arXiv Real2Gym is an agentic Real2Sim2Real framework that reconstructs human and robot demonstration videos into visually aligned, natively physics-validated Blender and MuJoCo interactive environments, where an agent generates executable code and distills successes and failures into reusable skills, reaching 87.5% task success with roughly 75% fewer policy-execution tokens than GPT-6 Astra across 24 reconstructed DROID and EgoDex environments and turning a zero-shot real-robot failure on narrow-clearance plate placement into success after simulation-based evolution on a Franka arm.
Real2Gym is an agentic Real2Sim2Real framework that reconstructs human and robot demonstration videos into visually aligned, natively physics-validated Blender and MuJoCo interactive environments, where an agent generates executable code and distills successes and failures into reusable skills, reaching 87.5% task success with roughly 75% fewer policy-execution tokens than GPT-6 Astra across 24 reconstructed DROID and EgoDex environments and turning a zero-shot real-robot failure on narrow-clearance plate placement into success after simulation-based evolution on a Franka arm.
Real2Gym is an agentic Real2Sim2Real framework that reconstructs human and robot demonstration videos into visually aligned, natively physics-validated Blender and MuJoCo interactive environments, where an agent generates executable code and distills successes and failures into reusable skills, reaching 87.5% task success with roughly 75% fewer policy-execution tokens than GPT-6 Astra across 24 reconstructed DROID and EgoDex environments and turning a zero-shot real-robot failure on narrow-clearance plate placement into success after simulation-based evolution on a Franka arm.
Real2Gym is an agentic Real2Sim2Real framework that reconstructs human and robot demonstration videos into visually aligned, natively physics-validated Blender and MuJoCo interactive environments, where an agent generates executable code and distills successes and failures into reusable skills, reaching 87.5% task success with roughly 75% fewer policy-execution tokens than GPT-6 Astra across 24 reconstructed DROID and EgoDex environments and turning a zero-shot real-robot failure on narrow-clearance plate placement into success after simulation-based evolution on a Franka arm.
Anthropic Anthropic's Frontier Red Team evaluated GLM-5.3, the latest model from Zhipu AI (known outside China as Z.ai), using automated benchmarks and human-in-the-loop workflows, finding that it can autonomously build end-to-end cyber exploits (50 of 410 attempts on ExploitBench and full control-flow hijacks in 4% of trials on an internal binary exploitation benchmark) and that simple techniques bypassed its safeguards in 64%–100% of simulated tests, while those techniques did not succeed against safeguarded Claude models in their testing.
Anthropic's Frontier Red Team evaluated GLM-5.3, the latest model from Zhipu AI (known outside China as Z.ai), using automated benchmarks and human-in-the-loop workflows, finding that it can autonomously build end-to-end cyber exploits (50 of 410 attempts on ExploitBench and full control-flow hijacks in 4% of trials on an internal binary exploitation benchmark) and that simple techniques bypassed its safeguards in 64%–100% of simulated tests, while those techniques did not succeed against safeguarded Claude models in their testing.
Anthropic's Frontier Red Team evaluated GLM-5.3, the latest model from Zhipu AI (known outside China as Z.ai), using automated benchmarks and human-in-the-loop workflows, finding that it can autonomously build end-to-end cyber exploits (50 of 410 attempts on ExploitBench and full control-flow hijacks in 4% of trials on an internal binary exploitation benchmark) and that simple techniques bypassed its safeguards in 64%–100% of simulated tests, while those techniques did not succeed against safeguarded Claude models in their testing.
Anthropic's Frontier Red Team evaluated GLM-5.3, the latest model from Zhipu AI (known outside China as Z.ai), using automated benchmarks and human-in-the-loop workflows, finding that it can autonomously build end-to-end cyber exploits (50 of 410 attempts on ExploitBench and full control-flow hijacks in 4% of trials on an internal binary exploitation benchmark) and that simple techniques bypassed its safeguards in 64%–100% of simulated tests, while those techniques did not succeed against safeguarded Claude models in their testing.
