It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Monday, November 02, 2026
'You're a stupid person!' Trump rages as protesters repeatedly interrupt rally
President Donald Trump seemed angered Sunday as protesters repeatedly interrupted his rally in Iowa.
Trump spoke at the Five Flags Center in Dubuque, Iowa, to rally supporters to vote for Rep. Ashley Hinson (R-IA) as she attempts to keep the state's Senate seat red on November 3. The rally was one of two events Trump held Sunday, with the other happening in Salina, Kansas.
Multiple protesters disrupted Trump's speech in Iowa Sunday, which seemed to send the president into a frenzy.
One protester interrupted Trump as he talked about Hinson's weight loss journey, saying he "knew the importance of what she was doing." The move seemed to anger Trump, as he was interrupted before he got to his "punchline."
"Don't bother. They're paid protesters," Trump said, waving his hand. "They go away quickly. They're like a gnat; they're like a little fly."
As Trump berated one protester, the crowd began chanting "U.S.A." Police escorted the person out of the rally without incident.
"There's usually two or three of them per rally," Trump said. "They get paid $250. They get paid by [George] Soros. They're losers!"
Trump seemed to grow more frustrated as protests continued to erupt inside the Five Flags Center.
"They put about three or four of them in [here," Trump said. "And your law enforcement does such a good job."
The president seemed to lose his cool after the third protester began shouting at him
"You're a stupid person!" Trump snapped.
Dire warning as 'cornered and desperate' Trump ally tips the world toward nuclear war
President Donald Trump may be "sleepwalking" toward a nuclear conflict at the urging of one of his "cornered and desperate" allies, according to one analyst.
Tucker Carlson, a former Fox News host, posted a lengthy warning on X, alleging that the Trump administration may approve a nuclear strike on Iran within a week of the midterm elections. It's an issue that Israeli Prime Minister Benjamin Netanyahu calls Trump about "every day," according to Carlson.
"The target would be Pickax Mountain in central Iran, where Israeli intelligence claims the Iranian government has restarted its nuclear enrichment program," Carlson wrote. " No conventional weapon in the American arsenal can penetrate 300 feet of solid granite to destroy the tunnels where enrichment is supposedly taking place. The only military option is a nuclear strike. Netanyahu has been pushing hard for one."
Carlson noted that the recent election results in Israel made Netanyahu's pleas more urgent. Netanyahu's governing coalition took a shellacking in the election because of a surge in support for Israel's Arab parties, which could pave the way for Netanyahu's exit from state politics.
"Netanyahu is cornered and desperate," Carlson noted. "After last week’s election results, he can’t form a government unless Trump intervenes directly in Israeli politics and brokers a deal with other parties that gives Netanyahu 61 seats in the Knesset. Otherwise, he is out as prime minister and on his way to ignominy, investigations, and possibly jail. Netanyahu must stay close to Trump. He needs this war."
"No country other than the United States has ever used a nuclear weapon in war. Some would like to. An American nuclear attack on Iran would set a new precedent. The Russians have already told at least two other governments that an American nuclear strike on Iran would free them to use nuclear weapons against Ukraine. Pakistan and India would be tempted to do the same against each other, as would North Korea against Seoul. After the first nuclear strike, World War Three is inevitable. That war would be short and final," Carlson added.
"We’ve been sleepwalking toward this moment for months," Carlson added. "Since February, the administration has quietly rewritten America’s nuclear doctrine to make first use of so-called 'battlefield nukes' easier and therefore more likely. No one in Washington in either party seems bothered by this. Now we’re on the brink of it."
