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“이번 주 결혼인데 식장 철거라뇨?” 봉변당한 예비부부들_我的网站

辛普森一家

A | “예식장 하나의 문제 아냐 금전적 손해는 물론, 정신적 충격도 상당”[이데일리 남소연 기자] 갑작스러운 예식장 철거로 해당 식장에서 결혼식을 올릴 예정이던 예비부부들이 발을 동동 구르고 있다. 피해자 가운데는 당장 이번 주 결혼식을 앞둔 예비부부도 있는 것으로 전해진다.

(사진=뉴시스)

서강대학교 곤자가컨벤션 측은 지난 23일 결혼식을 예약한 예비부부들에게 법원의 강제집행으로 예식장 운영이 어렵다는 사실을 알렸다. 이후 실제 강제집행이 진행되면서, 예식장 내부 장식과 버진로드 등 주요 시설물이 철거됐다.이번 강제집행은 예식장 운영업체와 건물 관리업체 간의 장기 분쟁에서 비롯됐다. 코로나19 당시 예식장 운영이 정상적으로 이뤄지지 못하면서 적자가 누적됐고 그 여파로 운영업체가 임대료를 제때 내지 못하는 일이 반복되자, 건물 관리업체 측이 강제집행 절차에 나선 것이다.피해자들은 강제집행 가능성이 예견된 상황에서도 운영업체가 예약을 받아온 게 아니냐는 의혹을 제기하고 있다. 피해자 중 한 명은 건물 관리업체로부터 ‘지난해 12월부터 예약받지 말라고 고지했다’는 설명을 들었다며, “운영업체가 이를 숨기고 계속 신규 계약을 진행한 것”이라고 연합뉴스에 전했다. 다만, 운영업체 측은 “집행 중지 신청과 소송을 진행하며 강제집행을 막으려 노력해왔고, 실제로 강제집행 조치가 이뤄지지 않고 집행하겠다는 협박만 있어 영업을 할 수밖에 없었다”는 입장인 것으로 알려졌다. 피해자들은 오픈 채팅방을 만들어 단체 대응에 나선 상태다. 당장 이번 사태로 피해를 본 예비부부들을 모아 한국소비자원 집단분쟁조정 신청을 준비하고 있다. 운영업체 측은 피해자 측에 예약금 환불 의사를 밝혔지만, 피해자들은 예약금 환불만으로는 실제 피해를 보전하기 어렵다고 호소하고 있다. 피해자들은 “결혼식은 예식장 하나의 문제가 아니다”라며 “이미 확정된 날짜를 기준으로 본식 스냅 ·영상 촬영 계약금, 웨딩 메이크업·헤어 샵 예약 및 계약금, 하객 초청과 청첩장 인쇄·발송, 혼주 일정 조율까지 수많은 계약과 일정이 전부 얽혀 있다”고 토로했다. 이어 “특히 이번 주부터 예식을 앞두고 있던 예비부부들은 사실상 대체할 시간조차 없이 위 계약들을 그대로 날리거나 급하게 몇 배의 비용을 더 들여서라도 새 장소를 구해야 하는 상황에 내몰렸다”며 “금전적 손실은 물론, 인생에서 가장 중요한 날 중 하나를 준비하며 겪는 정신적 충격도 상당하다”고 호소했다.。 Brain-reading AI model reveals how different brain regions are linked to cognitive functions. Photo: Courtesy of Lu Han

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.
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