Nearness
The purpose of this study was to examine whether subjective nearness-to-death moderated the association between health worries and death anxiety due to the COVID-19 outbreak among older adults in Israel. Using an online survey, 277 participants were recruited and completed self-reported questionnaires, which included background characteristics, exposure to COVID-19 risk experiences, self-rated health, subjective nearness-to-death, health worries regarding COVID-19, and death anxiety. Findings revealed that subjective nearness-to-death moderated the association between health worries and death anxiety. The importance and significance of subjective perceptions concerning the distance from death as far, which may serve as a resilient resource, is discussed.
nearness
>The Covid-19 crisis teaches us how priceless human nearness is. Art and education can't do without it either. Like works of art, people lose their aura when kept at digital arm's length. Art is lifeless when it can't resonate with bodies. In Nearness, Marlies De Munck and Pascal Gielen diagnose a new reality. Only culture marks the difference between surviving and living.
Divine nearness is dependent upon attainment to the knowledge of God, upon severance from all else save God. It is contingent upon self-sacrifice and to be found only through forfeiting wealth and worldly possessions. It is made possible through the baptism of water and fire revealed in the Gospels. Water symbolizes the water of life, which is knowledge, and fire is the fire of the love of God; therefore, man must be baptized with the water of life, the Holy Spirit and the fire of the love of the Kingdom. Until he attains these three degrees, nearness to God is not possible.
Draw nigh unto Him with a pure heart, cheerful face, gazing eye and a joyful spirit and plunge with thy whole being into the sea of the love of God and forget all else save Him, so that thou mayest be filled with such spiritual sentiments from the kingdom of God, which will take the reins of desire from thy hands and move thee with the power of thy Lord, just as the wind moveth a mote in the open air as it willeth. At that time we will draw unto each other in spirit, a nearness which will be eternal, everlasting and endless.
Man has different ways of approaching God. One man thinks he must make extraordinary efforts in science to arrive at the knowledge of the divine and another thinks that he must train his morals. The prophets teach us that the only way to approach God is by characterizing ourselves with the attributes of divinity. Some people lay stress on fasting. They affirm that in augmenting the weakness of the body they develop a spiritual sensibility and thus they think to approach God. Weakening one's self physically does not necessarily contribute to spiritual progress. Humility, kindness, resignation, and all these spiritual attributes emanating from great physical strength are acceptable to God. That an enfeebled man cannot fight is not accounted a virtue. Were physical weakness a virtue the dead would be perfect, for they can do nothing. If a man be just, kind, humble and merciful and his qualities are acquired through the will-power -- this is Godlike. A child cannot kill a man; but a Bonaparte can abstain from war, from shedding blood, from devastating countries. A dumb person will not speak ill of any one, a paralyzed hand cannot strike; but a strong arm can refrain from striking. Justice, love and kindness must be the instruments of strength, not of weakness. Exaggerated fasting destroys the divine forces. God has created man in a way that cannot be surpassed; we must not try to change his creation. Strive to attain nearness to reality through the acquisition of strength of character, through morality, through good works and helping the poor, through being consumed with the fire of the love of God and in discovering each day new spiritual mysteries. This is the path of intimate approach.
O thou enraptured handmaid of God! Nearness is verily of the soul, not of the body; and the help that is sought, and the help that cometh, is not material but of the spirit; nevertheless it is my hope that thou wilt attain to nearness in every sense. The bounties of God will verily encompass a sanctified soul even as the sun's light doth the moon and stars: be thou assured of this.
Verily, I have read the expressions of thy longing for the visit and thy craving to come to this Brilliant Spot. But the violators of the Covenant of God have stirred up the dust of deception, and besides this, there are numerous obstacles and it is impossible for thee to come in these times. But verily, I pray my Lord to make this success feasible unto thee in a future time. There is for this a mature wisdom concealed from sight, but it shall appear as clear as the sun in midday. As to thee, be rejoiced at the glad-tidings of thy Lord and trust in His great gifts, the lights of which have shone forth upon the horizons of hearts and souls, and trust in the assistance of thy Master, and ask what thou wishest of the gifts of thy Lord, the Unconstrained! Draw nigh unto Him with a pure heart, cheerful face, gazing eye and a joyful spirit and plunge with thy whole being into the sea of the love of God and forget all else save Him, so that thou mayest be filled with such spiritual sentiments from the kingdom of God, which will take the reins of desire from thy hands and move thee with the power of thy Lord, just as the wind moveth a mote in the open air as it willeth. At that time we will draw unto each other in spirit, a nearness which will be eternal, everlasting and endless.