KAIST cuts AI fine-tuning from 10 hours to 3… faster development of medical and coding AI
Professor Jae-Gil Lee’s research team from the School of Computing develops “CluSTER,” a technology that selects and distributes training data
Figure 1. Conceptual diagram of the clustering-based balanced data sampling and allocation method developed by the research team. Different colors represent clusters of data that have similar effects on learning, and different shapes within the same color represent specific data types within each cluster. In conventional training on the full dataset, data types with a large number of samples can be repeatedly included across multiple GPUs, so similar computations may be repeated. The proposed method selects diverse data from each cluster in a balanced way and allocates different clusters to each GPU, which reduces redundant computation. For
Can AI fine-tuning, that once took 10 hours, be completed in just 3 hours? KAIST researchers have developed a technology that selects the most useful training data and distributes it evenly across multiple GPUs. By reducing repetitive learning of similar data, the method shortened training time by up to 69.6% while maintaining nearly the same AI performance. The technology is expected to help develop AI tailored to specific tasks, such as corporate customer service or assisting medical professionals with information retrieval, more quickly and at lower cost.
KAIST (President Choongsik Bae) announced on November 2 that a research team led by Professor Jae-Gil Lee from the School of Computing developed “CluSTER,” a large language model fine-tuning technology that groups and selects data with similar effects on learning and distributes them evenly across multiple GPUs.
To use a large language model in a specific field such as coding or medicine, it must be additionally trained on relevant data. This process is called “fine-tuning.” It is similar to teaching a broadly knowledgeable AI coding problems and solutions or medical questions and answers so that it can perform tasks in that particular field more effectively.
This process requires substantial computation, so multiple GPUs divide the data and process it in parallel. A GPU is a semiconductor device capable of rapidly performing the calculations required for AI training. Just as several people can divide up a large set of problems and solve them together, multiple GPUs split the training data, process it, and combine the results to improve a single AI model.
However, if the data are distributed randomly, multiple GPUs may repeatedly process data that have similar learning effects. This is like a student repeatedly solving problems that are very similar to ones they have already mastered, resulting in relatively little additional learning compared with the amount of computation required.
The research team therefore focused not only on “how much data to train on,” but also on “which data to select and how to distribute them.” The goal was to reduce repetitive learning while ensuring that the AI did not miss diverse information it needed to learn.
CluSTER first analyzes how each piece of data affects AI learning and groups together data with similar effects. The grouping is not based simply on whether the data cover the same topic or use similar words. Even problems that look different on the surface are classified into the same group if they teach the AI similar problem-solving abilities.
For example, when teaching a student addition, a problem about combining two apples with three apples and a problem about combining two pencils with three pencils tell different stories, but the calculation being learned is the same. In this way, the research team identified and grouped data that had similar effects on learning even when they were expressed differently.
The system then selects data from each group and distributes them so that multiple GPUs process a diverse range of learning information evenly. It is similar to reducing repetitive problems in a workbook while making sure that all important types of problems remain and are divided among several people to solve. The key feature is that the method does not merely reduce the amount of data, but also enables multiple GPUs to use the selected data efficiently.
The research team fine-tuned large language models with approximately 7 billion and 13 billion parameters using three training datasets from the coding and medical fields. Parameters are internal numerical values adjusted as an AI model learns and are used to reflect acquired knowledge in its responses. The team evaluated the effectiveness of the technology through a total of eight performance benchmarks.
In the experiments, the models maintained nearly the same performance even when trained on only about 31% of the full dataset, while training time was reduced by up to 69.6%. Applying the maximum reduction rate, training that previously took 10 hours could be completed in about 3 hours. The research team primarily conducted experiments in an environment using four GPUs and also confirmed better performance than conventional methods in an eight-GPU environment.
This study shows that AI training efficiency can be improved without adding more GPUs if the training data are selected and distributed effectively. In the future, the technology is expected to help companies and research institutions reduce the time and GPU costs required for additional training when developing AI for specific purposes such as coding assistance or medical question answering.
Professor Jae-Gil Lee said, “To improve the efficiency of AI training, it is important not only to use more GPUs, but also to intelligently select which data should be used for learning,” adding, “This study is significant because it shows that existing GPU resources can be used more efficiently to reduce the training burden of large-scale AI models.”
