by Michael Tilley ([email protected]) 6 seconds ago 0 views 
U.S. District Court Judge Timothy Brooks on Monday (Dec. 23) issued an order striking down key provisions in Act 372, an attempt by the Arkansas Legislature to censor library books. Brooks in July 2023 issued an injunction blocking implementation of the act.
Arkansas Attorney General Tim Griffin said he respects the judge’s ruling, but plans to appeal.
Several Arkansas libraries, library associations, bookstore owners, and booksellers in early 2023 sued the state in an effort to overturn Act 372, which was set to become law on Aug. 1, 2023.
The lawsuit was also connected to an attempt by Crawford County officials to relocate certain books to a “social section.” U.S. District Court Judge P.K. Holmes III ruled in September against Crawford County in a First Amendment lawsuit regarding the removal and relocation of books largely because of objections from citizens to LGBTQ content.
Specifically, Act 372 creates a process for books to be challenged in public libraries, with library officials having the option to appeal the challenge with a local and/or city government. It also removes the exemption protecting librarians from criminal penalties if they are found to have knowingly provided certain materials to minors. Republicans who successfully pushed for Act 372 said the law was needed to protect minors from certain books, with many of the books mentioned being LGBTQ-related.
Plaintiffs in the lawsuit filed within the Fayetteville Division of the Western District Court of Arkansas include the Fayetteville Public Library, the Central Arkansas Library System, the Eureka Springs Carnegie Public Library, and the American Booksellers Association. Defendants listed include Crawford County Judge Chris Keith, members of the Crawford County Public Library Board, and all Arkansas prosecuting attorneys.
According to the plaintiffs, Act 372 limits access to constitutionally protected materials, violates constitutionally protected free speech, violates due process, and lacks a judicial review of decisions to ban or relocate library items.
Brooks, based in Fayetteville with the Western District of Arkansas, largely agreed with arguments set forth by plaintiffs that the law was vague and allowed unconstitutional censorship by library committees and local governments.
“Moreover, if a library committee or local governmental body elected to relocate a book instead of withdrawing it, Section 5 contemplates moving the book ‘to an area that is not accessible to minors under the age of eighteen (18) years’ – without defining what ‘accessible to minors’ means,” Brooks noted in part of his ruling. “If Section 5 were to take effect, libraries would have to guess what level of security would be necessary to satisfy the law’s ‘[in]accessib[ility]’ requirements. For all of these reasons, the Court finds that Section 5 fails the ‘stringent vagueness test’ that applies to a law that interferes with access to free speech.”
In another section of the ruling, Brooks noted: “The Court therefore concludes that Plaintiffs have established as a matter of law that Section 5 would permit, if not encourage, library committees and local governmental bodies to make censorship decisions based on content or viewpoint, which would violate the First Amendment.”
Gov. Sarah Sanders, who praised passage of Act 372, said she will work with Griffin on the appeal.
“Act 372 is just common sense: schools and libraries shouldn’t put obscene material in front of our kids. I will work with Attorney General Griffin to appeal this ruling and uphold Arkansas law,” Sanders said.
Holly Dickson, ACLU of Arkansas executive director, said the judge’s order “ensures that libraries remain sanctuaries for learning and exchange of ideas and information.”
“This was an attempt to ‘thought police,’ and this victory over totalitarianism is a testament to the courage of librarians, booksellers, and readers who refused to bow to intimidation,” she said in a statement.
Link here for a PDF of Brooks’ order.
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Denzel Washington Has Become a Minister After Saying ‘You Can’t Talk’ About Religion in Hollywood  Just Jared
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BBB Tip: How to choose safe browser extensions  BBB
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$500 Billion Crypto Selloff: Bitcoin Falls 14% As Post-Election Rally Loses Steam  Forbes
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As artificial intelligence (AI) becomes more embedded in health care, the ability to accurately evaluate AI models is critical. In medical applications, where early diagnosis and anomaly detection are often key, selecting the right performance AI metrics can determine the clinical success or failure of AI tools. If a health care AI tool claims to predict disease risk or guide treatment options, it must be rigorously validated to ensure its outputs are true representations of the medical phenomena it assesses. In evaluating health care artificial intelligence, two critical factors, validity and reliability, must be considered to ensure trustworthy AI systems.
