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Living Color is the first book to investigate the social history of skin color from prehistory to the present, showing how our body's most visible trait influences our social interactions in profound and complex ways. In a fascinating and wide-ranging discussion, Nina G. Jablonski begins with the biology and evolution of skin pigmentation, explaining how skin color changed as humans moved around the globe. She explores the relationship between melanin pigment and sunlight, and examines the consequences of rapid migrations, vacations, and other lifestyle choices that can create mismatches between our skin color and our environment. Richly illustrated, this book explains why skin color has come to be a biological trait with great social meaning— a product of evolution perceived by culture. It considers how we form impressions of others, how we create and use stereotypes, how negative stereotypes about dark skin developed and have played out through history—including being a basis for the transatlantic slave trade. Offering examples of how attitudes about skin color differ in the U.S., Brazil, India, and South Africa, Jablonski suggests that a knowledge of the evolution and social importance of skin color can help eliminate color-based discrimination and racism.

The mosquito then transmits it to another individual. [...]the life cycle of the Plasmodium parasite is complete1. [...]for higher accuracy we follow the colour based discrimination technique to detect the malarial parasite in the blood smear images and we use morphological operations in order to find the perimeter of the detected parasite which in turn is used for the classification of the parasite species. The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels. [...]areas of foreground pixels grow in size while holes within those regions become smaller. CONCLUSION: [...]the color based discrimination is shows accuracy of 86% and greater efficiency in the screening of the presence of malarial parasite using image processing techniques.

Background Research shows that transmasculine people experience discrimination based on their gender identity and/or expression (i.e., cissexism) while obtaining health care. However, studies examining the experience of other forms of discrimination in health care settings among diverse subgroups of transmasculine individuals, including those from minoritized racial/ethnic backgrounds, are very limited. Methods Guided by intersectionality, we designed a qualitative research study to explore how transmasculine people of color experience—and resist—multiple, intersecting forms of discrimination in health care settings. Guided by a purposive sampling strategy, we selected 19 transmasculine young adults of color aged 18–25 years to participate in 5 mini-focus groups conducted between February and May 2019 in Boston, MA. Focus group transcripts were analyzed using a template style approach to thematic analysis that involved both deductive and inductive coding using a codebook. Coded text fragments pertaining to participants’ experiences of health care discrimination were clustered into themes and sub-themes. Results Transmasculine people of color described experiencing notable challenges accessing physical and mental health care as a result of structural barriers to identifying health care providers with expertise in transgender health, finding providers who share one or more of their social positions and lived experiences, and accessing financial resources to cover high health care costs. Further, participants discussed anticipating and experiencing multiple forms of interpersonal discrimination—both independently and simultaneously—in health care settings, including cissexism, racism, weight-based discrimination, and ableism. Moreover, participants described the negative impact of anticipating and experiencing multiple interpersonal health care discrimination on their health care utilization, quality of care, and mental and physical health. Lastly, participants discussed using various strategies to resist the multiple, intersecting forms of discrimination they encounter in health care settings, including setting boundaries with health care providers, seeking care from competent providers with shared social positions, engaging in self-advocacy, drawing on peer support during health care visits, and obtaining health information through their social networks. Discussion Efforts are needed to address cissexism, racism, weight-based discrimination, ableism, and other intersecting forms of discrimination in clinical encounters, health care institutions and systems, and society in general to advance the health of transmasculine people of color and other multiply marginalized groups.

