2019 movies to geek out over Now playing: Watch this: TV and Movies Us tells the story of Adelaide Wilson, a wife and mother whose traumatic childhood experience casts a shadow over a summer beach trip with her family. Trauma haunts survivors for years to come like a shadow they can’t shake, and Us puts the members of the Wilson family face-to-face with their own shadows: doppelgangers in red jumpsuits wielding glinting gold scissors. They’re called The Tethered, and they’ve got a score to settle with their better look-alikes.You could say Us has been hotly anticipated. It follows 2017’s Get Out, a tightly-made, critically acclaimed thriller with a social message that earned Peele an Oscar for Best Original Screenplay. It helped set him up as a bright light in the realm of thrills and suspense. In fact, he’s pretty busy these days with projects like the reboot of the Twilight Zone, for which he’s the executive producer and host, and Weird City, a YouTube Original dystopian anthology series. Lupita Nyong’o takes a good long look at herself in Us. Claudette Barius So it’s hard not to make comparisons to Get Out. But where Get Out was a tidy concept, executed in a way that deliberately and carefully guided you through the story toward the big ideas, Us is bigger, broader and perhaps less neat. One of the inevitable questions people will ask about Us is whether it’s scarier than Get Out. Peele gets the dread machine going in the first scene at a beach-side amusement park, and the tensest moments include the Wilson family trying to figure out why people outside are trying to get in. And that old horror staple, the ominous Bible verse, will never not be creepy — in this case, Jeremiah 11:11. You can see the uncomprehending terror on Lupita Nyong’o’s face as she stares at her Tethered counterpart, tears streaming down her cheeks. SXSW 2019 Jordan Peele’s Us gets a new trailer and it’s terrifying 77 Photos 2 Mar 15 • LG ‘Snow White’ makes ice cream from capsules Mar 19 • AOC, Bill Nye and the apocalypse: The insanity of SXSW 2019 1:00 SXSW 2019 Mar 15 • Men can now breastfeed reading • Us review: Jordan Peele’s horror flick holds up a dark mirror to Get Out See All Share your voice But while Us shoots for a next level of frightening, it runs into a few weak spots along the way.For one, we learn the Tethered aren’t just knocking on the Wilsons’ door. But that larger plot is pretty vague.The effectiveness of the movie’s humor is also tough to gauge. The audience at SXSW was extremely game, laughing uproariously, even in places where it wasn’t entirely clear whether there was a joke to be had — like when the Tethered first show up in the Wilsons’ driveway, and Gabe Wilson (Winston Duke), who’d been dadding it up hard (in the best way) since the start of the movie, goes outside with a bat and some adopted bravado to try and get these mysterious figures to leave. The audience howled.In a horror film a well-placed quip can cut the tension to not only temporarily relieve the audience but also enable the tension to build back up again. Stick it in the wrong place and the laughs just undercut everything around it.Throughout the film, we’re shown lots of mirrors and reflections, visually reflecting the theme. After all, the Wilsons are seeing themselves like they never have before, though warped.Similarly, Us mirrors Get Out. It’s more scary fare with social commentary. But the copy’s never quite the same as the original.Want another take on Us? See GameSpot’s review here. Comments Tags • It shouldn’t come as a shock that a horror film titled Us plays with the idea that perhaps we are the villains.The film’s writer, director and producer, Jordan Peele, addresses how we fear and hate those we consider to be other without examining ourselves. “Maybe the monster we need to look at has our face,” Peele told the audience at the film’s world premiere during SXSW 2019. “Maybe the evil is us.” Us opens March 22 in the US and UK. Looking in a mirror is one of the recurring motifs of Peele’s new horror flick, and the biggest metaphorical theme too. Movie reviews
The Bombay Stock Exchange in Mumbai on Feb 29, 2016 (representational image).IANSReliance Industries Ltd. (RIL) shares hit a new 52-week high of Rs 1,287 on the Bombay Stock Exchange (BSE) on Friday even as benchmark indices Sensex and Nifty were trading in the red. The RIL stock later shed gains and was trading at Rs 1,268 at around 11.40 am; brokerage CLSA has raised the target price for the stock to Rs 1,500 in a note, reported the Business Today.RIL share price has rallied sharply in the past three months, from Rs 994 on December 5, 2016 to a new high of Rs 1,287, marking a gain of 29.4 percent. The previous 52-week high was Rs 1,256.50 scaled on February 27, 2017.The 30-scrip BSE Sensex was down 84 points to 28,756 while the NSE Nifty was trading at 8,872, down 28 points. Top Sensex losers were HDFC, Asian Paints and ITC.In other corporate news, the Goods and Services Tax (GST) Council decided to peg the peak GST rate at 40 percent to brace for any increase in the rate after the proposed cess is removed. The move does not impact the four tax slabs — 5, 12, 18 and 28 percent.The necessary amendment will be made in the draft GST Bill when the legislation is debated by the Parliament during the second half of the Budget Session that commences on March 9.The trend in Asian stock markets was weak on Friday, reflecting on Indian bourses. “After a weak start, Asian markets were trading lower today as markets took a breather in the U.S. & Europe on Thursday. Post weakness in the Unites States, the Japanese markets today opened almost 0.10 percent lower. The weakness in global markets is very much likely to give domestic markets a lacklustre start as indicated by the SGX Nifty which is trading 0.33 percent down in red,” Geojit BNP Paribas said in a note.Shares of EPC company Larsen & Toubro (L&T) remained almost flat at Rs 1,464 in response to the Karnataka government scrapping the Rs 1,791-crore steel flyover ( from Basaveshwara Circle to Hebbal) project. The company had won the project last October and was awarded the letter of acceptance (LOA).On Thursday, foreign portfolio investors (FPIs) were net buyers of Indian equities worth Rs 123 crore, according to provisional data released by the National Stock Exchange (NSE).The 10-year G-Sec bond ended with a yield of 6.845 percent and the Indian rupee closd at 66.71 to the US dollar on Thursday.
