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Medical students highly associated with alcohol abuse

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New Delhi: A team of US researchers has found that medical students, especially who are young, single and under high debt are twice as likely to abuse alcohol than their peers who are not attending medical school.

Burnout factors such as emotional exhaustion or feelings of depersonalization were highly associated with alcohol abuse or dependence among the medical students.

“Our findings clearly show there is reason for concern,” said Liselotte Dyrbye from Mayo Clinic in the US.

“We recommend institutions pursue a multifaceted solution to address related issues with burnout, the cost of medical education and alcohol abuse,” Dyrbye added in the paper published in the journal Academic Medicine.

The researchers surveyed 12,500 medical students and one-third of those responded. Approximately 1,400 of that subgroup experienced clinical alcohol abuse or dependence.

The results indicate three factors that were independently associated — a younger age than most peers in medical school, being unmarried and amount of educational debt.

No statistical difference was found between differing years of medical school or between men and women.

“In our paper we recommend wellness curricula for medical schools, identifying and remediating factors within the learning environment contributing to stress and removal of barriers to mental health services,” added first author Eric Jackson.(IANS)

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Google AI can focus on individual speakers in a crowd

The visual signal not only improves the speech separation quality significantly in cases of mixed speech, but, importantly, it also associates the separated, clean speech tracks

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Google india launches 'Tz' to help people pay their utility bills. Wikimedia Commons
Google AI to identify speakers from crowd. Wikimedia Commons

Just as most smartphone cameras now allow users to focus on a single object among many, it may soon be possible to pick out individual voices in a crowd by suppressing all other sounds, thanks to a new Artificial Intelligence (AI) system developed by Google researchers.

This is an important development as computers as not as good as humans at focusing their attention on a particular person in a noisy environment. Known as the cocktail party effect, the capability to mentally “mute” all other voices and sounds comes natural to us humans.

Google has collaborated with getty images. Wikimedia Commons
Google AI will identify individual speakers now. Wikimedia Commons

However, automatic speech separation — separating an audio signal into its individual speech sources — remains a significant challenge for computers, Inbar Mosseri and Oran Lang, software engineers at Google Research, wrote in a blog post this week. In a new paper, the researchers presented a deep learning audio-visual model for isolating a single speech signal from a mixture of sounds such as other voices and background noise.

“In this work, we are able to computationally produce videos in which speech of specific people is enhanced while all other sounds are suppressed,” Mosseri and Lang said. The method works on ordinary videos with a single audio track, and all that is required from the user is to select the face of the person in the video they want to hear, or to have such a person be selected algorithmically based on context.

Also Read: Want To Know What Facebook, Google Know About You?

The researchers believe this capability can have a wide range of applications, from speech enhancement and recognition in videos, through video conferencing, to improved hearing aids, especially in situations where there are multiple people speaking. “A unique aspect of our technique is in combining both the auditory and visual signals of an input video to separate the speech,” the researchers said.

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This will also help in speech enhancement . VOA

“Intuitively, movements of a person’s mouth, for example, should correlate with the sounds produced as that person is speaking, which in turn can help identify which parts of the audio correspond to that person,” they explained.

The visual signal not only improves the speech separation quality significantly in cases of mixed speech, but, importantly, it also associates the separated, clean speech tracks with the visible speakers in the video, the researchers said. IANS

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