Africa
Coreset for Robust Geometric Median: Eliminating Size Dependency on Outliers
Fang, Ziyi, Huang, Lingxiao, Yang, Runkai
We study the robust geometric median problem in Euclidean space $\mathbb{R}^d$, with a focus on coreset construction.A coreset is a compact summary of a dataset $P$ of size $n$ that approximates the robust cost for all centers $c$ within a multiplicative error $\varepsilon$. Given an outlier count $m$, we construct a coreset of size $\tilde{O}(\varepsilon^{-2} \cdot \min\{\varepsilon^{-2}, d\})$ when $n \geq 4m$, eliminating the $O(m)$ dependency present in prior work [Huang et al., 2022 & 2023]. For the special case of $d = 1$, we achieve an optimal coreset size of $\tildeΘ(\varepsilon^{-1/2} + \frac{m}{n} \varepsilon^{-1})$, revealing a clear separation from the vanilla case studied in [Huang et al., 2023; Afshani and Chris, 2024]. Our results further extend to robust $(k,z)$-clustering in various metric spaces, eliminating the $m$-dependence under mild data assumptions. The key technical contribution is a novel non-component-wise error analysis, enabling substantial reduction of outlier influence, unlike prior methods that retain them.Empirically, our algorithms consistently outperform existing baselines in terms of size-accuracy tradeoffs and runtime, even when data assumptions are violated across a wide range of datasets.
Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures
Chang, Tyler A., Arnett, Catherine, Eldesokey, Abdelrahman, Sadallah, Abdelrahman, Kashar, Abeer, Daud, Abolade, Olanihun, Abosede Grace, Mohammed, Adamu Labaran, Praise, Adeyemi, Sharma, Adhikarinayum Meerajita, Gupta, Aditi, Iyigun, Afitab, Simplício, Afonso, Essouaied, Ahmed, Chorana, Aicha, Eppa, Akhil, Oladipo, Akintunde, Ramesh, Akshay, Dorkin, Aleksei, Kondoro, Alfred Malengo, Aji, Alham Fikri, Çetintaş, Ali Eren, Hanbury, Allan, Dembele, Alou, Niksarli, Alp, Arroyo, Álvaro, Bajand, Amin, Khanna, Amol, Chkhaidze, Ana, Condez, Ana, Mkhonto, Andiswa, Hoblitzell, Andrew, Tran, Andrew, Poulis, Angelos, Majumder, Anirban, Vacalopoulou, Anna, Wong, Annette Kuuipolani Kanahele, Simonsen, Annika, Kovalev, Anton, S, Ashvanth., Lana, Ayodeji Joseph, Kinay, Barkin, Alhafni, Bashar, Busole, Benedict Cibalinda, Ghanem, Bernard, Nathani, Bharti, Đurić, Biljana Stojanovska, Agbonile, Bola, Bergsson, Bragi, Fischer, Bruce Torres, Tutar, Burak, Çınar, Burcu Alakuş, Kane, Cade J. Kanoniakapueo, Udomcharoenchaikit, Can, Arnett, Catherine, Helwe, Chadi, Nerella, Chaithra Reddy, Liu, Chen Cecilia, Nwokolo, Chiamaka Glory, España-Bonet, Cristina, Amol, Cynthia, Lee, DaeYeop, Arad, Dana, Dzenhaliou, Daniil, Pugacheva, Daria, Choi, Dasol, Abolade, Daud, Liu, David, Semedo, David, Popoola, Deborah, Mataciunas, Deividas, Nyaboke, Delphine, Kumar, Dhyuthy Krishna, Glória-Silva, Diogo, Tavares, Diogo, Goyal, Divyanshu, Lee, DongGeon, Anajemba, Ebele Nwamaka, Grace, Egonu Ngozi, Mickel, Elena, Tutubalina, Elena, Herranen, Elias, Anand, Emile, Habumuremyi, Emmanuel, Ajiboye, Emuobonuvie Maria, Yulianrifat, Eryawan Presma, Adenuga, Esther, Rudnicka, Ewa, Itiola, Faith Olabisi, Butt, Faran Taimoor, Thekkekara, Fathima, Haouari, Fatima, Tjiaranata, Filbert Aurelian, Laakom, Firas, Grasso, Francesca, Orabona, Francesco, Periti, Francesco, Solomon, Gbenga Kayode, Ngo, Gia Nghia, Udhehdhe-oze, Gloria, Martins, Gonçalo, Challagolla, Gopi