There Are Two Types of Narcissist, and the Difference Is Crucial

In a time when flaunting your best self on social media has become a norm, narcissistic traits seem to be everywhere. In today’s slang, off-putting behaviors like entitlement, superiority, and self-congratulating are known as ‘flexing‘. Such traits might be more common these days, but being narcissistic is still seen as a pathological personality trait, akin to being sadistic, manipulative, or even psychopathic. However, a 2021 study of 270 people with a median age of 20 lends more credit to the notion that narcissistic behaviors are not always driven by the same things as psychopathy.

“For a long time, it was unclear why narcissists engage in unpleasant behaviors, such as self-congratulation, as it actually makes others think less of them. Our work reveals that these narcissists are not grandiose, but rather insecure,” said clinical psychologist Pascal Wallisch from New York University (NYU). “More specifically, the results suggest that narcissism is better understood as a compensatory adaptation to overcome and cover up low self-worth,” added clinical psychologist Mary Kowalchyk, also from NYU.

Psychologists do already distinguish between two rather different types of narcissists: ‘vulnerable narcissists‘ who have low self-esteem, attachment anxiety, and are highly sensitive to criticism; and ‘grandiose narcissists‘, who have high self-esteem and self-aggrandizement. This latest research helps to further disentangle the two. Kowalchyk and team used a series of measures to assess the levels of different traits including narcissism, self-esteem, and psychopathy for each of their participants, and found that flexing behavior is strongly associated with individuals who also have high insecurities and sense of guilt. Those exhibiting psychopathy showed relatively low levels of guilt.

Source: https://www.sciencealert.com/

AI Classify Chest X-Rays With Human-Level Accuracy

Analyzing chest X-ray images with machine learning algorithms is easier said than done. That’s because typically, the clinical labels required to train those algorithms are obtained with rule-based natural language processing or human annotation, both of which tend to introduce inconsistencies and errors. Additionally, it’s challenging to assemble data sets that represent an adequately diverse spectrum of cases, and to establish clinically meaningful and consistent labels given only images.

In an effort to move forward the goalpost with respect to X-ray image classification, researchers at Google devised AI models to spot four findings on human chest X-rays: pneumothorax (collapsed lungs), nodules and masses, fractures, and airspace opacities (filling of the pulmonary tree with material). In a paper published in the journal Nature, the team claims the model family, which was evaluated using thousands of images across data sets with high-quality labels, demonstrated “radiologist-levelperformance in an independent review conducted by human experts.

The study’s publication comes months after Google AI and Northwestern Medicine scientists created a model capable of detecting lung cancer from screening tests better than human radiologists with an average of eight years experience, and roughly a year after New York University used Google’s Inception v3 machine learning model to detect lung cancer. AI also underpins the tech giant’s advances in diabetic retinopathy diagnosis through eye scans, as well as Alphabet subsidiary DeepMind’s AI that can recommend the proper line of treatment for 50 eye diseases with 94% accuracy.

This newer work tapped over 600,000 images sourced from two de-identified data sets, the first of which was developed in collaboration with Apollo Hospitals and which consists of X-rays collected over years from multiple locations. As for the second corpus, it’s the publicly available ChestX-ray14 image set released by the National Institutes of Health, which has historically served as a resource for AI efforts but which suffers shortcomings in accuracy.

The researchers developed a text-based system to extract labels using radiology reports associated with each X-ray, which they then applied to provide labels for over 560,000 images from the Apollo Hospitals data set. To reduce errors introduced by the text-based label extraction and provide the relevant labels for a number of ChestX-ray14 images, they recruited radiologists to review approximately 37,000 images across the two corpora.

Google notes that while the models achieved expert-level accuracy overall, performance varied across corpora. For example, the sensitivity for detecting pneumothorax among radiologists was approximately 79% for the ChestX-ray14 images, but was only 52% for the same radiologists on the other data set.

Chest X-ray depicting a pneumothorax identified by Google’s AI model and the panel of radiologists, but missed by individual radiologists. On the left is the original image, and on the right is the same image with the most important regions for the model prediction highlighted in orange

The performance differences between datasets … emphasize the need for standardized evaluation image sets with accurate reference standards in order to allow comparison across studies,” wrote Google research scientist Dr. David Steiner and Google Health technical lead Shravya Shetty in a blog post, both of whom contributed to the paper. “[Models] often identified findings that were consistently missed by radiologists, and vice versa. As such, strategies that combine the unique ‘skills’ of both the [AI] systems and human experts are likely to hold the most promise for realizing the potential of AI applications in medical image interpretation.”

The research team hopes to lay the groundwork for superior methods with a corpus of the adjudicated labels for the ChestX-ray14 data set, which they’ve made available in open source. It contains 2,412 training and validation set images and 1,962 test set images, or 4,374 images in total.

We hope that these labels will facilitate future machine learning efforts and enable better apples-to-apples comparisons between machine learning models for chest X-ray interpretation,” wrote Steiner and Shetty.  

Source: https://venturebeat.com/

Mass Production of Low-Cost Solar Cells

An international team of university researchers today reports solving a major fabrication challenge for perovskite cells — the intriguing potential challengers to silicon-based solar cells.

These crystalline structures show great promise because they can absorb almost all wavelengths of light. Perovskite solar cells are already commercialized on a small scale, but recent vast improvements in their power conversion efficiency (PCE) are driving interest in using them as low-cost alternatives for solar panels.

In the cover article published online in Nanoscale, a publication of the Royal Society of Chemistry, the research team reveals a new scalable means of applying a critical component to perovskite cells to solve some major fabrication challenges. The researchers were able to apply the critical electron transport layer (ETL) in perovskite photovoltaic cells in a new way — spray coating — to imbue the ETL with superior conductivity and a strong interface with its neighbor, the perovskite layer.

The researchers turned to spray coating, which applies the ETL uniformly across a large area and is suitable for manufacturing large solar panels. They reported a 30 percent efficiency gain over other ETLs – from a PCE of 13 percent to over 17 percent – and fewer defects.

Added Taylor, “Our approach is concise, highly reproducible, and scalable. It suggests that spray coating the PCBM ETL could have broad appeal toward improving the efficiency baseline of perovskite solar cells and providing an ideal platform for record-breaking p-i-n perovskite solar cells in the near future.”

The research is led by André D. Taylor, an associate professor in the NYU Tandon School of Engineering’s Chemical and Biomolecular Engineering Department, with Yifan Zheng, the first author on the paper and a Peking University researcher. Co-authors are from the University of Electronic Science and Technology of China, Yale University, and Johns Hopkins University.

Source: https://engineering.nyu.edu/