Fiel photo of gold barsCustoms intelligence at Hazrat Shahjalal International Airport recovered gold bars and ornaments weighing around 406 grams from a passenger of Biman Bangladesh Airlines from Abu Dhabi on Saturday, reports UNB.The passenger is Md Saidur Rahman, hailing from Koshba of Brahmanbaria.Officials at the Customs Intelligence and Investigation Directorate (CIID) said tipped off, its officials started monitoring movement of Saidur Rahman just after he was landed by the flight of Biman at around 11:00 am.When he was about to cross the green channel of the airport’s customs, the CIID officials challenged him, said CIID director general Moinul Khan. Searching his body, they recovered three gold bars, which were kept hidden inside his underwear in a unique way, and several ornaments from his luggage. Total weight of the three gold bars and gold ornaments is 406 grams worth Tk 2 million Moinul said.
Share Friday, September 14, 2018HISD dissolves special ed panelLaura IsenseeHISD Board President Wanda Adams and Trustee Anne Sung listened to parents share their frustration and questions about special education in the district at a 2017 meeting.The Houston school board has disbanded a panel to advise them on special education, after board members overwhelmingly voted to adopt the group’s recommendations to improve special ed.Those are meant to improve access, services and oversight of special education.But the board dissolved the special ed ad hoc committee it created last year after a statewide crisis in special ed. Some parents pushed back. When a disaster hits, what are an employer’s responsibilities? Hurricane Florence is expected to shut down parts of the East Coast for days. It’s something people in the Houston area are too familiar with after Harvey last year.Aside from the impact on lives and property, natural disasters affect employment.To learn more about how employers are expected to deal with employees who are affected by a hurricane or other disaster, we spoke with Aaron Holt, an employment law attorney at law firm Cozen O’Connor in Houston. Emmett points to future fights over Rainy Day fundAndrew Schneider/Houston Public MediaHarris County Judge Ed Emmett delivering his 11th annual State of the County addressHarris County Judge Ed Emmett used his State of the County message to mark the progress toward recovering from Harvey. But he also hinted at political fights to come. Galveston plans for infrastructure upgradesGalveston City Council Screenshot The Galveston Capital Improvement Plan includes millions of dollars in projects that are expected to unfold over the next five years.Now that they’ve finished most of their Hurricane Ike recovery efforts, Galveston officials say they’ll focus on infrastructure improvements that couldn’t be funded through the federal government.Under a five-year capital improvement plan, Galveston will use $62 million from last year’s bond issue to fix deteriorating streets and drainage systems. Ted Cruz speaks to Houston MattersSenator Ted Cruz is running for re-election on a platform that includes advocating for energy deregulation, increased surveillance on the Southern border and replacing the Affordable Care Act (ACA), commonly known as Obamacare.Cruz, a Republican from Texas who is running against Democratic Congressman Beto O’Rourke, was interviewed Thursday on Houston Matters, as part of its series of interviews with candidates running in major political races. Woman to be deported for voting illegallyA Mexican national whose attorney said she used a cousin’s identity to live in the U.S. for decades and held a job assessing students in Houston schools is facing deportation after pleading guilty to illegally voting in the 2016 election.Laura Janeth Garza’s conviction Thursday comes more than a year after Republican Texas Attorney General Ken Paxton’s office helped prosecute another Mexican national also facing deportation for illegal voting.
