfraRed Echellette) near-infrared spectrum of the strongest C iii] emitter in our sample, revealing gas that is both metal poor and highly ionized. Using photoionization models, we are able to simultaneously reproduce the intense C iii] and optical line emission for extremely young (2–3 Myr) and metal-poor (0.06–0.08 Z⊙) stellar populations, as would be expected after a substantial upturn in the star formation rate of a low-mass galaxy. The sources in this survey are among the first for which C iii] has been used as the primary means of redshift confirmation. We suggest that it should be possible to extend this approach to z > 6 with current facilities, using C iii] to measure redshifts of objects with IRAC excesses indicating EW$_{\rm {[O\,{\small III}]+H\,\beta }}\simeq 2000$ Å, providing a method of spectroscopic confirmation independent of Ly α.
Single-qubit measurement of Heisenberg uncertainty lower bounds for three incompatible observables

Being one of the centroidal concepts in quantum theory, the fundamental constraint imposed by Heisenberg uncertainty relations has always been a subject of immense attention and challenging in the context of joint measurements of general quantum mechanical observables. In particular, the recent extension of the original uncertainty relations has grabbed a distinct research focus and set a new ascendent target in quantum mechanics and quantum information processing. In the present work we explore the joint measurements of three incompatible observables, following the basic idea of a newly proposed error trade-off relation. In comparison to the counterpart of two incompatible observables, the joint measurements of three incompatible observables are more complex and of more primal interest in understanding quantum mechanical measurements. Attributed to the pristine idea proposed by Heisenberg in 1927, we develop the error trade-off relations for compatible observables to categorically approximate the three incompatible observables. Implementing these relations we demonstrate the first experimental witness of the joint measurements for three incompatible observables using a single ultracold $^{40}Ca^{+}$ ion in a harmonic potential. We anticipate that our inquisition would be of vital importance for quantum precision measurement and other allied quantum information technologies.
What’s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering

Multi-hop textual question answering requires combining information from multiple sentences. We focus on a natural setting where, unlike typical reading comprehension, only partial information is provided with each question. The model must retrieve and use additional knowledge to correctly answer the question. To tackle this challenge, we develop a novel approach that explicitly identifies the knowledge gap between a key span in the provided knowledge and the answer choices. The model, GapQA, learns to fill this gap by determining the relationship between the span and an answer choice, based on retrieved knowledge targeting this gap. We propose jointly training a model to simultaneously fill this knowledge gap and compose it with the provided partial knowledge. On the OpenBookQA dataset, given partial knowledge, explicitly identifying what’s missing substantially outperforms previous approaches.
Radar Interference Mitigation for Automated Driving

Autonomous driving relies on a variety of sensors, especially on radars, which have unique robustness under heavy rain/fog/snow and poor light conditions. With the rapid increase of the amount of radars used on modern vehicles, where most radars operate in the same frequency band, the risk of radar interference becomes a compelling issue. This article analyses automotive radar interference and proposes several new approaches, which combine industrial and academic expertise, toward the path of interference-free autonomous driving.
New Formulation of Mixed-Integer Conic Programming for Globally Optimal Grasp Planning

We present a two-level branch-and-bound (BB) algorithm to compute the optimal gripper pose that maximizes a grasp metric in a restricted search space. Our method can take the gripper's kinematics feasibility into consideration to ensure that a given gripper can reach the set of grasp points without collisions or predict infeasibility with finite-time termination when no pose exists for a given set of grasp points. Our main technical contribution is a novel mixed-integer conic programming (MICP) formulation for the inverse kinematics of the gripper that uses a small number of binary variables and tightened constraints, which can be efficiently solved via a low-level BB algorithm. Our experiments show that optimal gripper poses for various target objects can be computed taking 20-180 minutes of computation on a desktop machine and the computed grasp quality, in terms of the $\mathbf {Q}_1$ metric, is better than those generated using sampling-based planners.
Some Remarks on the Middle Levels Theorem

Some remarks on the middle-levels graphs, their theorem on the existence of Hamilton cycles by T. Mutze and the short proof of this by P. Gregor, T. Mutze and J. Nummenpalo are provided by means of restricted-growth strings. This allows to present a construction as in the cited proof by combining the cycles provided by the union of the 1-factors of the first two lexical colors, 0 and 1, and certain 6-cycles that connect them, an alternative visualizing methodology of that used originally.
Language models and Automated Essay Scoring

