银魂

Chinese scientists develop ‘brain-reading’ AI model to help predict depression risk, may inspire future emotional-perception humanoids_我的网站

A |     두산에너빌, 비상대책본부 구성…남동발전 “매뉴얼 따라 조치”                   26일 네팔 누와코트(Nuwakot) 지구 트리슐리 바자르(Trishuli Bazar)에서 갑작스러운 홍수가 발생한 후, 강변을 따라 주택들이 진흙탕 물에 일부 잠겨 있다. 같은 날 네팔 북부 라수와(Rasuwa) 지구를 휩쓴 대규모 홍수로 도로와 전력 시설이 파손되었으며, 당국은 피해 규모 파악에 나섰다. AFP 연합뉴스네팔 홍수로 현지에서 수력발전소를 건설하던 한국인 근로자들이 연락이 두절되거나 고립되면서 해당 기업들도 대책 마련에 분주하다. 두산에너빌리티는 26일 오후 비상대책본부를 구성해 신속한 상황 파악과 사고 수습에 나섰다. 정연인 대표이사를 포함한 현장대응팀도 27일 현지에 급파할 예정이다. 두산에너빌리티가 공사 중인 네팔 어퍼트리슐리-1 수력발전소 건설 현장의 한국인 직원은 총 20명으로 이중 재해 당시 현장에 있던 15명 중 5명이 현재 연락이 되지 않고 있다. 두산에너빌리티 측은 “사태 발생 후 연락이 두절된 직원의 가족에게도 신속히 연락해 상황을 설명했다”며 “관계 당국 및 사업 관계자들과 긴밀히 협력해 실종자 수색과 구조를 지원하는 데 최선의 노력을 기울이겠다”고 말했다. 어퍼트리슐리-1 수력발전소는 네팔 수도 카트만두에서 북쪽 70km에 위치한 트리슐리 강에 216메가와트(MW) 규모로 건설 중이다. 두산에너빌리티는 발전소 건설과 터빈, 발전기 등 주요 기자재 제작·공급을 담당하고 있다. 직원 3명과 연락이 두절된 남동발전 역시 긴급 대응에 나섰다. 남동발전에 따르면 현지 파견 직원 총 7명 중 수도 카트만두 현지 법인 인원을 제외한 현장 건설 관리 인력 3명과 연락이 닿지 않고 있다. 남동발전 측은 “해외 사업 담당 부서를 중심으로 지속적인 연락 시도와 함께 매뉴얼에 따라 필요한 조치를 취하고 있다”고 밝혔다.。

B |     

Brain-reading AI model reveals how different brain regions are linked to cognitive functions. Photo: Courtesy of Lu Han
Chinese scientists have developed a “brain-reading” AI model that could help predict the risk of depression among adolescents up to four years in advance by analyzing how humans respond to facial expressions, a technology expected to inspire future development of embodied intelligent humanoids capable of perceiving human emotion and thoughts through nuanced facial cues. 
WHO data show that around 332 million people worldwide have depression, about one-third of whom have treatment-resistant forms of the condition. In China, an estimated 95 million people suffer from depression, National Business Daily reported, citing statistics from the China Mental Health Survey. 
Using data from a population-based longitudinal adolescent cohort recruited across several European countries, the research team led by Lu Han, assistant professor at the School of Artificial Intelligence, Shenzhen University, has built an AI model that predicted which 19-year-olds were more likely to develop depression at the age of 23. The predictions were backed up by an independent clinical cohort of individuals with depression. The team’s paper was published in the journal Science Advances this month.
According to Lu, the study used brain scans taken at age 19 to predict depression-related symptoms at age 23. The study focuses on adolescence because the transition from adolescence to early adulthood is a key developmental period when depressive symptoms can increase rapidly. The earlier risks are identified, the greater the opportunity for prevention, Lu told the Global Times on Monday, adding that the findings need to be further validated in middle-aged and older adults and across different ethnic groups in future research. 
In this study, the researchers analyzed data from adolescents in the IMAGEN, a population-based longitudinal cohort recruited across several European countries. At age 19, participants underwent an fMRI emotional-face task, and their emotional symptoms were assessed using standardized questionnaires. Genetic data obtained from blood samples were also analyzed, and participants were followed up at age 23. The researchers examined whether neural representations of angry faces at age 19 were associated with emotional symptoms and could predict elevated emotional symptoms four years later.
According to Lu, people without depression can more easily distinguish emotional changes based on others’ facial expressions and respond accordingly – for example, responding with friendliness to a smiling expression. But people with depression cannot do this, and are more likely to assume people are angry with them. 
A brain-aligned deep-learning model developed by Lu’s team suggested that those participants whose brains were less able to distinguish between different facial emotions and tended to perceive others as angry were more likely to develop symptoms of depression and anxiety in adulthood. 
The hypothesis that adolescents at risk of depression may respond differently to other people’s facial expressions than those without such risk based on the negative information processing bias long observed in depression research: people at risk of depression are more likely to notice, interpret, or remember negative social information, Lu said. 
The researchers focused on angry facial expressions because they signal social threat and rejection, which are closely linked to interpersonal difficulties and negativity bias associated with depression. They hope to further understand how this bias develops within the visual system. 
Building on this, they created a deep learning model, which mimics how the brain processes visual information, to predict how the brain encodes abstract emotional concepts such as anger.
They found that 19-year-olds whose response to facial expressions was skewed in favour of negative emotions or memories were the most likely to develop some form of depression.
Based on these findings, Lu’s team then developed a marker that can identify possible warning signs. 
According to Lu, the study found that the computational biomarker was linked to the depression-related variant rs11123030 and polygenic risk for depression, suggesting that genetic susceptibility may affect emotional perception. It also provided predictive information beyond family stress and socioeconomic factors, complementing rather than replacing environmental risk factors. Therefore, depression is neither purely genetic nor purely psychological, but a complex mental disorder arising from the interplay of genetic susceptibility, brain development, emotional and cognitive processes, and life experiences. 
According to Lu, the study is also expected to advance AI by aligning deep neural networks with human brain activity and using parameter perturbations to probe neural mechanisms, allowing models to both predict and explain how biases may arise. 
The findings suggest that future affective computing and embodied AI should go beyond simply labeling facial expressions, incorporating visual details while preventing prior assumptions from overriding real-time sensory input, Lu said, adding that the findings could provide valuable insights for developing more interpretable robotic perception systems that more closely emulate the way humans process emotions.
。

Current article:http://www.zhouangnaoguangsundianzhenmou.cfd/aakf/difk2.doc

Published on:02:24:48