When using medical AI, errors are inevitable, but understanding their implications is vital. False positives occur when an AI system incorrectly identifies a disease or condition in a patient who does not have it, leading to unnecessary tests, treatments, and patient anxiety. False negatives, on the other hand, occur when the system fails to detect a disease or condition that is present, potentially delaying critical interventions. These types of errors, known as Type I and Type II errors, respectively, are particularly relevant in AI systems designed for diagnostic purposes. Validity is crucial because inaccurate predictions can lead to inappropriate treatments, missed diagnoses, or overtreatment, all of which compromise patient care. Reliability, the consistency of an AI system’s performance, is also substantially important. A reliable AI model will produce the same results when applied to similar cases, ensuring that physicians can trust its outputs across different patient populations and clinical scenarios. Without reliability, physicians may receive conflicting or inconsistent recommendations from AI health care tools, leading to confusion and uncertainty in clinical decision-making.
A physician must focus on three important AI metrics: 1) area under the precision-recall curve (AUPRC), 2) area under the receiver operating characteristic curve (AUROC), and 3) average precision (AP), and how they apply to health care AI models. In health care, many AI predictive tasks involve imbalanced datasets, where the positive class (e.g., patients with a specific disease) is much smaller than the negative class (e.g., healthy patients). This is often the case in areas like cancer detection, rare disease diagnosis, or anomaly detection in critical care settings. Traditional performance metrics may not fully capture how well an AI model performs in such situations, particularly when the rare positive cases are the most clinically significant.
In binary classification, where an AI model is tasked with predicting whether a patient has a certain condition or not, choosing the right metric is crucial. For instance, an AI model that predicts “healthy” for nearly every case might score well on accuracy but fail to detect the rare but critical positive cases. This makes AI metrics like AUPRC, AUROC, and AP particularly valuable in evaluating how well an AI system balances identifying true positives while minimizing false positives and negatives.
Area under the precision-recall curve (AUPRC) is a performance metric that is particularly well-suited for imbalanced classification tasks, such as health care anomaly detection or disease screening. AUPRC summarizes the trade-offs between precision (the percentage of true positive predictions out of all positive predictions) and recall (the percentage of actual positive cases correctly identified). It is especially useful in scenarios where finding positive examples, such as identifying cancerous lesions or predicting organ failure, is of utmost importance.
AUPRC is particularly relevant in AI health care because precision is critical, especially when treatments or interventions can have negative consequences. Recall is essential when missing a true positive, such as a missed cancer diagnosis, could be life-threatening. By focusing on these two AI metrics, AUPRC provides a clearer picture of how well an AI model performs when the goal is to maximize correct positive classifications while keeping false positives in check. For example, in the context of sepsis detection in the ICU, where early and accurate detection is crucial, a high AUPRC indicates that the AI model can identify true sepsis cases without overwhelming clinicians with false positives.
While AUPRC is valuable for evaluating AI systems in imbalanced datasets, another common AI metric is the area under the receiver operating characteristic curve (AUROC). AUROC is often used in binary classification tasks because it evaluates both false positives and false negatives by plotting the true positive rate against the false positive rate. However, AUROC can be misleading in imbalanced datasets where the majority class (e.g., healthy patients) dominates the predictions. In such cases, AUROC may still give a high score even if the AI model is performing poorly in detecting the minority positive cases.
For example, in a cancer screening program where the prevalence of cancer is very low, an AI model that predicts “no cancer” for most cases could still score well on AUROC despite missing a significant number of true cancer cases. In contrast, AUPRC would give a more accurate reflection of the model’s ability to find the rare positive cases. That said, AUROC is still valuable in situations where both false positives and false negatives carry significant costs. In applications like early cancer screening, where missing a diagnosis (false negative) can be just as costly as over-diagnosis (false positive), AUROC may be a better choice for evaluating AI model performance.
