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BEIJING — The Chinese government placed sanctions on seven companies on Friday in response to recent US statements of military sales and aid to Taiwan, the self-governing island that China claims as part of its territory. The sanctions also came in response to the recent approval of the US government's annual defense spending bill, which a Chinese Foreign Ministry statement said "includes multiple negative sections on China." Register to read this story and more for free . Signing up for an account helps us improve your browsing experience. OR See our subscription options.

Donna Probes: Pop-ups to brick and mortarNovember 21, 2024 This article has been reviewed according to Science X's editorial process and policies . Editors have highlightedthe following attributes while ensuring the content's credibility: fact-checked proofread by Mary Fetzer and Francisco Tutella, Pennsylvania State University Police radio transmissions contain personally identifiable information that could pose privacy risks for members of the public, especially Black males, according to a new study by researchers at Penn State and the University of Chicago. "This study provides a window into police activity as events unfold," said Shomir Wilson, associate professor of information sciences and technology at Penn State and study co-author. "We found that because police radio transmissions disproportionately involve Black suspects, there's a proportionally higher privacy risk for Black people in these communications." The researchers studied a total of 24 hours of human-transcribed and annotated broadcast police communications transmitted on a single day in three Chicago dispatch zones, or regions used to coordinate police activity. According to U.S. census data, one zone was majority non-Hispanic white, one majority Hispanic and one majority non-Hispanic Black. The team found that broadcast police communications mentioned males nine times more frequently than females and that Black males were most often mentioned of all groups, even in the majority white zone. The researchers presented their findings at the 27th Association for Computing Machinery Conference on Computer-Supported Cooperative Work and Social Computing on Nov. 9-13 in Costa Rica. The team received a diversity, equity and inclusion recognition from the conference's awards committee. "The typical police radio transmission is short and serves a coordinating purpose, something like "Car 54, where are you?'" said Chris Graziul, research assistant professor at the University of Chicago, study co-author and one of two principal investigators leading the project. "These transmissions try to communicate what's happening and describe who's involved. In the process, sensitive information is often disclosed." The researchers obtained 9,115 transmissions—what they called "utterances"—that occurred when police or dispatch communicated via radio broadcast. They manually transcribed the transmissions and then randomly chose 2,000 utterances from across the three zones to analyze further. They developed a qualitative annotation scheme to label the text. They divided the annotated data into six categories, ranging from event information, such as "residential alarm break in" or "traffic stop," and procedural transmissions, such as the "Car 54" example, to casual transmissions like "Morning, squad." The researchers found that event utterances contained the most references—about 60%—to gender, race/ethnicity, age and protected health information , which can be used to identify individuals. Nearly 68% of utterances that included a sociodemographic indicator used male gendered terms, and approximately 69% of those utterances referred to Black people, according to the researchers. "Our findings contribute to a larger body of evidence about racial disparities in policing. What is novel here is the data source : radio transmissions," Graziul said. "Despite prolific use by police systems around the world, few have explored what this means of communication can tell us about how policing operates in practice. "Disproportionate mentions of Black people reflect a novel way to observe how officers' attention is unevenly distributed across racial/ethnic groups, and identifying this disparity helps us understand challenges to the ethical use of this data source for research, like preventing leakage of sensitive personal information, which would impact Black communities substantially more than other communities." Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights. Sign up for our free newsletter and get updates on breakthroughs, innovations, and research that matter— daily or weekly . After examining the utterances, the team tested a large language model (LLM), a widely used artificial intelligence tool, to determine its capacity to find personal information in the transcripts. Despite the unique nature of the language involved with broadcast police communications, the LLM detected personally identifiable information with high accuracy, highlighting the risk of privacy vulnerability. Bad actors, such as identity thieves, could use AI technology to quickly find and misuse the personal information in transcripts of police radio activity, according to the researchers. "This work reveals a concerning trend of racial inequality in terms of the exposure of sensitive information during police radio transmissions," said Pranav Narayanan Venkit, graduate student pursuing a doctoral degree in informatics in the College of Information Sciences and Technology and first author on the paper. "This study may help researchers and developers give more thought to interactions between LLM and different segments of society—the policing community, minority populations and various other populations—to identify biases and protect personal information." Miranda Goodman, who graduated with her bachelor's degree from Penn State this past summer, and fourth-year Penn State student Samantha Kenny also contributed to this work. More information: Pranav Narayanan Venkit et al, Race and Privacy in Broadcast Police Communications, Proceedings of the ACM on Human-Computer Interaction (2024). DOI: 10.1145/3686921 Provided byPennsylvania State UniversityIndia to play Champions Trophy games in Dubai after team denied Pakistan trip