More than fifty years after the civil rights movement, there are still glaring racial inequities all across the United States. In Rich Thanks to Racism , Jim Freeman, one of the country's leading civil rights lawyers, explains why as he reveals the hidden strategy behind systemic racism. He details how the driving force behind the public policies that continue to devastate communities of color across the United States is a small group of ultra-wealthy individuals who profit mightily from racial inequality. In this groundbreaking examination of \"strategic racism,\" Freeman carefully dissects the cruel and deeply harmful policies within the education, criminal justice, and immigration systems to discover their origins and why they persist. He uncovers billions of dollars in aligned investments by Bill Gates, Charles Koch, Mark Zuckerberg, and a handful of other billionaires that are dismantling public school systems across the United States. He exposes how the greed of prominent US corporations and Wall Street banks was instrumental in creating the world's largest prison population and our most extreme anti-immigrant policies. Freeman also demonstrates how these \"racism profiteers\" prevent flagrant injustices from being addressed by pitting white communities against communities of color, obscuring the fact that the struggles faced by white people are deeply connected with those faced by people of color. Rich Thanks to Racism is an invaluable road map for all those who recognize that the key to unlocking the United States' full potential is for more people of all races and ethnicities to prioritize racial justice.

Remote sensing data has been widely used for various Earth Observation (EO) missions such as land use and cover classification, weather forecasting, agricultural management, and environmental monitoring. Most existing remote-sensing-data-based models are based on supervised learning that requires large and representative human-labeled data for model training, which is costly and time-consuming. The recent introduction of self-supervised learning (SSL) enables models to learn a representation from orders of magnitude more unlabeled data. The success of SSL is heavily dependent on a pre-designed pretext task, which introduces an inductive bias into the model from a large amount of unlabeled data. Since remote sensing imagery has rich spectral information beyond the standard RGB color space, it may not be straightforward to extend to the multi/hyperspectral domain the pretext tasks established in computer vision based on RGB images. To address this challenge, this work proposed a generic self-supervised learning framework based on remote sensing data at both the object and pixel levels. The method contains two novel pretext tasks, one for object-based and one for pixel-based remote sensing data analysis methods. One pretext task is used to reconstruct the spectral profile from the masked data, which can be used to extract a representation of pixel information and improve the performance of downstream tasks associated with pixel-based analysis. The second pretext task is used to identify objects from multiple views of the same object in multispectral data, which can be used to extract a representation and improve the performance of downstream tasks associated with object-based analysis. The results of two typical downstream task evaluation exercises (a multilabel land cover classification task on Sentinel-2 multispectral datasets and a ground soil parameter retrieval task on hyperspectral datasets) demonstrate that the proposed SSL method learns a target representation that covers both spatial and spectral information from massive unlabeled data. A comparison with currently available SSL methods shows that the proposed method, which emphasizes both spectral and spatial features, outperforms existing SSL methods on multi- and hyperspectral remote sensing datasets. We believe that this approach has the potential to be effective in a wider range of remote sensing applications and we will explore its utility in more remote sensing applications in the future.

The present study investigated the effect of object representation on attentional priority regarding distractor inhibition and target search processes while the statistical regularities of singleton distractor location were biased. A color singleton distractor appeared more frequently at one of six stimulus locations, called the ‘high-probability location,’ to induce location-based suppression. Critically, three objects were presented, each of which paired two adjacent stimuli in a target display by adding background contours (Experiment 1 ) or using perceptual grouping (Experiments 2 and 3 ). The results revealed that attention capture by singleton distractors was hardly modulated by objects. In contrast, target selection was impeded at the location in the object containing the high-probability location compared to an equidistant location in a different object. This object-based suppression in target selection was evident when object-related features were parts of task-relevant features. These findings suggest that task-irrelevant objects modulate attentional suppression. Moreover, different features are engaged in determining attentional priority for distractor inhibition and target search processes.