Another important AI metric is average precision (AP), which is commonly used as an approximation for AUPRC. While there are multiple methods to estimate the area under the precision-recall curve, AP provides a reliable summary of how well an AI model performs across different precision-recall thresholds. AP is particularly useful in health care applications where anomaly detection is key. For instance, in predicting hypotension during surgery, where early detection can prevent life-threatening complications, the AP score provides insight into the AI system’s effectiveness in catching such anomalies early and with high precision.
There are different ways to estimate the area under the precision-recall curve (AUPRC), with the trapezoidal rule and average precision (AP) being two of the most common. While both methods are useful, they can produce different results:
For AI health care applications like cardiac arrest prediction, where precise detection is vital, AP often gives a clearer picture of the AI model’s ability to balance precision and recall effectively. Physicians must be aware that in health care, making clinical decisions based on AI predictions requires a deep understanding of how well the AI model performs in rare but critical situations. AUPRC may be suited to evaluating AI models designed to detect rare conditions, such as cancer diagnosis, sepsis detection, and hypotension prediction, where a high AUPRC score ensures that the AI system is catching these rare events while minimizing false alarms that could distract clinicians.
In summary, the evaluation of AI models in health care requires careful consideration of which AI metrics provide the most meaningful insights. For tasks involving imbalanced datasets common in health care applications such as disease diagnosis, anomaly detection, and early screening, AUPRC offers a more targeted and reliable assessment than traditional AI metrics like AUROC. By focusing on precision and recall, AUPRC gives a more accurate reflection of an AI system’s ability to find rare but important positive cases, making it an essential tool for evaluating AI in medical practice. Average precision (AP) also serves as a valuable approximation of AUPRC and can provide even more precise insights into how well an AI system balances precision and recall across varying thresholds. Together, these AI metrics empower clinicians and researchers to assess the performance of AI models in real-world health care settings, ensuring that AI tools contribute effectively to improving patient outcomes.
Neil Anand is an anesthesiologist.
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On this week’s Sustainable Living show, Anni Ellis is joined by Dr. Bob Linde of Acupuncture and Herbal Therapies and Traditions School of Herbal Studies to discuss wild foraging and herbal medicine. Dr. Bob, Registered Herbalist and Acupuncture Physician, has worn many hats in his life. From commercial lobster and conch diver, treasure hunter, to infantryman in Desert Storm, and Green Peace worker, he is the owner of Acupuncture & Herbal Therapies, a multi-practitioner/multimodality practice in St. Petersburg Florida, and the Founder and Clinical Supervisor of Traditions School of Herbal Studies, also in St. Pete.
Topics discussed include:
-how Dr. Bob found his way into herbalism
-helpful herbs for colds and flu
-herbal and medicinal plants in Florida
-foraging your own edibles and medicinals
-kitchen medicine
-safe and sustainable foraging/harvesting practices
and more!
Find out more about Dr. Bob, his herbal practice, educational events and school at Acupuncture and Herbal Therapies and Traditions School of Herbal Therapies.
If you love the Sustainable Living Show, make sure to tune in every Monday at 11am on 88.5fm or listen to past episodes in the archives here. You can also stay up to date with show happenings on our Facebook page. Head over to the tip jar and direct your donation to Sustainable Living to show your monetary support. Remember, it takes a community to build a community.
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Herbs and wild foraging with Dr. Bob Linde (Acupuncture and Herbal Therapies)
The Scoop for Monday Monday, December 23th, 2024
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Horsemen in action during the 2019 Durbar festival in Kano. Image source: Khalid' Ozavogu Abdul (@OzavoguAbdul on Twitter)
On Wednesday, members of a closed cryptocurrency support Telegram channel, held a meeting across four states in the northern region of Nigeria—Kano, Niger, Sokoto, and Katsina—on how they could enjoy the full promises of the Pi Network, an entity that describes itself on its website as “the first and only digital currency you can mine on your phone. The resolution, according to Bashir Zhamani*, 27, who was part of one the meetings held in Suleja, a Niger state city, was to build a Pi chain mall in Kano, Nigeria where people can shop using their PiCoin, a digital currency mined on the Pi Network app. 
