Citizens' Issues
HC asks govt to ensure treatment to rail mishap victims in private hospitals

Rail accident victims must be provided prompt medical treatment in private hospitals within first hour of the mishap, without waiting for the police formalities, the Bombay HC said in a landmark order

 

In an important and landmark order, especially for daily train commuters, the Bombay High Court has asked Maharashtra Government to ensure that rail accident victims are provided prompt medical treatment in private hospitals without waiting for the police formalities.
 
Advocate Jamshed Mistry, who is amicus curiae (friend of the court) in the case filed by railway activist Samir Zaveri, said, "It's a historic order. For the first time a victim of an accident will actually receive treatment within the Golden Hour."
 
A Division Bench of Justice VM Kanade and Justice Revati Dere, also asked the Railways to set up a research team for finding a solution to reduce overcrowding in trains and prevent track deaths. 
 
Zaveri, who has filed the petition, has highlighted the plights of rail accident victims and told the Court how these people are deprived treatment during the first hour from mishap (golden hour) citing procedural delay, rules and regulations.
 
Advocate Mistry also informed the Bench about how rail accident victims are carried to government hospitals miles away instead of admitting and providing medical treatment in nearby private hospital. The Bench said, "The state has to look into the issue and ensure that all private hospitals give prompt medical aid to the injured and treat them without waiting for police formalities to be completed."
 
The HC also directed the Railways to ensure safety measures for commuters and also availability of emergency first aid kits and ambulances at all stations across the Mumbai Suburban Rail System (MSRS). 
 
Referring to statistics about deaths on railway tracks, the HC Bench also asked the Railways to increase number and frequencies of services, modify seating arrangements and introduce double-decker trains. 
 
"Technology and modern trains can go a long way in solving the problem of citizens losing their precious lives on the suburban railway lines," Adv Mistry said.
 
Earlier in July, Moneylife Foundation's Safe Rail Travel Group has conducted a Social Audit of 30 stations across the Mumbai Suburban Network. The Social Audit also recommended having double decker trains. "Double-Decker trains could be a good idea considering the large volume of people that the MSRS transports daily. Despite having a much lower number of people, all Australian trains are of this configuration," the report prepared by Australian students, Hong Leanne Truong and Ricky Vella from University of Western Sydney has said.

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Here's how human brain can handle so much data
Researchers led by an Indian-origin scientist from Georgia Institute of Technology have discovered how humans can categorise data using less than one percent of the original information.
 
They validated an algorithm to explain human learning -- a method that also can be used for machine learning, data analysis and computer vision.
 
“How do we make sense of so much data around us, of so many different types, so quickly and robustly?" said Santosh Vempala, distinguished professor of computer science.
 
“At a fundamental level, how do humans begin to do that? It's a computational problem,” he asked.
 
Vempala and colleagues presented test subjects with original, abstract images and then asked whether they could correctly identify that same image when randomly shown just a small portion of it.
 
“We hypothesised that random projection could be one way humans learn," said Rosa Arriaga, senior research scientist and developmental psychologist
 
“The prediction was right. Just 0.15 percent of the total data is enough for humans,” she added.
 
Next, researchers tested a computational algorithm to allow machines to complete the same tests.
 
Machines performed as well as humans, which provides a new understanding of how humans learn.
 
“We found evidence that, in fact, the human and the machine's neural network behave very similarly," Arriaga noted.
 
It is believed to be the first study of “random projection,” the core component of the researchers' theory, with human subjects.
 
“We were surprised by how close the performance was between extremely simple neural networks and humans," Vempala said.
 
“This fascinating paper introduces a localised random projection that compresses images while still making it possible for humans and machines to distinguish broad categories,” explained Sanjoy Dasgupta, professor of computer science and engineering at the University of California-San Diego.
 
The results were published in the journal Neural Computation (MIT press).
 
Disclaimer: Information, facts or opinions expressed in this news article are presented as sourced from IANS and do not reflect views of Moneylife and hence Moneylife is not responsible or liable for the same. As a source and news provider, IANS is responsible for accuracy, completeness, suitability and validity of any information in this article.

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When it comes to privacy, people are info-egoists!
Do you value your friends' private information on social media as much as your own? Most likely not, says a new study, suggesting that when it comes to their privacy, people are info-egoists.
 
People are much more concerned about sharing their own private information with third-party app developers than they are about revealing their friends' data, the study said.
 
However, as social media makes data increasingly interconnected, preserving one's own privacy while ignoring the privacy rights of others may make everybody's data more vulnerable, said Jens Grossklags, assistant professor of information sciences and technology at Pennsylvania State University in the US.
 
"The problem is becoming known as interdependent privacy," Grossklags said. 
 
"The privacy of individual consumers does not only depend on their own decisions, but is also affected by the actions of others," Grossklags pointed out.
 
In the study, the researchers measured the economic value of personal information which individuals place on their own and other's information. 
 
The participants valued the data in their own social media profiles at $2.31 and their friends' data at $1.56 when friends' data was irrelevant to a third party app's function. 
 
When friends' data was necessary for app function, the participants valued their own data at $2.04 and their friends' data at just 98 cents.
 
The researchers estimated that the average Facebook user, for example, with an average of more than 300 friends, would value the bundle of friends' data at less than a cent per friend when data collection is necessary. 
 
When data collection is unnecessary, people value the information for a single friend at less than three cents.
 
The researchers collected data from about 400 users of Mechanical Turk, a crowdsourced marketplace that allows members to earn money for completing various tasks. 
 
The findings were presented at the International Conference on Information Systems in Fort Worth, Texas, US.
 
Disclaimer: Information, facts or opinions expressed in this news article are presented as sourced from IANS and do not reflect views of Moneylife and hence Moneylife is not responsible or liable for the same. As a source and news provider, IANS is responsible for accuracy, completeness, suitability and validity of any information in this article.

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