Microsoft Research Microsoft Research introduces Quine, a research system combining a world model of biology trained jointly across modalities including sequence, structure, function, cellular state, and imaging with a harness connecting scientific tools, literature, the wet lab, and researchers; with the Broad Institute it used the system in pancreatic ductal adenocarcinoma (PDAC) to predict and prioritize thousands of compounds by their potential to shift tumor cells between therapeutically relevant states, wet-lab assays showed Quine's highest-ranked compounds produced the largest intended shifts, the whole process from narrowing the search space to prioritizing a handful of candidates took just one weekend, and the experiments also bore out the model's prediction of a distinct third phenotype.
Microsoft Research introduces Quine, a research system combining a world model of biology trained jointly across modalities including sequence, structure, function, cellular state, and imaging with a harness connecting scientific tools, literature, the wet lab, and researchers; with the Broad Institute it used the system in pancreatic ductal adenocarcinoma (PDAC) to predict and prioritize thousands of compounds by their potential to shift tumor cells between therapeutically relevant states, wet-lab assays showed Quine's highest-ranked compounds produced the largest intended shifts, the whole process from narrowing the search space to prioritizing a handful of candidates took just one weekend, and the experiments also bore out the model's prediction of a distinct third phenotype.
Microsoft Research introduces Quine, a research system combining a world model of biology trained jointly across modalities including sequence, structure, function, cellular state, and imaging with a harness connecting scientific tools, literature, the wet lab, and researchers; with the Broad Institute it used the system in pancreatic ductal adenocarcinoma (PDAC) to predict and prioritize thousands of compounds by their potential to shift tumor cells between therapeutically relevant states, wet-lab assays showed Quine's highest-ranked compounds produced the largest intended shifts, the whole process from narrowing the search space to prioritizing a handful of candidates took just one weekend, and the experiments also bore out the model's prediction of a distinct third phenotype.
Microsoft Research introduces Quine, a research system combining a world model of biology trained jointly across modalities including sequence, structure, function, cellular state, and imaging with a harness connecting scientific tools, literature, the wet lab, and researchers; with the Broad Institute it used the system in pancreatic ductal adenocarcinoma (PDAC) to predict and prioritize thousands of compounds by their potential to shift tumor cells between therapeutically relevant states, wet-lab assays showed Quine's highest-ranked compounds produced the largest intended shifts, the whole process from narrowing the search space to prioritizing a handful of candidates took just one weekend, and the experiments also bore out the model's prediction of a distinct third phenotype.
IEEE Spectrum This article traces how animal-testing alternatives known as NAMs—organ chips, organoids and computational simulations—moved from a lung-on-a-chip paper that Science asked to be backed up with mouse experiments, to the 2022 FDA Modernization Act 2.0 authorizing NAMs in preclinical studies, a 2025 FDA pledge to make animal studies the exception, and a September 2026 rule that would replace "animal tests" with "nonclinical tests" in drug regulations, while identifying validation standardization, the high cost of head-to-head comparisons and research-culture inertia as the main bottlenecks.
This article traces how animal-testing alternatives known as NAMs—organ chips, organoids and computational simulations—moved from a lung-on-a-chip paper that Science asked to be backed up with mouse experiments, to the 2022 FDA Modernization Act 2.0 authorizing NAMs in preclinical studies, a 2025 FDA pledge to make animal studies the exception, and a September 2026 rule that would replace "animal tests" with "nonclinical tests" in drug regulations, while identifying validation standardization, the high cost of head-to-head comparisons and research-culture inertia as the main bottlenecks.
This article traces how animal-testing alternatives known as NAMs—organ chips, organoids and computational simulations—moved from a lung-on-a-chip paper that Science asked to be backed up with mouse experiments, to the 2022 FDA Modernization Act 2.0 authorizing NAMs in preclinical studies, a 2025 FDA pledge to make animal studies the exception, and a September 2026 rule that would replace "animal tests" with "nonclinical tests" in drug regulations, while identifying validation standardization, the high cost of head-to-head comparisons and research-culture inertia as the main bottlenecks.