Hyunjin Kim, a Ph.D. student in the KAIST School of Computing, participated in the study as the first author, with Professor Jae-Gil Lee as the corresponding author. Youngeun Nam, a postdoctoral researcher at the University of Toronto, and Jaemin Han and Wonhyeok Choi, master’s students at the KAIST Graduate School of Data Science, participated as co-authors. The research findings were presented in October 2026 at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2026), a major international conference in natural language processing. ※ Paper title: Clustering-Based Balanced Sampling and Allocation with Data Parallelism for High-Performance Fine-Tuning, https://arxiv.org/pdf/2609.12584 ※ Author information: Hyunjin Kim (Ph.D. student, KAIST School of Computing, first author), Youngeun Nam (postdoctoral researcher, University of Toronto; KAIST alumnus, co-author), Jaemin Han (master’s student, KAIST Graduate School of Data Science, co-author), Wonhyeok Choi (master’s student, KAIST Graduate School of Data Science, co-author), Jae-Gil Lee (Professor, KAIST School of Computing, corresponding author)
This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP), funded by the Ministry of Science and ICT, through the SW Computing Industry Core Technology Development Program’s SW Star Lab project. The project title is “DB4DL: High-Usability and Performance In-Memory Distributed DBMS for Deep Learning” (RS-2020-II200862).
Article Title
Clustering-Based Balanced Sampling and Allocation with Data Parallelism for High-Performance Fine-Tuning
From left: Ph.D. student Hyunjin Kim and Professor Jae-Gil Lee (KAIST School of Computing). Inset photos at top right, from left: Youngeun Nam, postdoctoral researcher (University of Toronto); Jaemin Han, master’s student (KAIST Graduate School of Data Science); and Wonhyeok Choi, master’s student (KAIST Graduate School of Data Science).
Credit
KAIST
Cold showers benefit the environment and your health
Many people know that their behavior harms the environment, yet they struggle to change their habits. "This is especially true when they feel they have to give something up or spend more money," says psychologist Dr. Lena Glaser. As a psychotherapist, she regularly encounters this reluctance to change in her work.
Small changes that fit into everyday life
In her recently completed doctoral dissertation at the Faculty of Psychology, Glaser explored whether people are more willing to adopt habits that benefit both the climate and their health if those habits are easy to incorporate into everyday life and offer personal rewards. She investigated this question by studying the effects of regular cold showers.
"When people talk about changing their behavior to protect the climate, the discussion usually centers on transportation, diet or recycling," Glaser says. But in these areas, sweeping measures such as banning flights or serving only vegetarian meals in workplace cafeterias often meet with resistance. For some, it even feels like their lifestyle choices are being policed, despite knowing that flying contributes to climate change and that eating meat has negative consequences for both the environment and their health.
"We need a more empathetic approach if we want people to change their behavior," Glaser believes. She points out that water temperature and water consumption rarely come up in climate-action discussions, even though changing shower habits has an environmental impact comparable to recycling. "Taking a cold shower isn't exactly pleasant, but it's a small change to make. After all, we shower anyway." It's also a relatively easy step that can save money.
Fewer sick days
The study included 719 participants aged 18 to 65 who were asked to take showers as cold as their shower would allow for 12 weeks. To qualify, they had to be in good health and not already take cold showers.
Many participants dropped out during the first two weeks, but the dropout rate declined afterward. "That suggests people gradually get used to it," Glaser concludes.
As the study progressed, the remaining participants reported feeling increasingly better, both mentally and physically. According to Glaser, these subjective improvements also showed up in their sick leave: On average, participants took 31% fewer sick days than the control group.
Good for the environment and your wallet
When asked about the immediate benefits of a cold shower, participants said they felt more awake and refreshed. They also took pride in having pushed themselves to do something uncomfortable. "Experiences like these can be motivating," Glaser says.
Over time, taking cold showers also saves you money. People tend to spend less time in the shower, reducing their water consumption. They also use less energy because the water doesn't need to be heated. or a 45-liter shower, lowering the water temperature from 40°C to 15°C saves 1.3 kWh of energy. That means lower utility bills and a measurable reduction in CO2 emissions. Over the 12-week study, participants reduced their CO2 emissions by 15 kilograms per person, roughly equivalent to the emissions from driving 100 kilometers. In other words, cold showers make sense both environmentally and financially.