In this paper, we present a new comparative study on automatic essay scoring (AES). The current state-of-the-art natural language processing (NLP) neural network architectures are used in this work to achieve above human-level accuracy on the publicly available Kaggle AES dataset. We compare two powerful language models, BERT and XLNet, and describe all the layers and network architectures in these models. We elucidate the network architectures of BERT and XLNet using clear notation and diagrams and explain the advantages of transformer architectures over traditional recurrent neural network architectures. Linear algebra notation is used to clarify the functions of transformers and attention mechanisms. We compare the results with more traditional methods, such as bag of words (BOW) and long short term memory (LSTM) networks.
Bayesian Optimization for Iterative Learning

The performance of deep (reinforcement) learning systems crucially depends on the choice of hyperparameters. Their tuning is notoriously expensive, typically requiring an iterative training process to run for numerous steps to convergence. Traditional tuning algorithms only consider the final performance of hyperparameters acquired after many expensive iterations and ignore intermediate information from earlier training steps. In this paper, we present a Bayesian optimization(BO) approach which exploits the iterative structure of learning algorithms for efficient hyperparameter tuning. We propose to learn an evaluation function compressing learning progress at any stage of the training process into a single numeric score according to both training success and stability. Our BO framework is then tradeoff the benefit of assessing a hyperparameter setting over additional training steps against their computation cost. We further increase model efficiency by selectively including scores from different training steps for any evaluated hyperparameter set. We demonstrate the efficiency of our algorithm by tuning hyperparameters for the training of deep reinforcement learning agents and convolutional neural networks. Our algorithm outperforms all existing baselines in identifying optimal hyperparameters in minimal time.
The stability of the 18-fold symmetry soft-matter quasicrystals

This letter presents a study on the stability of the 12-fold symmetry soft-matter quasicrystals from the angle of thermodynamics combining dynamics of the matter. The results are quantitative, which depend upon only the material constants of the novel phase and very simple and intuitive, these material constants can be measured by experiments.
Altered rheological properties of red and white cells during normal gestation

Beside plasma viscosity and the functional state of the vessel wall, placental perfusion is determined by the concentration and behaviour of the blood cells. In order to evaluate the contribution of erythrocytes to placental flow in late pregnancy, we applied a new filtration technique allowing to analyze the passages of erythrocytes through a single pore. As a parameter of decreased deformability in late pregnancy, we found a significant increase in transit time (22.69 +/1.60 ms vs 24.03 +/2.40 ms; pore size 4.1 pm; hydrostatic pressure 100 Pa) correlating with the mean cell volume (r=0.763). Due to their size and rigidity, their property to adhere to altered endothelium and to release radicals and mediators, granulocytes were the second point of interest to investigate in late pregnancy. Granulocytes were more adherent in vitro when stimulated with PMA, FMLP or in the absence of these mediators. This was shown for BSAand fibronectincoated wells (BSA: PMA 72.6% vs 67.2%, FMLP 43.4% vs 36.1%, unstimulated 26.5% vs 15.4%; fibronectin: PMA 71.6% vs 66.7%, FMLP 44.7% vs 31.1 %, unstimulated 25.7% vs 18.2%). Transit times of granulocytes through 8 pm pores were significantly shorter in the pregnant group at 400 Pa (5.02 +/0.50 ms vs 5.49 +/0.74 ms), nearly identical at 200 Pa (9.34 +/1.79 ms vs 9.57 +/1.65 ms) and impossible to measure at 100 Pa (frequent obstruction). Luminol-enhanced whole blood chemiluminescence of unstimulated granulocytes was decreased in the pregnant group (0.19 counts/ce11l2s vs 0.33 counts/cell12s) but exceeded values of the control group when stimulated with PMA or FMLP (PMA: 20.13 counts/cell/2s vs 15.90 counts/cell/2s; FMLP: 2.35 counts/cell/2s vs 1.94 counts/cell/2s). Our results may indicate that in times of insufficient placental flow, as it is seen in hypertension-related gestational disorders, a decreased erythrocyte deformability as well as an enhanced adhesivity and chemiluminescence of activated granulocytes may perpetuate and aggravate placental malperfusion.
Optimal Dividend for Insurance Group with External Default Contagion

This paper studies the optimal dividend for a multi-line insurance group, in which each insurance company is exposed to some external credit default risk. The external default contagion is considered in the sense that one default event can affect the default probabilities of all surviving insurance companies. The total dividend problem is formulated for the insurance group and we reveal for the first time that the optimal singular dividend strategy is still of the barrier type. Furthermore, we show that the optimal barrier for each insurance company is modulated by the current default state, namely how many and which companies have defaulted will determine the dividend threshold for each surviving company. These interesting conclusions match with observations from the real market and are based on our analysis of the associated recursive system of Hamilton-Jacobi-Bellman variational inequalities (HJBVIs), which is new to the literature. The existence of classical solution is established and the rigorous proof of the verification theorem is provided. For the case of two companies, the value function and optimal barriers for each company can be explicitly constructed.
Post-Disturbance Dynamic Frequency Features Prediction Based on Convolutional Neural Network