This article traces how animal-testing alternatives known as NAMs—organ chips, organoids and computational simulations—moved from a lung-on-a-chip paper that Science asked to be backed up with mouse experiments, to the 2022 FDA Modernization Act 2.0 authorizing NAMs in preclinical studies, a 2025 FDA pledge to make animal studies the exception, and a September 2026 rule that would replace "animal tests" with "nonclinical tests" in drug regulations, while identifying validation standardization, the high cost of head-to-head comparisons and research-culture inertia as the main bottlenecks.
MIT Technology Review MIT Technology Review's The Download newsletter reports that Anthropic announced its new molecular biology lab's first discovery: its AI agents flagged a previously uncatalogued pattern surrounding an enzyme, a pattern "reminiscent" of what led to the gene-editing technology CRISPR, but biologists pushed back, with some questioning whether merely finding the pattern amounted to a discovery and another saying his team had already discovered the same pattern, raising questions about whether Anthropic's system had learned from his conversations with Claude.
MIT Technology Review's The Download newsletter reports that Anthropic announced its new molecular biology lab's first discovery: its AI agents flagged a previously uncatalogued pattern surrounding an enzyme, a pattern "reminiscent" of what led to the gene-editing technology CRISPR, but biologists pushed back, with some questioning whether merely finding the pattern amounted to a discovery and another saying his team had already discovered the same pattern, raising questions about whether Anthropic's system had learned from his conversations with Claude.
MIT Technology Review's The Download newsletter reports that Anthropic announced its new molecular biology lab's first discovery: its AI agents flagged a previously uncatalogued pattern surrounding an enzyme, a pattern "reminiscent" of what led to the gene-editing technology CRISPR, but biologists pushed back, with some questioning whether merely finding the pattern amounted to a discovery and another saying his team had already discovered the same pattern, raising questions about whether Anthropic's system had learned from his conversations with Claude.
MIT Technology Review's The Download newsletter reports that Anthropic announced its new molecular biology lab's first discovery: its AI agents flagged a previously uncatalogued pattern surrounding an enzyme, a pattern "reminiscent" of what led to the gene-editing technology CRISPR, but biologists pushed back, with some questioning whether merely finding the pattern amounted to a discovery and another saying his team had already discovered the same pattern, raising questions about whether Anthropic's system had learned from his conversations with Claude.
World Health Organization On 22 September 2026, WHO and Knowledge Ecology International (KEI) co-organized an expert panel, "Intellectual Property and Traditional Medicine: Rethinking Access, Benefit Sharing and Data Governance," on the sidelines of the 53rd Inter Government Consultations at WIPO, where participants noted that AI and digital technologies can accelerate drug discovery based on traditional medicine knowledge while making contributions by knowledge holders harder to trace, and put forward concrete proposals including federated data governance, prior informed consent and tailored benefit-sharing arrangements, while stressing that Indigenous Peoples and local communities should be recognized as partners in stewardship and governance rather than merely as sources of knowledge.
On 22 September 2026, WHO and Knowledge Ecology International (KEI) co-organized an expert panel, "Intellectual Property and Traditional Medicine: Rethinking Access, Benefit Sharing and Data Governance," on the sidelines of the 53rd Inter Government Consultations at WIPO, where participants noted that AI and digital technologies can accelerate drug discovery based on traditional medicine knowledge while making contributions by knowledge holders harder to trace, and put forward concrete proposals including federated data governance, prior informed consent and tailored benefit-sharing arrangements, while stressing that Indigenous Peoples and local communities should be recognized as partners in stewardship and governance rather than merely as sources of knowledge.
On 22 September 2026, WHO and Knowledge Ecology International (KEI) co-organized an expert panel, "Intellectual Property and Traditional Medicine: Rethinking Access, Benefit Sharing and Data Governance," on the sidelines of the 53rd Inter Government Consultations at WIPO, where participants noted that AI and digital technologies can accelerate drug discovery based on traditional medicine knowledge while making contributions by knowledge holders harder to trace, and put forward concrete proposals including federated data governance, prior informed consent and tailored benefit-sharing arrangements, while stressing that Indigenous Peoples and local communities should be recognized as partners in stewardship and governance rather than merely as sources of knowledge.