These are very exalted teachings, all pointing to a new way of life in which the believer must be vigilant not to abuse his God-given powers. To the extent that the individual can abide by these standards in his daily life, and is able to harmonize his thoughts, his words and his deeds with the Will of his Creator, he can succeed in reaching the goal of nearness to God.
Various problems in machine learning, databases, and statistics involve pairwise distances among a set of objects. It is often desirable for these distances to satisfy the properties of a metric, especially the triangle in- equality. Applications where metric data is useful include clustering, classification, metric-based indexing, and approximation algorithms for various graph problems. This paper presents the Metric Nearness Prob- lem: Given a dissimilarity matrix, find the "nearest" matrix of distances that satisfy the triangle inequalities. For p nearness measures, this pa- per develops efficient triangle fixing algorithms that compute globally optimal solutions by exploiting the inherent structure of the problem. Empirically, the algorithms have time and storage costs that are linear in the number of triangle constraints. The methods can also be easily parallelized for additional speed.
Matrix nearness problems [6] offer a natural framework for developing this idea. If there are n points, we may collect the measurements into an n n symmetric matrix whose (j, k) entry represents the dissimilarity between the j-th and k-th points. Then, we seek to approximate this matrix by another whose entries satisfy the triangle inequalities. That is,
The most relevant research appears in a recent paper of Roth et al. [11]. They observe that machine learning applications often require metric data, and they propose a technique for metrizing dissimilarity data. Their method, constant-shift embedding, increases all the dissimilarities by an equal amount to produce a set of Euclidean distances (i.e., a set of numbers that can be realized as the pairwise distances among an ensemble of points in a Euclidean space). The size of the translation depends on the data, so the relative and ab- solute changes to the dissimilarity values can be large. Our approach to metrizing data is completely different. We seek a consistent set of distances that deviates as little as pos- sible from the original measurements. In our approach, the resulting set of distances can arise from an arbitrary metric space; we do not restrict our attention to obtaining Euclidean distances. In consequence, we expect metric nearness to provide superior denoising. More- over, our techniques can also learn distances that are missing entirely.
There is at least one other method for inferring a metric. An article of Xing et al. [12] proposes a technique for learning a Mahalanobis distance for data in Rs. That is, a metric dist(x, y) = (x - y)T G(x - y), where G is an s s positive semi-definite matrix. The user specifies that various pairs of points are similar or dissimilar. Then the matrix G is computed by minimizing the total squared distances between similar points while forcing the total distances between dissimilar points to exceed one. The article provides explicit algorithms for the cases where G is diagonal and where G is an arbitrary positive semi-definite matrix. In comparison, the metric nearness problem is not restricted to Ma- halanobis distances; it can learn a general discrete metric. It also allows us to use specific distance measurements and to indicate our confidence in those measurements (by means of a weight matrix), rather than forcing a binary choice of "similar" or "dissimilar."
The outline of rest of the paper is as follows. Section 2 formally describes the MN problem. In Section 3, we present algorithms that allow us to solve MN problems with p nearness measures. Some applications and experimental results follow in Section 4. Section 5 dis- cusses our results, some interesting connections, and possibilities for future research.
The metric nearness problem requests a distance matrix M that is closest to a given dis- similarity matrix D with respect to some measure of "closeness." In this work, we restrict our attention to closeness measures that arise from norms. Specifically, we seek a distance matrix M so that,
This study examines the interactions between trading strategies based on the nearness to the 52-week high, the nearness to the 52-week low, and past returns. We offer evidence that the nearness to the 52-week low has predictive power for future average returns. Our results also reveal that the nearness to the 52-week high as well as to the 52-week low and past returns each have certain exclusive unpriced information content in the cross-sectional pricing of stocks. Moreover, a trading strategy based on the nearness to the 52-week low provides an excellent hedge for the momentum strategy, thereby nearly doubling the Sharpe ratio of the momentum strategy. 041b061a72