More generally, Glaser says, "When people see a personal benefit from changing their behavior and don't feel that they're giving something up or missing out, they're more motivated to stick with it."
Whether participants continued taking cold showers after the study ended is unknown. It's also unclear how environmentally conscious they were in other areas of their lives, or whether they compensated for the discomfort of cold showers by treating themselves in other ways. Such behavior could reduce or even cancel out the environmental benefits.
Find the temperature that works for you
Glaser has encouraging news for anyone who still dreads stepping into a cold shower: Even small changes in water temperature can have a positive effect. It doesn't have to be all or nothing. Finding a middle ground is perfectly fine.
"If we force ourselves to give up every comfort and pleasure, it will eventually take a toll on our well-being and mental health. And getting sick doesn't help the environment either."
Anyone interested in trying cold showers can start gradually, test their limits, and perhaps push them a little further over time. "The water doesn't have to be equally cold every day. We don't feel equally brave every day, and that's okay," Glaser says.
For someone trying it at home, the study's instruction to shower "as cold as possible" could be interpreted a little differently: Make the water as cold as you feel comfortable with that day. "It's important to listen to your body and pay attention to how you feel. But sometimes it's worth pushing yourself a little."
Global change is also affecting the behaviour and mobility of the ocellated lizard, a reptile typical of Mediterranean ecosystems. This species — the largest lizard on the Iberian Peninsula — could experience changes in its ability to move efficiently between shelters and feeding areas, escape predators and interact with its natural environment. This is the conclusion of a paper published in the Journal of Animal Ecology led by researcher Antigoni Kaliontzopoulou, from the Faculty of Biology and the Biodiversity Research Institute (IRBio) at the University of Barcelona.
The study describes how the behaviour of the ocellated lizard varies in response to rising temperatures, a vital aspect for reptiles and other ectothermic organisms, which depend on external heat sources and are unable to maintain body temperature in the same way as mammals and birds (endotherms).
In a warmer climate, the ability of ocellated lizard (Timon lepidus) populations to respond will not be uniform: some lizards, considered “generalists”, show greater resilience and a higher capacity for mobility at high temperatures compared to “specialists”, whose mobility stagnates at high temperatures and, as a result, are less adapted to take advantage of extreme warming scenarios.
“The strategy based on a ‘generalist’ profile proves to be more effective in the long term for coping with climate change than specialist profiles,” notes Antigoni Kaliontzopoulou, a member of the UB’s Department of Evolutionary Biology, Ecology and Environmental Sciences. “However, while increased mobility can offer more opportunities for reproduction and foraging, it is also associated with a higher risk of mortality from predation.”
The study, which integrates physiological, behavioural and microclimatic data, provides a powerful approach to predicting how climate change can reshape the spatial and temporal niches of ectotherms. “The results of the study are relevant to ectothermic organisms in general: lizards are excellent models for studying these impacts, as their body temperature and physiological performance depend directly and closely on the environment and temperature,” says Kaliontzopoulou.
The study is also signed by experts Katerina Sioumpoura, Neftalà Sillero and Carolina Reyes-Puig of the University of Porto (Portugal), and Urtzi Enriquez-Urzelai of the Czech Academy of Sciences (Czech Republic).
Biologging and mechanistic niche modelling reveal thermal dependence of individual lizard movement paterns
Article Publication Date
23-Oct-2026
The study, which integrates physiological, behavioural and microclimatic data, provides a powerful approach to predicting how climate change can reshape the spatial and temporal niches of ectotherms.
Credit
Katerina Sioumpoura
The results of the study are relevant to ectothermic organisms in general.
Credit
Journal of Animal Ecology
Hidden erosion: Study reveals soft mountain rock erodes three times faster than hard rock—with climate implications
UMass Amherst-led research relying on cosmic rays from exploded stars shows gentle alpine slopes hide rapid erosion rates that shape the Earth’s carbon cycle
AMHERST, Mass. — A University of Massachusetts Amherst-led research team has discovered that weaker rock in mountain landscapes erodes three times faster than harder rock–even on gentler slopes–and is one of the main attributes governing mountain erosion. The finding also challenges our current understanding of the relationship between erosion and the release of carbon dioxide, and suggests that geologists and atmospheric scientists may be underestimating the CO2 contributions coming from the Earth’s rocky places.
