A Muslim city trustee was hit with racist comments during a public meeting in Illinois, ABC7 reported . Reem Townsend, who is a Muslim and daughter of Palestinian immigrants, was speaking last month at a DuPage Township meeting to discuss a proposed food pantry and resource center. While she was addressing a constituent, she heard a man in the audience say, "Well, [I] didn't know that she was a suicide bomber." "I felt like this was an attack on being Palestinian," she said. "It was like, demonizing me and like, as if he's trying to make me not like I'm a real person." Later, the constituent she was addressing made a comment of his own. "As I was answering one of his statements, he said, 'Oh, you just go back to eating your lunch,'" she recalled. "I was able to stand up and say 'excuse me, it's Ramadan. I don't eat from sunup to sundown. When we eat this late it means we only eat one meal a day and I'm going to eat my food," she said. Townsend has had an influx of support after the incident. She even got a call from Illinois Governor JB Pritzker. "He just called to tell me how sorry and how awful that is. And you know, not to let this bring me down, but more people support me and he asked me what he can do to support me," Townsend said. In a statement, Prtizker tweeted, "I commend Trustee Townsend for her bravery and perseverance in the face of such attacks, and stand by her and other Muslim and Palestinian Illinoisans calling for an end to the hateful rhetoric perpetuated against their communities." "I just hope that like other Muslims and other
Palestinians don't see this and get discouraged," Townsend said. "And I'm not going to let this hold me back from anything." A born-again pastor and pro-Trump
Republican who was featured in a
New York Times article about insurgent factions fighting against the GOP establishment in
Pennsylvania has resigned his seat on a school board over allegations of an affair with a teen, WISR radio reported Tuesday . Bill Halle is the co-founder of an offshoot of the Republican Party called the Patriot Party in Butler County, PA, a group that aligns itself with former President
Donald Trump and supports his
election fraud claims. Halle, who also boasts of being the founder of the Grace Youth and Family Foundation, was accused of an affair with a 17-year-old girl, WISR radio reported . He resigned from the Butler Area School District School Board Sunday. The Butler Eagle reported that he had been its director. A judge has also issued a temporary protection order against Halle, though no formal charges have been filed, WISR said. IN OTHER NEWS: Desperate Lindsey Graham invokes Ruth Bader Ginsburg as he struggles to defend Clarence Thomas at hearing “I am simply not legally able to comment right now, but I will when legally permissible," Halle said in a
Facebook post. "Things are rarely as reported in the liberal press and on
Social Media Hack sites with secret unknown page administraters (sic)," he continued. "I want to thank everyone who has tried to call, text, message, etc., with words of encouragement and support for me. However, I covet your prayers first for the young woman involved." The youth organization announced that it won't be hosting the summer camp this year. Its restaurant, "The Net Cafe" will also be closed. See one local news report below or at the link here. Butler Area school director resigns after sexual violence protection order filed against him www.youtube.com CONTINUE READING Show less Sen. Lindsey Graham (R-SC) struggled on Tuesday to defend Justice Clarence Thomas' alleged ethics violations. At
Senate Judiciary Committee hearing on
Supreme Court ethics, Graham said the topic should not be about unreported gifts Thomas received from a billionaire. "We can talk about ethics, and that's great," he asserted, "but we're also going to talk about today of a concentrated effort by the left to delegitimize this court and to cherry-pick examples to make a point." Graham suggested that reports about conservative justices accepting lavish gifts were biased. " The New York Times did not tell us about Justice Sotomayor's travel to Florence, Italy," he complained before invoking former Justice Ruth Bader Ginsburg. "Justice Sotomayor and Justice Ginsburg traveled to Florence,
Italy on the dime of the New York University." The senator insisted the hearing "was not going to work." "I'm just saying there's a very selective outrage here, and from our point of view on this side of the aisle, we're going to push back as hard as we can and tell the
American people the truth about what's going on here," he concluded. "This is not about making the court better. This is about destroying a conservative court. It will not work." Watch the video clip below from the Senate Judiciary Committee. CONTINUE READING Show less The technology to decode our thoughts is drawing ever closer. Neuroscientists at the University of