On 22 September 2026, WHO and Knowledge Ecology International (KEI) co-organized an expert panel, "Intellectual Property and Traditional Medicine: Rethinking Access, Benefit Sharing and Data Governance," on the sidelines of the 53rd Inter Government Consultations at WIPO, where participants noted that AI and digital technologies can accelerate drug discovery based on traditional medicine knowledge while making contributions by knowledge holders harder to trace, and put forward concrete proposals including federated data governance, prior informed consent and tailored benefit-sharing arrangements, while stressing that Indigenous Peoples and local communities should be recognized as partners in stewardship and governance rather than merely as sources of knowledge.
MIT Technology Review This sponsored article, provided by HPE and not written by MIT Technology Review's editorial staff, argues that as AI moves from isolated pilots into production portfolios (assistants, retrieval-and-knowledge systems, agentic applications), a consumption-only approach turns AI spending into a hard-to-forecast variable monthly line item, so enterprises should assess workload by workload the 'crossover point' at which sustained use makes owning and operating capacity potentially more economical than buying one request at a time, while stressing that the capital decision is only half the equation and that adoption, governance, and continued expansion of high-value use cases are needed to keep that capacity productive.
This sponsored article, provided by HPE and not written by MIT Technology Review's editorial staff, argues that as AI moves from isolated pilots into production portfolios (assistants, retrieval-and-knowledge systems, agentic applications), a consumption-only approach turns AI spending into a hard-to-forecast variable monthly line item, so enterprises should assess workload by workload the 'crossover point' at which sustained use makes owning and operating capacity potentially more economical than buying one request at a time, while stressing that the capital decision is only half the equation and that adoption, governance, and continued expansion of high-value use cases are needed to keep that capacity productive.
This sponsored article, provided by HPE and not written by MIT Technology Review's editorial staff, argues that as AI moves from isolated pilots into production portfolios (assistants, retrieval-and-knowledge systems, agentic applications), a consumption-only approach turns AI spending into a hard-to-forecast variable monthly line item, so enterprises should assess workload by workload the 'crossover point' at which sustained use makes owning and operating capacity potentially more economical than buying one request at a time, while stressing that the capital decision is only half the equation and that adoption, governance, and continued expansion of high-value use cases are needed to keep that capacity productive.
This sponsored article, provided by HPE and not written by MIT Technology Review's editorial staff, argues that as AI moves from isolated pilots into production portfolios (assistants, retrieval-and-knowledge systems, agentic applications), a consumption-only approach turns AI spending into a hard-to-forecast variable monthly line item, so enterprises should assess workload by workload the 'crossover point' at which sustained use makes owning and operating capacity potentially more economical than buying one request at a time, while stressing that the capital decision is only half the equation and that adoption, governance, and continued expansion of high-value use cases are needed to keep that capacity productive.
OpenAI News The announcement introduces GPT-6.1 Sol, described as offering near-Astra intelligence for coding, computer use, and professional work at one-fifth of Astra's standard API input and output token prices.
The announcement introduces GPT-6.1 Sol, described as offering near-Astra intelligence for coding, computer use, and professional work at one-fifth of Astra's standard API input and output token prices.
The announcement introduces GPT-6.1 Sol, described as offering near-Astra intelligence for coding, computer use, and professional work at one-fifth of Astra's standard API input and output token prices.
The announcement introduces GPT-6.1 Sol, described as offering near-Astra intelligence for coding, computer use, and professional work at one-fifth of Astra's standard API input and output token prices.
OpenAI News At DevDay 2026, OpenAI announced more than 20 items spanning GPT-6 Astra, ChatGPT, Codex, APIs, security, and new tools for builders.
At DevDay 2026, OpenAI announced more than 20 items spanning GPT-6 Astra, ChatGPT, Codex, APIs, security, and new tools for builders.