The visual images in the eyes contain much more information than the brain can process. An important selection mechanism is feature-based attention (FBA). FBA is best described by attention filters that specify precisely the extent to which items containing attended features are selectively processed and the extent to which items that do not contain the attended features are attenuated. The centroid-judgment paradigm enables quick, precise measurements of such human perceptual attention filters, analogous to transmission measurements of photographic color filters. Subjects use a mouse to locate the centroid—the center of gravity—of a briefly displayed cloud of dots and receive precise feedback. A subset of dots is distinguished by some characteristic, such as a different color, and subjects judge the centroid of only the distinguished subset (e.g., dots of a particular color). The analysis efficiently determines the precise weight in the judged centroid of dots of every color in the display (i.e., the attention filter for the particular attended color in that context). We report 32 attention filters for single colors. Attention filters that discriminate one saturated hue from among seven other equiluminant distractor hues are extraordinarily selective, achieving attended/unattended weight ratios >20:1. Attention filters for selecting a color that differs in saturation or lightness from distractors are much less selective than attention filters for hue (given equal discriminability of the colors), and their filter selectivities are proportional to the discriminability distance of neighboring colors, whereas in the same range hue attention-filter selectivity is virtually independent of discriminabilty.

A single M13 bacteriophage color sensor was previously utilized for discriminating the geographical origins of agricultural products (garlic, onion, and perilla). The resulting discrimination accuracy was acceptable, ranging from 88.6% to 94.0%. To improve the accuracy further, the use of three separate M13 bacteriophage color sensors containing different amino acid residues providing unique individual color changes (Wild sensor: glutamic acid (E)-glycine (G)-aspartic acid (D), WHW sensor: tryptophan (W)-histidine (H)-tryptophan (W), 4E sensor: four repeating glutamic acids (E)) was proposed. This study was driven by the possibility of enhancing sample discrimination by combining mutually characteristic and complimentary RGB signals obtained from each color sensor, which resulted from dissimilar interactions of sample odors with the employed color sensors. When each color sensor was used individually, the discrimination accuracy based on support vector machine (SVM) ranged from 91.8–94.0%, 88.6–90.3%, and 89.8–92.1% for garlic, onion, and perilla samples, respectively. Accuracy improved to 98.0%, 97.5%, and 97.1%, respectively, by integrating all of the RGB signals acquired from the three color sensors. Therefore, the proposed strategy was effective for improving sample discriminability. To further examine the dissimilar responses of each color sensor to odor molecules, typical odor components in the samples (allyl disulfide, allyl methyl disulfide, and perillaldehyde) were measured using each color sensor, and differences in RGB signals were analyzed.

The recent PlanetScope constellation (130+ satellites currently in orbit) has shifted the high spatial resolution imaging into a new era by capturing the Earth’s landmass including inland waters on a daily basis. However, studies on the aquatic-oriented applications of PlanetScope imagery are very sparse, and extensive research is still required to unlock the potentials of this new source of data. As a first fully physics-based investigation, we aim to assess the feasibility of retrieving bathymetric and water quality information from the PlanetScope imagery. The analyses are performed based on Water Color Simulator (WASI) processor in the context of a multitemporal analysis. The WASI-based radiative transfer inversion is adapted to process the PlanetScope imagery dealing with the low spectral resolution and atmospheric artifacts. The bathymetry and total suspended matter (TSM) are mapped in the relatively complex environment of Venice lagoon during two benchmark events: The coronavirus disease 2019 (COVID-19) lockdown and an extreme flood occurred in November 2019. The retrievals of TSM imply a remarkable reduction of the turbidity during the lockdown, due to the COVID-19 pandemic and capture the high values of TSM during the flood condition. The results suggest that sizable atmospheric and sun-glint artifacts should be mitigated through the physics-based inversion using the surface reflectance products of PlanetScope imagery. The physics-based inversion demonstrated high potentials in retrieving both bathymetry and TSM using the PlanetScope imagery.

This tutorial provides a selective review of research on object-based deployment of attention. It focuses primarily on behavioral studies with human observers. The tutorial is divided into five sections. It starts with an introduction to object-based attention and a description of the three commonly used experimental paradigms in object-based attention research. These are followed by a review of a variety of manifestations of object effects and the factors that influence object segmentation. The final two sections are devoted to two key issues in object-based research: the mechanisms that give rise to the object effects and the role of space in object-based selection.