The significant imbalance between power generation and load caused by severe disturbance may make the power system unable to maintain a steady frequency. If the post-disturbance dynamic frequency features can be predicted and emergency controls are appropriately taken, the risk of frequency instability will be greatly reduced. In this paper, a predictive algorithm for post-disturbance dynamic frequency features is proposed based on convolutional neural network (CNN) . The operation data before and immediately after disturbance is used to construct the input tensor data of CNN, with the dynamic frequency features of the power system after the disturbance as the output. The operation data of the power system such as generators unbalanced power has spatial distribution characteristics. The electrical distance is presented to describe the spatial correlation of power system nodes, and the t-SNE dimensionality reduction algorithm is used to map the high-dimensional distance information of nodes to the 2-D plane, thereby constructing the CNN input tensor to reflect spatial distribution of nodes operation data on 2-D plane. The CNN with deep network structure and local connectivity characteristics is adopted and the network parameters are trained by utilizing the backpropagation-gradient descent algorithm. The case study results on an improved IEEE 39-node system and an actual power grid in USA shows that the proposed method can predict the lowest frequency of power system after the disturbance accurately and quickly.
AIMC 2017 Asia International Multidisciplinary Conference TRAINING & DEVELOPMENT AND PERFORMANCE OF NIGERIAN EDUCATIONAL SECTOR

The purpose of this study is to investigate the mediating effect of ethical climate (EC) on the link between training and development and organizational performance (OP). Resource Based View (RBV) stress that, internal resources play a vital role on OP, considering training and development as one of the dimension of HRM practices can be regarded as best internal resources influencing performance in organization. Some previous studies used group practices while others considered individual practice. Despite these studies, however, few studies have attempted investigating effect of training and development on OP. However, the mechanism through which training and development influence performance is not clearly explained in the literature, even though RBV highlighted that HRM system can indirectly leads to competitive advantage in organization through resource like ethics. This paper used EC as mediating variable on the link between training and development and OP to explain the original relationship. A quantitative survey approach was used; the data were collected from some selected ministries of education in Nigeria. A total of 181questionnaires were analyse using Smart PLS SEM 3.2. 6. The statistical findings shown that EC mediates the relationship between training and development and OP. The study also suggested for future research. © 2018 Published by Future Academy www.FutureAcademy. org.UK
Selected Papers of Beijing Forum 2010 Universal Health Care Coverage in China : Challenges and Opportunities

China's new round health systems reform aims to achieve universal health care coverage by 2020. Universal health care coverage has three dimensions, breadth (population coverage), depth (service provision, and height (financial protection), according to WHO's analytic framework. This article analyzes situation of universal coverage in China using existing data and raises challenges and opportunities for achieving the aims. © 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Beijing Forum. Basic clinical health care is provided under a system of three health insurance schemes in both rural and urban areas. In terms of population coverage, 90% of rural population and 65% of urban residents have been covered by the social health insurance schemes, reaching a relatively high population coverage level. However, due to the low level of premiums, especially in the rural health insurance scheme, scope of services and financial protection are constrained. In 2008, per capita premiums of the urban employeebased health insurance and resident-based health insurance were 14 and 4 times higher than that of the rural health insurance. Even though provision of public health care is overall good, there exist disparities in service provision between regions, the rural and urban areas, and population groups. Population coverage of maternal health care and chronic disease management need to be improved. For prenatal health care and chronic disease management, population coverage in rural area is about 50% lower than the urban cities. In addition, there is a gap between standard of health care provision and actual practice. For example, only 78.5% and 43.9% of urban and rural pregnant women utilized more than 5 times of prenatal care. Insufficient provision of public health care would affect control of medical cost escalation. China's government has implemented a number of policies and interventions for the universal health care coverage, including improving the existing health financing system, advancing provision of public health care, and strengthening interventions for priority public health problems. It is important to realize the challenges facing the government, including imbalance in economic and social development, the role of health care in agenda of the local government, low per capita health resource, and balance of different interest groups. Available online at www.sciencedirect. com © 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license. Selection and/or peer-review under responsibility of Beijing Forum 331 Meng Qingyue and Tang Shenglan / Procedia Social and Behavioral Sciences 77 ( 2013 ) 330 – 340
COMMISSION ON GENETIC RESOURCES FOR FOOD AND AGRICULTURE THE IMPACT OF CLIMATE CHANGE ON COUNTRIES’ INTERDEPENDENCE ON GENETIC RESOURCES FOR FOOD AND AGRICULTURE PRELIMINARY VERSION