The research, recently published in Science Advances, depends on a new technique relying on common quartz grains, the rare isotope beryllium-10 (known as 10Be) and cosmic rays from exploded stars.
Over the course of the universe’s nearly 14 billion years, countless stars have exploded, and when they do, they blast out showers of cosmic shrapnel in the form of high energy particles. These cosmic particles have been raining down on the Earth for uncountable ages in at a constant rate. When the particles penetrate rocks on the surface, 10Be is produced within quartz. For four decades, geologists have been able to harvest these quartz grains from rocks like granite and sandstone, in which the quartz grains are comparatively large, to test all sorts of hypotheses.
But in rocks where the grains are too fine, such as shale, the conventional 10Be technique hasn’t worked. And that has limited geologists’ abilities to accurately pinpoint things like erosion rates across a broad spectrum of rock types.
That is, until Marder and his colleagues developed a new method for harvesting the rare isotope from such rocks.
“When we look at landscapes, we are only seeing a snapshot in time,” says Eyal Marder, a postdoctoral researcher in Earth, geographic, and climate sciences at the University of Massachusetts Amherst and the lead author of the new paper.
But researchers want to understand the full geological movie from start to finish in order to understand the land’s lifecycle. Marder and his team studied the Colorado’s East River Watershed, which drains approximately 465 square kilometers of the southern Elk and West Elk mountains—jagged peaks whose most famous landmark, Crested Butte, is revered as one of the U.S.’s premier ski areas. The sheer alpine topography is notable for its diverse geological composition: a mix of incredibly durable igneous rock and much weaker shale, deposited by rivers eons ago.
Their new technique allowed Marder and his team to make a few surprising discoveries. As expected, weaker sedimentary rocks erode faster than harder igneous ones. What was surprising is how much faster they erode.
“Shale’s erodibility is three times that of the harder rocks in the East River Watershed,” says Marder.
It’s so fast that the contrast with the slower-eroding rocks can prolong landscape erosion over millions of years. This also means that the fastest erosion might happen not in steeper, more resistant parts of the mountains, but rather in lower, comparatively gentle, rounder, sedimentary ones.
This shift means that the best way to evaluate whether or not a landscape has reached equilibrium is not by visually gauging its pitch, but also estimating erosion rates from the amounts of 10Be in the sediment carried by the streams that drain mountainous regions.
Marder and his team have also exposed a missing piece in global climate models. Geologists recognize that weathering of different minerals on the surface can either release or absorb CO2. But the fact that in mountainous landscape, shale, which releases CO2 to the atmosphere, is eroding faster and at a larger magnitude than intrusive rocks, which absorb CO2 from the atmosphere, means that geologists and atmospheric scientists may have underestimated the CO2 contributions coming from mountains.
“Scale our work up from the Elk and West Elk mountains to all the mountainous regions of the world, and getting erosion rates and their equivalent impact on CO2 release right has a marked impact on what we know about biogeochemical global cycles and climate change,” says Marder. “Give us the type of rock and enough quartz for our 10Be measurements, and we can estimate what effect it will have on CO2 emissions from thousands to millions of years.”
Quartz grains isolated from the sediment they were found in. The next step is to dissolve them in order to extract, and ultimately measure, their beryllium-10.
Credit
Eyal Marder
About the University of Massachusetts Amherst
The flagship of the commonwealth, the University of Massachusetts Amherst is a nationally ranked public land-grant research university that seeks to expand educational access, fuel innovation and creativity and share and use its knowledge for the common good. Founded in 1863, UMass Amherst sits on nearly 1,450-acres in scenic Western Massachusetts and boasts state-of-the-art facilities for teaching, research, scholarship and creative activity. The institution advances a diverse, equitable, and inclusive community where everyone feels connected and valued—and thrives, and offers a full range of undergraduate, graduate and professional degrees across 10 schools and colleges and 100 undergraduate majors.
