新埔捷運站1號出口 旁邊7-11巷子進入20公尺 看到夏朵美髮左轉
怎知她喜歡你?
How to Know if a Girl Likes You
Co-authored by Elvina Lui
She glances your way, laughs at your jokes, and acts nervously around you. You’re not sure if she’s flirting, being friendly, or is simply uninterested. Whether you’ve had a crush on a girl for ages and are dying to know if the feeling is mutual or you just want to know if she likes you for curiosity’s sake, there are a few nearly foolproof ways to tell whether a girl likes you or not.
Understanding Body Language Cues
Look at her stance. When a girl likes you, she will face in your direction. If a girl has her torso turned towards you in an open manner, this means that she is confident talking with you. If she has a closed body position, namely crossed arms or legs, she may be shy or nervous to talk to you or she may simply be creating a barrier to signal that she is uninterested.[1]
When she is sitting with her legs crossed, watch her feet. If they are pointed towards you, it might mean that she likes you and wants to get closer to you.
Open stances indicate relaxation and comfort. Couples counselor and MFT, Elvina Lui, tells us: "Generally speaking a girl will stand closer to you and will have more relaxed body language if she feels comfortable around you — for example, she'll have relaxed shoulders and she won't cross her arms, But, everyone's personality and cultural upbringing is different, so don't take it as a sign she's not into you if this isn't the case."
Pay attention to eye contact. If a girl likes you, she will tend to either hold her gaze on you for a few seconds or glance down the moment your eyes make contact with hers. Either of these responses could mean that she likes you. If she pulls away quickly, it often means she is nervous or not ready to reveal her true intentions yet, but she may still like you.
When a girl likes you, her pupils might dilate, though this will be hard to tell.
If you happen to glance at the girl and you see her staring back at you, this could mean that she likes you.
Notice if she touches you or tries to get closer. When a girl likes you, she will often try to touch you, as this is a noticeable yet still subtle way to flirt. It allows a girl to size up how responsive you are. She may touch your arm when you say something funny, “accidentally” brush your shoulder or hands with hers, or gently place her hand on your knee.
Not all girls will feel comfortable reaching out using touch. In this case, don't assume that she doesn't like you just because she doesn't try to touch you. She may be too nervous to do so. If you like her, don't be shy––break the touch barrier yourself and see how she responds.
She may also find other reasons to touch you, such as softly punching your arm. These "one-of-the-mates" moves can be a thinly disguised way of getting closer to you without it being too evident to your friends and hers.
Pay attention to whether she randomly hugs you.
See if she mirrors your moves. If a girl imitates you - for example, if you run your fingers through your hair and you notice her do the same a few seconds later - she may be subconsciously mirroring your movements. This can be a tell that she likes you.
Notice if she’s playing with her hair. Gently twirling strands of her hair or partaking in other grooming behaviors like running her hands through her hair could be signs of flirting.
貓能預測死亡!
The Cat Who Could Predict Death
Taylor Grote/Unsplash
Of the many small humiliations heaped on a young oncologist in his final year of fellowship, perhaps this one carried the oddest bite: A 2-year-old black-and-white cat named Oscar was apparently better than most doctors at predicting when a terminally ill patient was about to die. The story appeared, astonishingly, in The New England Journal of Medicine in the summer of 2007. Adopted as a kitten by the medical staff, Oscar reigned over one floor of the Steere House nursing home in Rhode Island. When the cat would sniff the air, crane his neck and curl up next to a man or woman, it was a sure sign of impending demise. The doctors would call the families to come in for their last visit. Over the course of several years, the cat had curled up next to 50 patients. Every one of them died shortly thereafter.
No one knows how the cat acquired his formidable death-sniffing skills. Perhaps Oscar’s nose learned to detect some unique whiff of death — chemicals released by dying cells, say. Perhaps there were other inscrutable signs. I didn’t quite believe it at first, but Oscar’s acumen was corroborated by other physicians who witnessed the prophetic cat in action. As the author of the article wrote: “No one dies on the third floor unless Oscar pays a visit and stays awhile.”
The story carried a particular resonance for me that summer, for I had been treating S., a 32-year-old plumber with esophageal cancer. He had responded well to chemotherapy and radiation, and we had surgically resected his esophagus, leaving no detectable trace of malignancy in his body. One afternoon, a few weeks after his treatment had been completed, I cautiously broached the topic of end-of-life care. We were going for a cure, of course, I told S., but there was always the small possibility of a relapse. He had a young wife and two children, and a mother who had brought him weekly to the chemo suite. Perhaps, I suggested, he might have a frank conversation with his family about his goals?
But S. demurred. He was regaining strength week by week. The conversation was bound to be “a bummah,” as he put it in his distinct Boston accent. His spirits were up. The cancer was out. Why rain on his celebration? I agreed reluctantly; it was unlikely that the cancer would return.
When the relapse appeared, it was a full-on deluge. Two months after he left the hospital, S. returned to see me with sprays of metastasis in his liver, his lungs and, unusually, in his bones. The pain from these lesions was so terrifying that only the highest doses of painkilling drugs would treat it, and S. spent the last weeks of his life in a state bordering on coma, unable to register the presence of his family around his bed. His mother pleaded with me at first to give him more chemo, then accused me of misleading the family about S.’s prognosis. I held my tongue in shame: Doctors, I knew, have an abysmal track record of predicting which of our patients are going to die. Death is our ultimate black box.
In a survey led by researchers at University College London of over 12,000 prognoses of the life span of terminally ill patients, the hits and misses were wide-ranging. Some doctors predicted deaths accurately. Others underestimated death by nearly three months; yet others overestimated it by an equal magnitude. Even within oncology, there were subcultures of the worst offenders: In one story, likely apocryphal, a leukemia doctor was found instilling chemotherapy into the veins of a man whose I.C.U. monitor said that his heart had long since stopped.
But what if an algorithm could predict death? In late 2016 a graduate student named Anand Avati at Stanford’s computer-science department, along with a small team from the medical school, tried to “teach” an algorithm to identify patients who were very likely to die within a defined time window. “The palliative-care team at the hospital had a challenge,” Avati told me. “How could we find patients who are within three to 12 months of dying?” This window was “the sweet spot of palliative care.” A lead time longer than 12 months can strain limited resources unnecessarily, providing too much, too soon; in contrast, if death came less than three months after the prediction, there would be no real preparatory time for dying — too little, too late. Identifying patients in the narrow, optimal time period, Avati knew, would allow doctors to use medical interventions more appropriately and more humanely. And if the algorithm worked, palliative-care teams would be relieved from having to manually scour charts, hunting for those most likely to benefit.
Avati and his team identified about 200,000 patients who could be studied. The patients had all sorts of illnesses — cancer, neurological diseases, heart and kidney failure. The team’s key insight was to use the hospital’s medical records as a proxy time machine. Say a man died in January 2017. What if you scrolled time back to the “sweet spot of palliative care” — the window between January and October 2016 when care would have been most effective? But to find that spot for a given patient, Avati knew, you’d presumably need to collect and analyze medical information before that window. Could you gather information about this man during this pre-window period that would enable a doctor to predict a demise in that three-to-12-month section of time? And what kinds of inputs might teach such an algorithm to make predictions?
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