This document has been prepared at the request of the Secretariat of the Commission on Genetic Resources for Food and Agriculture by the Consultative Group on International Agricultural Research (CGIAR), coordinated by Bioversity International, as a contribution to the cross-sectoral theme, Consideration of policies and arrangements for access and benefit-sharing for genetic resources for food and agriculture, which the Commission will consider at its Twelfth Regular Session.
Shock Wave Therapy for Acute and Chronic Soft Tissue Wounds : A Feasibility Study 1

*AUVA-Trauma Center Meidling, Vienna, Austria; †Zentrum für Extracorporale Stosswellentherapie, Berlin, Germany; ‡Hadassah-Hebrew University Hospital, Mount Scopus, Jerusalem, Israel; §Georgetown University Hospital, Washington, District of Columbia; Combat Wound Initiative, Department of Surgery, Walter Reed Army Medical Center, Washington, District of Columbia; Brooke Army Medical Center, Department of Surgery, Fort Sam Houston, Texas; and £Combat Wound Initiative, National Naval Medical Center, Bethesda, Maryland, and Regenerative Medicine, Naval Medical Research Center, Silver Spring, Maryland
Direct and diffuse solar radiation components estimation based on RBF model : Case study

The current study, propose a new application of radial basis functions neural network for estimating direct normal radiation (DNR) and diffuse solar radiation (DSR) components based on different inputs parameters. The proposed methodology was validated and tested on limited data set recorded over three years (2014-2016) in a semi-arid climate in Algeria.The experimental results show that RBF-model was highly qualified for estimating DNR and DSR with high performance accuracy. The obtained statistical parameters of Normalized Root Mean Square Error (NRMSE), Determination Coefficient (R) and Correlation Coefficient (r) for DNR and DSR are: 0.030, 97.30 %, 98.60% and 0.044, 94.34%, 97.61%, respectively.
Enhanced Feature Specific Collaborative Filtering Model For Aspect Opinion And Temporal Based Product Recommendation

: nowadays, the online purchasing and advertising becomes massively increased due to the increase in utilization of internet services by the users. For the product sale and its quality description, the product‘s customer review plays a significant role. Thus, the words and phrases with large size in a raw data is converted into numerical values based on the opinion prediction method. The fault prediction of the reviews and inappropriate recommendation of the best product to the users are the main challenging issues in recent days. To avoid these issues, an Enhanced Feature Specific Collaborative Filtering Model (EFCFM) is proposed for Aspect Opinion and Temporal Based Product Recommendation system. Initially, the raw data is preprocessed using stop word removal technique and the keywords from that preprocessed data is extracted using POS tagger which has both positive and negative polarity. The features of the keywords are extracted from the Senti-WordNet database, product property from the POS tagger and the reviews from the user ratings. Then the Enhanced Feature Specific Collaborative Filtering Model is used to calculate the product‘s strength and weakness. Also it helps to predict the corresponding characteristics and its opinions. After that, the user query is also analyzed and finally, the opinion score based product recommendation is obtained. The proposed EFCFM technique is analyzed comparatively with other existing techniques with the metrics like precision, recall, f-measure, RMSE, and the MAE. The evaluation results show that the proposed EFCFM technique offers best product recommendations accurately to the users.
Plant functional types as estimator of carbon in dry forest of the Colombian Caribbean

Functional traits variation, as part of a plant functional types (PFT) for knowing carbon plant performance, has been poorly studied. The objective of this research was to establish the best PFT for carbon store prediction, in aboveground biomass in Colombian Caribbean dry forest, through a) evaluation of 4 functional traits associated with the stored carbon, b) characterizing and comparing PFT according with the variation of functional traits, and c) to determine PFT that better predicts carbon. In 5 permanent parcels of 1 ha, and trees with a diameter ≥ 10 cm, measurements of specific leaf area; leaf dry matter, leaf nitrogen concentration and leaf carbon concentration were taken. Two PFT were generated with analysis of hierarchical conglomerates. The obtained values of carbon in every single PFT, and estimated by the allometric equation are compared with the estimated for the arboreal community of the parcel. The PFT 1 was the best carbon predictor in the 5 parcels with conservative use of resources. This study contributes to support actions over the entailment of dry forest in carbon markets. © 2017 Universidad Nacional Autónoma de México, Instituto de Biología. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).