Researchers at MIT have come up with an intriguing approach to combat ‘Glioblastoma’- a malignant tumor of the brain/spinal cord- using machine learning techniques. By reducing the toxic chemotherapy and radiotherapy that is involved in treating this cancer, the researchers aim to improve the quality of life for patients, while also reducing the various side effects caused by the former using Reinforcement learning techniques. While the prognosis for adults is no more than 5 years, medical professionals try to shrink the tumor by administering drug doses in safe amounts. However, the pharmaceuticals are so strong that patients end up suffering from their side effects. Enter Machine Learning and Artificial Intelligence to save the day. While it’s no hidden truth that machine learning is being incorporated into healthcare on a huge scale, the MIT researchers have taken this to the next level. Using Reinforcement Learning as the Big Idea to train the model Media Lab researcher Gregory Yauney will be presenting a paper next week at the 2018 Machine Learning for Healthcare conference at Stanford University. This paper details how the MIT Media Lab researchers have come up with a model that could make dosing cycles less toxic but still effective. Incorporating a “self-learning” machine-learning technique, the model studies treatment regimens being used presently, and iteratively changes the measurements. In the end, it finds an ideal treatment design suited to the patient. This has proven to reduce the tumor sizes to a degree almost identical to that of original medical regimens. The model simulated trials of 50 patients and designed treatments that either reduced dosages to twice a year or skipped them all together. This was done keeping in mind that the model has to shrink the size of the tumor but at the same time ensuring that reduced dosages did not lead to harmful side effects. The model is designed to used reinforced learning (RL)- that comprises artificially intelligent “agents” that complete “actions” in an unpredictable, complex environment to reach the desired outcome. The model’s agent goes through traditionally administered regimens. It uses a combination of the drugs temozolomide (TMZ) and procarbazine, lomustine, and vincristine (PVC), administered to the patients over weeks or months. These regimens are based on protocols that have been used clinically for ages and are based on both, animal testing and various clinical tests and scenarios. The protocols are then used by Oncologists to predict how many doses the patients have to be administered based on weight. As the model explores the regimen, it decides on one of the two actions- Initiate a dose Withhold a dose If it does administer a dose, it has to make the decision if the patient needs the entire dose, or only a portion. After a decision is taken, the model checks with another clinical model to see if the tumor’s size has changed or if it’s still the same. If the tumor’s size has reduced, the model receives a reward else it is penalised. Rewards and penalties essentially are positive and negative numbers, say +1 or – 1. The researchers also had to ensure that the model does not over-dose or give out the maximum number of doses to reduce the mean diameter of the tumor. Therefore, the model is programmed in such a way that whenever it chooses to administer all full doses, it gets penalized. Thus the model is forced to administer fewer, smaller doses. Patik Shah, a principal investigator at the Media Lab who supervised this research, further stresses on the fact that, as compared to traditional RL models that work toward a single outcome, such as winning a game, and take any and all actions that maximize that outcome, the model implemented by the MIT researchers is a “unorthodox RL model that weighs potential negative consequences of actions (doses) against an outcome (tumor reduction)” The model is strikingly wired to find a dose that does not necessarily maximize tumor reduction, but also establishes a perfect balance between maximum tumor reduction and low toxicity for the patients. The training and testing methodology used The model was trained on 50 simulated patients – randomly selected from a large database of glioblastoma patients. These patients had previously undergone traditional treatments. The model conducted about 20,000 trial-and-error test runs for every patient. Once training was complete, the model understood the parameters for optimal regimens. The model was then tested on 50 new simulated patients and used the above-learned parameters to formulate new regimens based on various constraints that the researchers provided. The models treatment regimen was compared to the results of a conventional regimen using both TMZ and PVC. The outcome obtained was practically similar to the results obtained after the human counterparts administered treatments. The model was also able to treat each patient individually, as well as in a single cohort, and achieved similar results (medical data for each patient was available to the researchers). In short, the model has helped to generate precision medicine-based treatments by conducting one-person trials using unorthodox machine-learning architectures.Nicholas J. Schork, a professor and director of human biology at the J. Craig Venter Institute, and an expert in clinical trial design explains “Humans don’t have the in-depth perception that a machine looking at tons of data has, so the human process is slow, tedious, and inexact,” he further adds “Here, you’re just letting a computer look for patterns in the data, which would take forever for a human to sift through, and use those patterns to find optimal doses.” To sum it all up, Machine learning is again proving to be an essential asset in the medical field- helping both researchers as well as patients to view medical treatments in an all new perspective. If you would like to know more about the progress done so far, head over to MIIT news. Read Next 23andMe shares 5mn client genetic data with GSK for drug target discovery Machine learning for genomics is bridging the gap between research and clinical trials 6 use cases of Machine Learning in Healthcare