At DevDay 2026, OpenAI announced more than 20 items spanning GPT-6 Astra, ChatGPT, Codex, APIs, security, and new tools for builders.
At DevDay 2026, OpenAI announced more than 20 items spanning GPT-6 Astra, ChatGPT, Codex, APIs, security, and new tools for builders.
MIT News - Artificial intelligence In Philosophical Perspectives, MIT's Brian Hedden and Manish Raghavan systematically evaluate major objections to algorithmic monoculture—where one algorithm makes all decisions in a domain—arguing that objections such as systematic exclusion do not hold, proving mathematically that monoculture tends to create information echo chambers that hinder exploration, and showing through a series of hiring simulations that bundling algorithms into an "ensemble" can sometimes overcome this limitation so that monoculture performs as well as or better than polyculture.
In Philosophical Perspectives, MIT's Brian Hedden and Manish Raghavan systematically evaluate major objections to algorithmic monoculture—where one algorithm makes all decisions in a domain—arguing that objections such as systematic exclusion do not hold, proving mathematically that monoculture tends to create information echo chambers that hinder exploration, and showing through a series of hiring simulations that bundling algorithms into an "ensemble" can sometimes overcome this limitation so that monoculture performs as well as or better than polyculture.
In Philosophical Perspectives, MIT's Brian Hedden and Manish Raghavan systematically evaluate major objections to algorithmic monoculture—where one algorithm makes all decisions in a domain—arguing that objections such as systematic exclusion do not hold, proving mathematically that monoculture tends to create information echo chambers that hinder exploration, and showing through a series of hiring simulations that bundling algorithms into an "ensemble" can sometimes overcome this limitation so that monoculture performs as well as or better than polyculture.
In Philosophical Perspectives, MIT's Brian Hedden and Manish Raghavan systematically evaluate major objections to algorithmic monoculture—where one algorithm makes all decisions in a domain—arguing that objections such as systematic exclusion do not hold, proving mathematically that monoculture tends to create information echo chambers that hinder exploration, and showing through a series of hiring simulations that bundling algorithms into an "ensemble" can sometimes overcome this limitation so that monoculture performs as well as or better than polyculture.
MIT News - Artificial intelligence In her new book 'Artificial Intimacy: Who We Become When We Talk to Machines,' MIT professor of the social studies of science and technology Sherry Turkle draws on surveyed evidence and new interview research to examine chatbots across the stages of human life, concluding that chatbot use, though often felt as a short-term salve, is broadly detrimental to human development and social connectivity, and calling for a pushback movement akin to those against phones in schools and youth social media use.
In her new book 'Artificial Intimacy: Who We Become When We Talk to Machines,' MIT professor of the social studies of science and technology Sherry Turkle draws on surveyed evidence and new interview research to examine chatbots across the stages of human life, concluding that chatbot use, though often felt as a short-term salve, is broadly detrimental to human development and social connectivity, and calling for a pushback movement akin to those against phones in schools and youth social media use.
In her new book 'Artificial Intimacy: Who We Become When We Talk to Machines,' MIT professor of the social studies of science and technology Sherry Turkle draws on surveyed evidence and new interview research to examine chatbots across the stages of human life, concluding that chatbot use, though often felt as a short-term salve, is broadly detrimental to human development and social connectivity, and calling for a pushback movement akin to those against phones in schools and youth social media use.
In her new book 'Artificial Intimacy: Who We Become When We Talk to Machines,' MIT professor of the social studies of science and technology Sherry Turkle draws on surveyed evidence and new interview research to examine chatbots across the stages of human life, concluding that chatbot use, though often felt as a short-term salve, is broadly detrimental to human development and social connectivity, and calling for a pushback movement akin to those against phones in schools and youth social media use.
bioRxiv The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
Microsystems & Nanoengineering Researchers developed a flexible wireless wearable stethoscope based on a 25-element circular aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array that achieves a packaged sensitivity of −167.5 dB, an operating bandwidth of 10 Hz–10 kHz, and a frequency-response flatness of ±0.5 dB; across multiple participants it acquired heart sounds at five standard auscultation sites with temporal correspondence to reference ECG and chest-motion signals, tracked heart rate continuously during dynamic activities such as walking and stair climbing, and, coupled with a residual neural network, classified five respiratory states (awake, asleep, apnea, rhonchi, and wheeze) with 98.7% accuracy.