Naga Sai Ram, Son, Guijin, Abdykadyrova, Gulnaz, Einarsson, Hafsteinn, Hu, Hai, Saffari, Hamidreza, Zaidi, Hamza, Zhang, Haopeng, Shairah, Harethah Abu, Vuong, Harry, Kuulmets, Hele-Andra, Bouamor, Houda, Yu, Hwanjo, Debess, Iben Nyholm, Deveci, İbrahim Ethem, Hanif, Ikhlasul Akmal, Cho, Ikhyun, Calvo, Inês, Vieira, Inês, Manzi, Isaac, Daud, Ismail, Itzhak, Itay, Iuliia, null, Alekseenko, null, Belashkin, Ivan, Spada, Ivan, Zhelyazkov, Ivan, Brinton, Jacob, Isbarov, Jafar, Čibej, Jaka, Čuhel, Jan, Kocoń, Jan, Krito, Jauza Akbar, Purbey, Jebish, Mickel, Jennifer, Za, Jennifer, Kunz, Jenny, Jeong, Jihae, Dávalos, Jimena Tena, Lee, Jinu, Magalhães, João, Yi, John, Kim, Jongin, Chataignon, Joseph, Imperial, Joseph Marvin, Thevakumar, Jubeerathan, Land, Judith, Jiang, Junchen, Kim, Jungwhan, Sirts, Kairit, R, Kamesh, V, Kamesh, Tshinu, Kanda Patrick, Kukk, Kätriin, Ponkshe, Kaustubh, Huseynova, Kavsar, He, Ke, Buchanan, Kelly, Sarveswaran, Kengatharaiyer, Zaman, Kerem, Mrini, Khalil, Kyars, Kian, Kruusmaa, Krister, Chouhan, Kusum, Krishnakumar, Lainitha, Sánchez, Laura Castro, Moscoso, Laura Porrino, Choshen, Leshem, Sencan, Levent, Øvrelid, Lilja, Alazraki, Lisa, Ehimen-Ugbede, Lovina, Thevakumar, Luheerathan, Thavarasa, Luxshan, Malik, Mahnoor, Keita, Mamadou K., Jangid, Mansi, De Santis, Marco, García, Marcos, Suppa, Marek, D'Ciofalo, Mariam, Ojastu, Marii, Sikander, Maryam, Narayan, Mausami, Skandalis, Maximos, Mehak, Mehak, Bozkurt, Mehmet İlteriş, Workie, Melaku Bayu, Velayuthan, Menan, Leventhal, Michael, Marcińczuk, Michał, Potočnjak, Mirna, Shafiei, Mohammadamin, Sharma, Mridul, Indoria, Mrityunjaya, Habibi, Muhammad Ravi Shulthan, Kolić, Murat, Galant, Nada, Permpredanun, Naphat, Maugin, Narada, Corrêa, Nicholas Kluge, Ljubešić, Nikola, Thomas, Nirmal, de Silva, Nisansa, Joshi, Nisheeth, Ponkshe, Nitish, Habash, Nizar, Udeze, Nneoma C., Thomas, Noel, Ligeti-Nagy, Noémi, Coulibaly, Nouhoum, Faustin, Nsengiyumva, Buliaminu, Odunayo Kareemat, Ogundepo, Odunayo, Fejiro, Oghojafor Godswill, Funmilola, Ogundipe Blessing, God'spraise, Okechukwu, Samuel, Olanrewaju, Oluwaseun, Olaoye Deborah, Akindejoye, Olasoji, Popova, Olga, Snissarenko, Olga, Chiemezie, Onyinye Anulika, Kinay, Orkun, Tursun, Osman, Moses, Owoeye Tobiloba, Joshua, Oyelade Oluwafemi, Fiyinfoluwa, Oyesanmi, Gamallo, Pablo, Fernández, Pablo Rodríguez, Arora, Palak, Valente, Pedro, Rupnik, Peter, Ekiugbo, Philip Oghenesuowho, Sahoo, Pramit, Prokopidis, Prokopis, Niau-Puhipau, Pua, Yahya, Quadri, Mignone, Rachele, Singhal, Raghav, Kadiyala, Ram Mohan Rao, Merx, Raphael, Afolayan, Rapheal, Rajalakshmi, Ratnavel, Ghosh, Rishav, Oji, Romina, Solis, Ron Kekeha, Guerra, Rui, Zawar, Rushikesh, Bashir, Sa'ad Nasir, Alzaabi, Saeed, Sandeep, Sahil, Batchu, Sai Pavan, Kantareddy, SaiSandeep, Pranida, Salsabila Zahirah, Buchanan, Sam, Rutunda, Samuel, Land, Sander, Sulollari, Sarah, Ali, Sardar, Sapkota, Saroj, Tautvaisas, Saulius, Sen, Sayambhu, Banerjee, Sayantani, Diarra, Sebastien, M, SenthilNathan., Lee, Sewoong, Shah, Shaan, Venkitachalam, Shankar, Djurabaeva, Sharifa, Ibejih, Sharon, Dutta, Shivanya Shomir, Gupta, Siddhant, Suárez, Silvia Paniagua, Ahmadi, Sina, Sukumar, Sivasuthan, Song, Siyuan, A., Snegha, Sofianopoulos, Sokratis, Simon, Sona Elza, Benčina, Sonja, Gvasalia, Sophie, More, Sphurti Kirit, Dragazis, Spyros, Kaufhold, Stephan