“Pioneers in China are already selling and buying phones and cars with their Pi,” he said. “We need to also start doing it here.” Pioneer is the name Pi Network users are called.
There are reports of malls scattered across Asia receiving Pi as payment and myriads of social media posts corroborating that—tweets about Asians, especially Chinese, paying for electronic gadgets with their PiCoin. Some Nigerians in the north of the country have also reportedly acquired cars and phones using their PiCoin. However, for a token that has no official price and hasn’t started trading yet, the  buzz is curious.
PiCoin was founded by three Stanford students, Vince McPhilip, Chengdiao Fan and Nicolas Kokkalis in 2013, and its web app launched in 2014. The mobile app was released on March 14, 2019, allowing users to download it and earn tokens from their phones. Individuals could earn thousands of Pi tokens by pressing a green flash symbol on the app hourly. The process was simple and fulfilled the purpose of democratising crypto, as it required no monetary investment or strong technological know-how, unlike bitcoin and ethereum. As a result, it quickly acquired lots of users. In fact, as of June, the network said it has crossed the 35 million user mark.
For about eight years, between 2014 till this August, the network was at mining or pre-mainnet phase, meaning users could only mine and keep. This phase took so long that it bored out its enthusiasts and they abandoned the app en masse while some even declared it a waste of time. “I stopped mining for two years,” said Debo, a crypto enthusiast who resides in Ilorin, a capital city in the north-central region of Nigeria. But in March, when the network announced that users can now carry out a know-your-customer (KYC), a requirement to move to the next level, which is the mainnet phase, enthusiasm was rekindled.
The mainnet phase is simply the stage where actions can be carried on the coin within the network. Users can, for instance, perform P2P transactions with other pioneers who have also reached the mainnet stage. According to the network, the goals of the Mainnet phase are to make further progress in decentralisation and utilities, ensure stability and longevity, and retain growth and security. 
Zhamani, with 700 Pi hasn’t been verified yet while some pioneers he brought onto the network, including some who have as low as 60 Pi, have been verified. “Verification is by luck. Looks like the core team is using the KYC to screen out people,” Zhamani said. The core team is the team behind the Pi Network. Verification can take between five days to six months after means of identification is submitted. Debo, on the other hand, has been verified, and it took 2 weeks.
Debo has moved all his 1,700 Pi into the mainnet. But he said he has locked them for another three years. “I believe PiCoin has the tendency to be almost as valuable as the top crypto in the next three years, so I will wait until it launches fully,” he told TechCabal. Debo is betting the same way he did Solana, a coin he said gave him his first million. 
But not everybody can wait like Debo. For Audu*, a primary school teacher in a public school in Kano who also got verified into the Mainnet, selling is the best option as he doesn’t want to wait another three years to get monetary value. He entered the Mainnet with about 2500 Pi, and as soon as he knew he could do P2P transactions, he sold off more than half of it. “I still have 1000 Pi to sell or keep, but I’m glad to have made money from the effort,” he told TechCabal. 
A Pi was selling for ₦350 in August when the Mainnet migration started, then it dropped to  ₦300, and currently at  ₦150. Audu was able to sell his coin for  ₦300/Pi. This means that he made ₦450,000 from a piece of currency he acquired from a few taps on some app. He mentioned that his wife has more Pi than him and has made close to a million naira. Now that the price has come down by half of what they sold for, they are both considering holding on to the rest of the coin till whenever the next phase arrives and values go up. But for them, nothing is carved in stone and it feels good to know they can always pull up an app and sell off a few assets whenever they need money. 
For those buying Pi, there are two reasons: one, reselling to Asians who use it to acquire actual products; second, to collect and hold until it becomes the next bitcoin. This was confirmed to TechCabal by Soft, a crypto trader who asked for his real name to be hidden due to privacy concerns. “There’s no other reason outside of these two reasons because the coin can only stay within the network for now,” Soft said. 