NoneTOKYO , Dec. 15, 2024 /PRNewswire/ -- Representatives from China and Japan shared their insights on promoting artificial intelligence (AI) governance and data sharing at a sub-forum of the 20th Beijing-Tokyo Forum in Tokyo recently. The sub-forum contributed eastern wisdom to AI governance and digital social development, demonstrating the significance of international cooperation for the development of the digital economy, according to Gao Shaolin, advisor at Peking University's Legal Artificial Intelligence Research Center. AI governance framework The participants agreed that the next 10 years will be a critical period for the development of AI. Gao Wen, academician of the Chinese Academy of Engineering (CAE), said since China's State Council issued a guideline on developing AI in 2017, the nation has made significant progress in AI research and development and industrial layout, especially in computing power and 5G network construction. By the end of 2023, China had over half of the world's 1.57 billion 5G users, according to the World Internet Development Report 2024. It ranked second globally in AI and computing power scale, which has laid a solid foundation for the rapid development of AI. Tatsuo Yamazaki , project professor at the International University of Health and Welfare, said it was very meaningful for Japan and China to discuss strengthening AI governance rules. Fumihiko Kamio , research director of the Nomura Research Institute, echoed his view. He emphasized that the core goal of AI technology is to improve productivity and eliminate obstacles to social development, and called on Chinese and Japanese experts to work together to build an AI governance framework to cope with the global challenges. Deepening international cooperation China put forth the Global AI Governance Initiative in October last year. In July, the UN General Assembly adopted a China -sponsored resolution on enhancing international cooperation on AI capacity-building. The participants spoke highly of the Global Cross-Border Data Flow Cooperation Initiative recently proposed by China . They agreed that AI governance requires global collaboration, especially in the formulation of international standards and the construction of ethical frameworks, where China and Japan can play an active role. Ding Wenhua, academician of the CAE, said China and Japan have both similarities and differences in technology development and governance priorities, so deepening cooperation will bring unique value to global AI governance. " China and Japan should deepen AI technology cooperation between enterprises, work together in AI security research, talent exchange, and jointly explore more possibilities for the application of technology," Wang Zhongyuan , president of the Beijing Academy of Artificial Intelligence, said. Balancing development & risks AI governance refers to the guardrails established to ensure AI systems and tools remain safe and ethical and respect human rights. Xu Zhilong , editor-in-chief of Science and Technology Daily, stressed that AI, as a revolutionary technology, has far-reaching impacts on all areas of society and economy. However, its potential risks such as data leakage and the spread of false information should not be ignored. "Technological progress and security ethics should be developed in a balanced way to ensure that AI technology always serves the progress of human civilization," Xu said. AI governance should not only heed the current technological ethics issues, but also prevent possible long-term risks, such as AI going out of human control, according to Toshio Iwamoto , senior corporate advisor of NTT DATA. He said AI R&D and application should abide by the principles of fairness, transparency, safety and availability. Yuan Yue, chairman of Beijing Dataway Horizon, shared his view from the perspective of regulatory models. "Policy choices should be based on the current status and goals of national technological development," Yuan said, adding that China prefers to provide a more friendly development environment for enterprises while ensuring an effective response to risks. View original content to download multimedia: https://www.prnewswire.com/news-releases/science-and-technology-daily-promoting-ai-governance-jointly-302332050.html SOURCE Science and Technology Daily

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Veeam protection platform updated with added identity and threat analysis toolsLY-3541860 is a monoclonal antibody commercialized by Co, with a leading Phase II program in Relapsing Multiple Sclerosis (RMS). According to Globaldata, it is involved in 3 clinical trials, of which 1 was completed, and 2 are ongoing. Smarter leaders trust GlobalData The gold standard of business intelligence. The revenue for LY-3541860 is expected to reach an annual total of $19 mn by 2040 in the US based off GlobalData’s Expiry Model. The drug’s revenue forecasts along with estimated costs are used to measure the value of an investment opportunity in that drug, otherwise known as net present value (NPV). Applying the drug’s phase transition success rate to remaining R&D costs and likelihood of approval (LoA) to sales related costs provides a risk-adjusted NPV model (rNPV). The rNPV model is a more conservative valuation measure that accounts for the risk of a drug in clinical development failing to progress. LY-3541860 Overview Eli Lilly and Co Overview Co (Lilly) is a healthcare company that discovers, develops, and markets human healthcare products. The company offers medicines for cardiovascular conditions, diabetes, endocrinology, cancer, neurological problems, autoimmune disorders, men’s health, and musculoskeletal problems. The company distributes its pharmaceutical health products through independent wholesale distributors. Lilly conducts research and development activities to discover and deliver innovative medicines. It promotes products through sales representatives and marketing agreements with other pharmaceutical companies. The company operates R&D facilities, and production and distribution facilities in North America, South America, Europe, the Middle East, Africa and Asia-Pacific. Lilly is headquartered in Indianapolis, Indiana, the US. The company reported revenues of (US Dollars) US$34,124.1 million for the fiscal year ended December 2023 (FY2023), an increase of 19.6% over FY2022. In FY2023, the company’s operating margin was 18.9%, compared to an operating margin of 25% in FY2022. In FY2023, the company recorded a net margin of 15.4%, compared to a net margin of 21.9% in FY2022. The company reported revenues of US$11,302.8 million for the second quarter ended June 2024, an increase of 28.9% over the previous quarter. For a complete picture of LY-3541860’s valuation, From Blending expert knowledge with cutting-edge technology, GlobalData’s unrivalled proprietary data will enable you to decode what’s happening in your market. You can make better informed decisions and gain a future-proof advantage over your competitors. , the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article. To create this model, GlobalData takes into account factors including patent law, known and projected regulatory approval processes, cash flows, drug margins and company expenses. Combining these data points with GlobalData’s world class analysis creates high value models that companies can use to help in evaluation processes for each drug or company. The rNPV method integrates the probability of a drug reaching a clinical stage into the cash flow at that time, which provides a more accurate valuation, as it considers the probability that the drug never makes it through the clinical pathway to commercialization. GlobalData’s rNPV model uses proprietary likelihood of approval (LoA) and phase transition success rate (PTSR) data for the indication in the highest development stage, which can be found on GlobalData’s .

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