P., S, Suba., AlRashed, Sultan, Ranathunga, Surangika, Someya, Taiga, Pungeršek, Taja Kuzman, Haklay, Tal, Jibril, Tasi'u, Aoyama, Tatsuya, Abashidze, Tea, Cruz, Terenz Jomar Dela, Blevins, Terra, Nikas, Themistoklis, Idoko, Theresa Dora, Do, Thu Mai, Chubakov, Tilek, Gargiani, Tommaso, Rathore, Uma, Johannesen, Uni, Ugwu, Uwuma Doris, Putra, Vallerie Alexandra, Kumar, Vanya Bannihatti, Jeyarajalingam, Varsha, Arzt, Varvara, Nedumpozhimana, Vasudevan, Ondrejova, Viktoria, Horbik, Viktoryia, Kummitha, Vishnu Vardhan Reddy, Dinić, Vuk, Sewunetie, Walelign Tewabe, Wu, Winston, Zhao, Xiaojing, Diarra, Yacouba, Nikankin, Yaniv, Mathur, Yash, Chen, Yixi, Li, Yiyuan, Xavier, Yolanda, Belinkov, Yonatan, Abayomi, Yusuf Ismail, Alyafeai, Zaid, Shan, Zhengyang, Tam, Zhi Rui, Tang, Zilu, Nadova, Zuzana, Abbasi, Baber, Biderman, Stella, Stap, David, Ataman, Duygu, Schmidt, Fabian, Gonen, Hila, Wang, Jiayi, Adelani, David Ifeoluwa
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we present Global PIQA, a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by 335 researchers from 65 countries around the world. The 116 language varieties in Global PIQA cover five continents, 14 language families, and 23 writing systems. In the non-parallel split of Global PIQA, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements. We find that state-of-the-art LLMs perform well on Global PIQA in aggregate, but they exhibit weaker performance in lower-resource languages (up to a 37% accuracy gap, despite random chance at 50%). Open models generally perform worse than proprietary models. Global PIQA highlights that in many languages and cultures, everyday knowledge remains an area for improvement, alongside more widely-discussed capabilities such as complex reasoning and expert knowledge. Beyond its uses for LLM evaluation, we hope that Global PIQA provides a glimpse into the wide diversity of cultures in which human language is embedded.
Global urban visual perception varies across demographics and personalities
Quintana, Matias, Gu, Youlong, Liang, Xiucheng, Hou, Yujun, Ito, Koichi, Zhu, Yihan, Abdelrahman, Mahmoud, Biljecki, Filip
Understanding people's preferences is crucial for urban planning, yet current approaches often combine responses from multi-cultural populations, obscuring demographic differences and risking amplifying biases. We conducted a largescale urban visual perception survey of streetscapes worldwide using street view imagery, examining how demographics -- including gender, age, income, education, race and ethnicity, and personality traits -- shape perceptions among 1,000 participants with balanced demographics from five countries and 45 nationalities. This dataset, Street Perception Evaluation Considering Socioeconomics (SPECS), reveals demographic- and personality-based differences across six traditional indicators -- safe, lively, wealthy, beautiful, boring, depressing -- and four new ones -- live nearby, walk, cycle, green. Location-based sentiments further shape these preferences. Machine learning models trained on existing global datasets tend to overestimate positive indicators and underestimate negative ones compared to human responses, underscoring the need for local context. Our study aspires to rectify the myopic treatment of street perception, which rarely considers demographics or personality traits.
BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Wu, Jiageng, Gu, Bowen, Zhou, Ren, Xie, Kevin, Snyder, Doug, Jiang, Yixing, Carducci, Valentina, Wyss, Richard, Desai, Rishi J, Alsentzer, Emily, Celi, Leo Anthony, Rodman, Adam, Schneeweiss, Sebastian, Chen, Jonathan H., Romero-Brufau, Santiago, Lin, Kueiyu Joshua, Yang, Jie
Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, benchmarking on large-scale real-world data such as electronic health records (EHRs) is critical, as clinical decisions are directly informed by these sources, yet current evaluations remain limited. Most existing benchmarks rely on medical exam-style questions or PubMed-derived text, failing to capture the complexity of real-world clinical data. Others focus narrowly on specific application scenarios, limiting their generalizability across broader clinical use. To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources across nine languages. It covers eight major task types spanning the entire continuum of patient care across six clinical stages and 20 representative applications, including triage and referral, consultation, information extraction, diagnosis, prognosis, and billing coding, and involves 14 clinical specialties. We systematically evaluated 95 LLMs (including DeepSeek-R1, GPT-4o, Gemini series, and Qwen3 series) under various inference strategies. Our results reveal substantial performance variation across model sizes, languages, natural language processing tasks, and clinical specialties. Notably, we demonstrate that open-source LLMs can achieve performance comparable to proprietary models, while medically fine-tuned LLMs based on older architectures often underperform versus updated general-purpose models. The BRIDGE and its corresponding leaderboard serve as a foundational resource and a unique reference for the development and evaluation of new LLMs in real-world clinical text understanding. The BRIDGE leaderboard: https://huggingface.co/spaces/YLab-Open/BRIDGE-Medical-Leaderboard
What Elon Musk's Version of Wikipedia Thinks About Hitler, Putin, and Apartheid
What does Elon Musk want the world to know about "white genocide theory"? Because he's been vocal about the issue in the past-- advancing the idea, for example, that Jews are pushing "hatred against whites"--I decided to search for the term on Grokipedia, the competitor to Wikipedia that Musk launched yesterday. First, the site uses just that term,, rather than, as you would see on Wikipedia and elsewhere. Just a few sentences in, Grokipedia provides the "empirical underpinnings" of this supposed campaign to eliminate white people of European descent around the world. And the site argues that conversation about this purported genocide is systematically suppressed by the media and academia, which are "prone to ideological biases favoring multiculturalism" and "relegate the theory to fringe conspiracy status despite the observable data on population trajectories."
Nvidia will build AI supercomputers for US Department of Energy
Nvidia, the artificial intelligence (AI) chip leader, will build seven new supercomputers for the United States Department of Energy (DOE), CEO Jensen Huang has said. The company has $500bn in bookings for its AI chips, Huang said on Tuesday in a keynote address at the company's GTC event in Washington, DC, the US capital. It is striking deals around the world while also navigating a US-China trade war that could determine which country's technology is most used across the globe. Investors are looking for clarity on what chips the tech company will be able to sell to the vast Chinese market, but Huang in his keynote speech praised policies by US President Donald Trump while announcing new products and deals. These included network technology that will let Nvidia AI chips work with quantum computers.
Seal bearing ancient language found in Jerusalem confirms Bible story in the Old Testament
'Monster' hurricane Melissa makes landfall in Jamaica as multiple people are left dead: Live updates Here are the REAL danger signs you're drinking too much. Forget the crippling headache and brain fog, now doctors reveal the five little-known alarm bells... if you suffer these this is what it's time to do Three US Air Force members are found dead overnight after husband'murdered wife and her colleague before killing himself' Alec Baldwin's daughter Ireland, 30, makes rare sighting with mom Kim Basinger, 71... after calling her family'poisonous' Warning gold rally is turning into a'mini-bust' as prices keep falling I know the pathetic truth about Kristen Bell's'cry for help' that will settle this domestic violence scandal once and for all: KENNEDY'Humiliating' truth about influencer TooTurntTony and his extreme stunts: He's ripped, makes $3m a year and has all the hottest girls... but a dark reality lies beneath LIZ JONES: Why I believe ruthless Kate's the driving force behind Andrew's eviction - and why no one now dares cross her Netanyahu orders'powerful strikes in Gaza' after accusing Hamas of violating ceasefire terms following'faked' return of hostage remains Doctors thought I was on drugs... but they were left horrified when they looked inside my ear A simple, non-surgical medical procedure is giving men the penis shape that ALL women secretly love. The real reasons you wake up at 3am. No it's not just regular insomnia - there's hidden causes that are so easy to fix. Chris Evans, 44, welcomes first child with wife Alba Baptista, 28, as baby's gender and name is revealed Ex-SNL stars break silence on show's'challenging' workplace amid firing bloodbath and mass cast exodus Man's simple diet and exercise regime allows him to run marathons at 91.