The Pi Network network uses a halving system, the same system bitcoin and other blockchain use to limit the supply of the coin. The network essentially halves the amount of Pi given in rewards after reaching certain milestones. For instance, users could mine 1.6 Pi when it first started, but as of now, the user count is in the tens of millions; only 0.2 Pi can be mined per hour. Once the token has 1 billion users, the mining rewards become 0.
Few days before the group meeting in Kano, a Twitter account named pen_griffen shared a image announcing the registration of a business called Arewa Pi Mega Mall with the Corporate Affairs Commission (CAC). While the image looks doctored, a quick search on the corporate registry returned that the business is truly registered. 
This level of enthusiasm is not strange in the cryptocurrency world as almost all tokens and shitcoin passed through this hyper phase where holders dream of an utopia powered by their newly-found and beloved coin, before they go bust. But unlike most shitcoin that were either rug-pulled or went bust due to their lack of utility within a few months or year that they launched, the PiCoin core team has taken their time to build their network. In fact, they have announced that they aren’t offering any initial coin offering (ICO), meaning they won’t be selling it. So anybody who wants a piece of the pie must download the Pi app, and the roadmap on its white paper is very clear to this.
Regardless of how good this coin may sound, whether it’s another crypto heaven or hell waiting to be unleashed is still unknown. But if there’s one thing we know about the cryptoverse, time will always tell. 

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A 17-year-old girl was stabbed to death in Azusa by her ex-boyfriend just days before Christmas, according to her family.
The Azusa Police Department responded around 10 p.m. Friday to a report of an assault with a deadly weapon on North Soldano Avenue, according to a police news release.
Officers found the girl at the scene suffering from stab wounds; she was taken to a hospital, where she was later pronounced dead, police said.
The 18-year-old suspect is in custody, officials said. The case will be presented to the Los Angeles County District Attorney’s office for the consideration of criminal charges.
The victim’s family has identified her as Angelina Camillia Gonzales.
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Robert Caraballo, Angelina’s stepfather, told ABC7 that the suspect picked up Angelina from a cheerleading banquet and took her to his home. The family was eventually able to find Angelina’s location by tracking her cellphone.
“When I got there, there was already crime scene tape up and forensics and all kinds of stuff. And I ran up to the officers like, ‘Hey, my daughter’s in there. You know, my daughter is in there. I’m trying to find her. She’s not home.’ And that’s when they told me, ‘Sit aside for a minute,’” Caraballo said.
Caraballo told the outlet that he had previously told Angelina to stay away from her ex.
Angelina’s family is raising money via GoFundMe to help pay for funeral costs.
“She had a heart of gold. She was my perfect gorgeous Angel baby. I don’t even have the words to say. I can’t even cope. I need her. Our family is devastated,” Angelina’s mother, Leticia Pimentel Caraballo, wrote on the page.
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Summer Lin is a reporter on the Fast Break Desk, the Los Angeles Times’ breaking news team. Before coming to The Times, she covered breaking news for the Mercury News and national politics and California courts for McClatchy’s publications, including the Miami Herald. An East Coast native, Lin moved to California after graduating from Boston College and Columbia University’s Graduate School of Journalism. Lin was among The Times’ staff members who covered the Monterey Park mass shooting in 2023, which was recognized by the Pulitzer Board as a finalist in breaking news.
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Metaplanet Snaps Up More Bitcoin in Biggest Single Buy to Date  Decrypt
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A new cryptocurrency is capturing the spotlight, drawing attention as major investors divert their gaze from popular meme coins like Dogecoin and Dogwifhat. This emerging altcoin is stirring excitement in the crypto community, hinting at fresh opportunities. Discover what sets this digital asset apart and why it’s becoming the talk of the town.