Texas have for the first time decoded data from non-invasive brain scans and used them to reconstruct language and meaning from stories that people hear, see or even imagine. In a new study published in Nature Neuroscience , Alexander Huth and colleagues successfully recovered the gist of language and sometimes exact phrases from functional magnetic resonance imaging (fMRI) brain recordings of three participants. Technology that can create language from brain signals could be enormously useful for people who cannot speak due to conditions such as motor neurone disease . At the same time, it raises concerns for the future privacy of our thoughts. Language decoded Language decoding models , also called “speech decoders”, aim to use recordings of a person’s brain activity to discover the words they hear, imagine or say. Until now, speech decoders have only been used with data from devices surgically implanted in the brain, which limits their usefulness. Other decoders which used non-invasive brain activity recordings have been able to decode single words or short phrases, but not continuous language. The new research used the blood oxygen level dependent signal from fMRI scans, which shows changes in blood flow and oxygenation levels in different parts of the brain. By focusing on patterns of activity in brain regions and networks that process language, the researchers found their decoder could be trained to reconstruct continuous language (including some specific words and the general meaning of sentences). Specifically, the decoder took the brain responses of three participants as they listened to stories, and generated sequences of words that were likely to have produced those brain responses. These word sequences did well at capturing the general gist of the stories, and in some cases included exact words and phrases. The researchers also had the participants watch silent movies and imagine stories while being scanned. In both cases, the decoder often managed to predict the gist of the stories. For example, one user thought “I don’t have my driver’s license yet”, and the decoder predicted “she has not even started to learn to drive yet”. Further, when participants actively listened to one story while ignoring another story played simultaneously, the decoder could identify the meaning of the story being actively listened to. How does it work? The researchers started out by having each participant lie inside an fMRI scanner and listen to 16 hours of narrated stories while their brain responses were recorded. These brain responses were then used to train an encoder – a computational model that tries to predict how the brain will respond to words a user hears. After training, the encoder could quite accurately predict how each participant’s brain signals would respond to hearing a given string of words. However, going in the opposite direction – from recorded brain responses to words – is trickier. The encoder model is designed to link brain responses with “semantic features” or the broad meanings of words and sentences. To do this, the system uses the original GPT language model , which is the precursor of today’s GPT-4 model. The decoder then generates sequences of words that might have produced the observed brain responses. The decoder could also describe the action when participants watched silent movies. Tang et al. / Nature Neuroscience The accuracy of each “guess” is then checked by using it to predict previously recorded brain activity, with the prediction then compared to the actual recorded activity. During this resource-intensive process, multiple guesses are generated at a time, and ranked in order of accuracy. Poor guesses are discarded and good ones kept. The process continues by guessing the next word in the sequence, and so on until the most accurate sequence is determined. Words and meanings The study found data from multiple, specific brain regions – including the speech network, the parietal-temporal-occipital association region, and prefrontal cortex – were needed for the most accurate predictions. One key difference between this work and earlier efforts is the data being decoded. Most decoding systems link brain data to motor features or activity recorded from brain regions involved in the last step of speech output, the movement of the mouth and tongue. This decoder works instead at the level of ideas and meanings. One limitation of using fMRI data is its low “temporal resolution”. The blood oxygen level dependent signal rises and falls over approximately a 10-second period, during which time a person might have heard 20 or more words. As a result, this technique cannot detect individual words, but only the potential meanings of sequences of words. No need for privacy panic (yet) The idea of technology that can “read minds” raises concerns over mental privacy. The researchers conducted additional experiments to address some of these concerns. These experiments showed we don’t need to worry just yet about having our thoughts decoded while we walk down the street, or indeed without our extensive cooperation. A decoder trained on one person’s thoughts performed poorly when predicting the semantic detail from another participant’s data. What’s more, participants could disrupt the decoding by diverting their attention to a different task such as naming animals or telling a different story. Movement in the scanner can also disrupt the decoder as fMRI is highly sensitive to motion, so participant cooperation is essential. Considering these requirements, and the need for high-powered computational resources, it is highly unlikely that someone’s thoughts could be decoded against their will at this stage. Finally, the decoder does not currently work on data other than fMRI, which is an expensive and often impractical procedure. The group plans to test their approach on other non-invasive brain data in the future. Christina Maher , Computational Neuroscientist and Biomedical Engineer, University of
Sydney This article is republished from The Conversation under a Creative Commons license. Read the original article . CONTINUE READING Show less