Russian forces gain foothold in strategic Ukrainian town
Russian troops are making a concerted push in eastern Ukraine and have gained a foothold in the strategic hub of Pokrovsk, Ukrainian President Volodymyr Zelensky says. Moscow's soldiers outnumber Kyiv's 8-1 in the area and Ukraine cannot match that, Zelensky added while insisting Russia had not yet achieved the planned result. Russia has been trying to capture Pokrovsk for two years. The key supply and transport hub provides supplies and reinforcements to the eastern front - and it would get Moscow closer to occupying the entirety of the Donetsk region. It would also put towns of the heavily fortified fortress belt - Kramatorsk, Slovyansk, Kostyantynivka and Druzhkivka - within easier reach of Moscow.
Mysterious interstellar visitor set to reveal its true self in just HOURS
'Monster' hurricane Melissa makes landfall in Jamaica as multiple people are left dead: Live updates Alec Baldwin's daughter Ireland, 30, makes rare sighting with mom Kim Basinger, 71... after calling her family'poisonous' Netanyahu orders'powerful strikes in Gaza' after accusing Hamas of violating ceasefire terms following'faked' return of hostage remains Warning gold rally is turning into a'mini-bust' as prices keep falling Poignant moment Trump is gifted priceless Abe golfing relic ahead of signing landmark deal... and issuing gushing praise of Japan LIZ JONES: Why I believe ruthless Kate's the driving force behind Andrew's eviction - and why no one now dares cross her Boss of Google's self-driving car company makes dystopian statement about the vehicles killing people Bill Gates now says climate change won't be as serious as he fears - and calls for more spending on vaccines instead Chris Evans, 44, welcomes first child with wife Alba Baptista, 28, as baby's gender and name is revealed I traveled to Latin America for a discount tummy tuck... Apple Martin releases music video after nepo baby's singing was slammed as'off-key drunken karaoke performance' Jennifer Lawrence admits she's planning on a boob job as she reveals all the plastic surgery she's had The mysterious interstellar visitor traveling through our solar system may finally reveal its true nature in just hours, as scientists wait for it to emerge from behind the sun. While many astronomers are convinced the object known as 3I/ATLAS will be confirmed as a comet, some scientists have said the three-mile-long visitor could be an artificially constructed craft that's maneuvering around the solar system. Scientists expect to determine which scenario is correct once they observe exactly where the object exits perihelion, saying that a noticeable shift in its trajectory tomorrow could indicate that 3I/ATLAS is artificially powered. In space travel, the most effective moment to accelerate or decelerate a spacecraft is when it is closest to a massive body. Firing the engine at this point, an effect known as the Oberth effect, produces the greatest change in speed.
Hurricane Melissa triggers flight delays at Florida airport as Category 5 storm sends dangerous winds toward US
Netanyahu orders'powerful strikes in Gaza' after accusing Hamas of violating ceasefire terms following'faked' return of hostage remains LIZ JONES: Why I believe ruthless Kate's the driving force behind Andrew's eviction - and why no one now dares cross her Two teenage plane passengers are'stabbed with metal fork during mid-flight attack' Apple Martin releases music video after nepo baby's singing was slammed as'off-key drunken karaoke performance' War inside Biden's circle revealed as chief of staff urged him to quit following debate disaster Horror in Manhattan as young woman's naked body is found dumped on sidewalk Trump strikes FOUR'narco-terror' boats in one day as death toll skyrockets Hurricane tracker shows Melissa is now stronger than Katrina as'storm of the century' closes in on Jamaica: Live updates Chris Evans, 44, welcomes first child with wife Alba Baptista, 28, as baby's gender and name is revealed Ivanka Trump appointed to glitzy nonprofit board alongside TWO megastar singers who've previously attacked her father'She hasn't told the full story. This is typical her': How David Harbour is'looking after' Lily Allen's daughters despite'victim' singer publicly humiliating him... as insider tells DOLLY BUSBY what's REALLY going on Jennifer Lawrence admits she's planning on a boob job as she reveals all the plastic surgery she's had Brigitte Macron's daughter reveals cruel taunt the French first lady's GRANDCHILDREN have to face and describes the toll it has taken on her health Departures from Miami International Airport (MIA) are facing major delays as severe weather linked to hurricane activity sweeps through South Florida . According to the latest update issued at 11:28am EDT, departing flights are delayed an average of 45 minutes and are climbing. The Federal Aviation Administration (FAA) alert comes as Hurricane Melissa is just minutes away from making landfall on Jamaica as a Category 5, powerful enough to send pounding waves and dangerous winds north to Florida . Earlier today, meteorologists confirmed that Melissa was now more intense than Katrina, which caused an estimated $125bn worth of damage and killed 1,392 people when it struck New O rleans in 2005.