Unleash your inner alpha with DOGEN, the meme token built for those who want to live a beautiful life. DOGEN is the alpha dog that never misses a chance to win big. This is the token for winners who won’t settle for anything less. Think luxury cars, stacks of cash, and beautiful women — that’s the Alpha DOGEN lifestyle!
Ready to live like an Alpha DOGEN and enjoy the finer things in life? It’s on the runway, gearing up for a 700% takeoff by the end of the presale — and that’s just the start. With DOGEN, you’re looking at potential thousand-fold returns as memetokens lead the hottest trend of this altcoin season.
The earlier you jump in, the more you win! It’s a deal that others will envy, and you’ll be at the top of the pack.
DOGEN is the new doggie on the Solana memetokens ground alongside BONK, WIF, and Popcat known for their astonishing 1000% growth. Currently undervalued, DOGEN is poised to take this crypto narrative to the next level, potentially breaking records in this bull run.
DOGEN isn’t just a short-lived hype; it’s a growing movement. The team behind DOGEN is laser-focused on building a thriving community of alpha leaders who refuse to settle for less and are geared up to dominate the market. This token offers real value, from exciting campaigns to exclusive perks for early adopters. DOGEN is an opportunity to be part of something that lasts.
DOGEN’s multi-level referral program is as fierce as it gets: You’ll score 7% from every token your direct bros (1st level) buy using your referral code. Plus, there are more rewards down the line — you can grow your profits as your referrals bring in even more users.
Hold DOGEN and let others envy you! Join the Dogen Army today and conquer the crypto world!🚀
Dogecoin (DOGE) is a cryptocurrency that began in 2013 as a fun alternative to traditional coins, using a Shiba Inu dog as its logo. Unlike Bitcoin, which has a limited supply, Dogecoin has no maximum cap and adds 10,000 new coins every minute. Initially seen as a joke, its value soared in 2021, breaking into the top ten cryptocurrencies with a market value over $50 billion. This rise was fueled by social media and support from figures like Elon Musk. Dogecoin’s strong community and broad appeal show its potential. In the current market cycle, it remains a significant player, and its active community might attract those interested in cryptocurrency.
Dogwifhat (WIF) is a new dog-themed meme coin on the Solana blockchain, inspired by the viral Dogwifhat meme. Meme coins like WIF gain popularity through community engagement and viral trends, often stemming from internet memes or pop culture. Dog-themed tokens are notable in the crypto world for their strong community appeal. Unlike traditional cryptocurrencies, WIF focuses on creating a sense of fun and community. The coin’s potential lies in its ability to attract users through its meme-based charm and the efficiency of the Solana blockchain. In the current market cycle, WIF is capturing attention, and its growth is linked to its community engagement and the popularity of meme coins on Solana.
With coins like DOGE and WIF losing momentum, DOGEN stands out for those seeking luxury and success. Expected to grow 700% by presale’s end, with potential for massive returns this altcoin season, DOGEN follows successful tokens like BONK and Popcat. It builds a community of leaders, offering exclusive perks for early adopters.
Site: Dogen crypto
Twitter: https://x.com/dogenmeme
Telegram: https://t.me/Dogen_Portal

Meet Alex, a distinguished writer and researcher specializing in the dynamic world of cryptocurrency and blockchain technology. With a wealth of experience and an unyielding passion for staying at the forefront of this ever-evolving industry, Alex is your trusted guide in navigating the complex terrain of digital assets and blockchain innovation. Alex holds a Ph.D. in Blockchain Development, a testament to his unparalleled expertise in this field. His educational journey, combined with his multifaceted perspective, allows him to excel in dissecting the geographical and economic factors shaping the cryptocurrency market, providing insights that delve beyond the surface. What sets Alex apart is not just his professional expertise, but his personal dedication to the transformative potential of blockchain technologies. His keen research skills ensure that he remains a reliable source for industry trends and insights, helping you make informed decisions in the world of cryptocurrencies. Join Alex on this exciting journey through the crypto realm, where knowledge meets innovation, and discover the possibilities that lie within the blockchain revolution. Business Email: info@crypto-news-flash.com Phone: +49 160 92211628
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