Images of Women’s Sexuality in Advertisements: A Content Analysis of Black- and White-Oriented Women’s and Men’s Magazines

This article represents an analysis of the sexual images of women in magazine advertisements. I examined advertisements in Black- and White-oriented, mens and womens magazines to compare the images of womens sexuality that are constructed for each specific audience. Over 600 images of women were analyzed based on seven dimensions, including function/role, relative function/authority, physical/body position, relative size/height, character traits, body view, and physical characteristics. The images of womens sexuality vary depending on the race of the intended audience and the race of the women in the advertisements. Advertisements for White audiences portray women in roles and with characteristics that suggest dependency and submissiveness, whereas advertisements for Black audiences portray women as independent and dominant. I also found that White women are objectified much more than Black women are.     

Images of midlife development among young,middle-aged,and older adults

This study examined patterns of perceived change across adulthood in a sample of 121 young (M age=19.8 years), middle-aged (M age=47.9 years) and older (M age=74.6 years) adults. Subjects rated amount of stress in life domains, salience of life course issues, and nature of personal attributes for young, middle-aged, and older targets. Seven prototypical patterns of perceived change were found. Midlife was seen as a period with many responsibilities, increased stress in several domains, and little time for leisure, but also as a peak in competence, ability to handle stress, sense of control, productivity, and social responsibility. The findings are discussed in terms of losses, which were seen primarily in the physical realm, and gains, which were mainly seen in the psychological and social realms.     

Image and anti-image covariance matrices from a correlation matrix that may be singular

Following the work of Tucker, Cooper, and Meredith, image and anti-image covariance matrices from a correlation matrix that may be singular are derived.     

Categorization of natural movements by pigeons: visual concept discrimination and biological motion

In three experiments, pigeons were exposed to a discriminated autoshaping procedure in which categories of moving stimuli, presented on videotape, were differentially associated with reinforcement. All stimuli depicted pigeons making defined responses. In Experiment 1, one category consisted of several different scenes of pecking and the other consisted of scenes of walking, flying, head movements, or standing still. Four of the 4 birds for which pecking scenes were positive stimuli discriminated successfully, whereas only 1 of the 4 for which pecking was the negative category did so. In the pecking-positive group, there were differences between the pecking rates in the presence of the four negative actions, and these differences were consistent across subjects. In Experiment 2, only the categories of walking and pecking were used; some but not all birds learned this discrimination, whichever category was positive, and these birds showed some transfer to new stimuli in which the same movements were represented only by a small number of point lights (Johansson's “biological motion” displays). In Experiment 3, discriminations between pecking and walking movement categories using point-light displays were trained. Four of the 8 birds discriminated successfully, but transfer to fully detailed displays could not be demonstrated. Pseudoconcept control groups, in which scenes from the same categories of motion were used in both the positive and negative stimulus sets, were used in Experiments 1 and 3. None of the 8 pigeons trained under these conditions showed discriminative responding. The results suggest that pigeons can respond differentially to moving stimuli on the basis of movement cues alone.     

卦象说初探

象是象数易学的一个重要范畴,爻象、卦象是解易的重要工具之一.卦象不仅是理解<周易>卦爻辞的根本,也是春秋筮法占断的主要依据.从春秋时期<左传>、<国语>简单的卦象,到战国时期<易传>稍具形态的卦象说,再到汉代繁杂的逸象说,卦象说的形成经历了一个漫长的历史过程,并在不同时期对易学的发展产生了不同的影响.     

《周易·说卦传》中的类问题

论文通过对<说卦>中纷繁的类现象的讨论,旨在说明周易观象取类思维的规律,从而接通中国学术中类思维的规律,指出类思维是贯穿在古代思想学说中最一般的规则.     

读宋明理学随录易学札记

<易>为性命之源,深受理学家重视.性理以八卦取象存在"性理象",有:乾为性,震为性.乾为理,坤为理,巽为理.乾为神,为易.乾为命,巽为命.艮为止,又为性德.坎为志,又为心,又为智.以性理象对<小畜><大畜><无妄><益><困><坎><成><恒><艮><大壮>等卦的象辞分析,在阐释义理易学上别开一解.作者还讨论了程颢和朱熹对"易体"概念的不同观点.程颢哲学中有"易体"概念,朱熹哲学中不需要"易体"概念.     

本易理以诂易辞 由易辞以准易象——试论尚氏易学的特色及其对易学史的贡献

本文认为,本易理以诂易辞,由易辞以准易象,这是尚秉和先生注解《周易》的基本原则,也是他独辟蹊径所发明的注易新方法。依据此一原则和方法,尚先生一扫东汉以来易家注易迂曲立说、穿凿附会之风气,穷究易理,广搜易象,以易解易,以易证易,其对《周易》经、传的诠释,"六通四辟,若合符契"。由此不仅纠正了千百年来易家注易的诸多流弊和错误,而且也牢固地确立了尚秉和先生在易学史上的大师地位。     

The cost of facing fear: Visual working memory is impaired for faces expressing fear

Previous research has identified numerous factors affecting the capacity and accuracy of visual working memory (VWM). One potentially important factor is the emotionality of the stimuli to be encoded and held in VWM. We often must hold in VWM information that is emotionally charged, but much is still unknown about how the emotionality of stimuli impacts VWM performance. In the current research, we performed four studies examining the impact of fearful facial expressions on VWM for faces. Fearful expressions were found to produce a consistent cost to VWM performance. This cost was modulated by encoding time, but not set size. This cost was only present for faces in an upright orientation consistent with this cost being a product of the emotionality of the faces rather than lower‐level perceptual differences between neutral and fearful faces. These findings are discussed in the context of existing theoretical accounts of the impact of emotion on information processing. We suggest that a number of competing effects drive both costs and benefits and are at play when emotional information must be stored in VWM, with the task context determining the balance between them.     

Application of deep transfer learning for automated brain abnormality classification using MR images

Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.