Researchers developed a flexible wireless wearable stethoscope based on a 25-element circular aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array that achieves a packaged sensitivity of −167.5 dB, an operating bandwidth of 10 Hz–10 kHz, and a frequency-response flatness of ±0.5 dB; across multiple participants it acquired heart sounds at five standard auscultation sites with temporal correspondence to reference ECG and chest-motion signals, tracked heart rate continuously during dynamic activities such as walking and stair climbing, and, coupled with a residual neural network, classified five respiratory states (awake, asleep, apnea, rhonchi, and wheeze) with 98.7% accuracy.
Researchers developed a flexible wireless wearable stethoscope based on a 25-element circular aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array that achieves a packaged sensitivity of −167.5 dB, an operating bandwidth of 10 Hz–10 kHz, and a frequency-response flatness of ±0.5 dB; across multiple participants it acquired heart sounds at five standard auscultation sites with temporal correspondence to reference ECG and chest-motion signals, tracked heart rate continuously during dynamic activities such as walking and stair climbing, and, coupled with a residual neural network, classified five respiratory states (awake, asleep, apnea, rhonchi, and wheeze) with 98.7% accuracy.
Researchers developed a flexible wireless wearable stethoscope based on a 25-element circular aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array that achieves a packaged sensitivity of −167.5 dB, an operating bandwidth of 10 Hz–10 kHz, and a frequency-response flatness of ±0.5 dB; across multiple participants it acquired heart sounds at five standard auscultation sites with temporal correspondence to reference ECG and chest-motion signals, tracked heart rate continuously during dynamic activities such as walking and stair climbing, and, coupled with a residual neural network, classified five respiratory states (awake, asleep, apnea, rhonchi, and wheeze) with 98.7% accuracy.
发表出处待核验 The study introduces PixelConfig, a differential-analysis framework that reverse-engineers Meta Pixel configurations through code-patching replays and developer-account controlled experiments, and uses Internet Archive's Wayback Machine to longitudinally compare configurations on 18K health websites against a top-10K control group from 2017 to 2024, finding that default-enabled tracking features such as automatic events and first-party cookies reached adoption rates up to 98.4%, that health websites show tracking of potentially sensitive information tied to booking medical appointments and button clicks associated with specific conditions such as erectile dysfunction, and that restriction features like Core Setup were configured on 34.3% of health websites versus 8.
The study introduces PixelConfig, a differential-analysis framework that reverse-engineers Meta Pixel configurations through code-patching replays and developer-account controlled experiments, and uses Internet Archive's Wayback Machine to longitudinally compare configurations on 18K health websites against a top-10K control group from 2017 to 2024, finding that default-enabled tracking features such as automatic events and first-party cookies reached adoption rates up to 98.4%, that health websites show tracking of potentially sensitive information tied to booking medical appointments and button clicks associated with specific conditions such as erectile dysfunction, and that restriction features like Core Setup were configured on 34.3% of health websites versus 8.
The study introduces PixelConfig, a differential-analysis framework that reverse-engineers Meta Pixel configurations through code-patching replays and developer-account controlled experiments, and uses Internet Archive's Wayback Machine to longitudinally compare configurations on 18K health websites against a top-10K control group from 2017 to 2024, finding that default-enabled tracking features such as automatic events and first-party cookies reached adoption rates up to 98.4%, that health websites show tracking of potentially sensitive information tied to booking medical appointments and button clicks associated with specific conditions such as erectile dysfunction, and that restriction features like Core Setup were configured on 34.3% of health websites versus 8.
The study introduces PixelConfig, a differential-analysis framework that reverse-engineers Meta Pixel configurations through code-patching replays and developer-account controlled experiments, and uses Internet Archive's Wayback Machine to longitudinally compare configurations on 18K health websites against a top-10K control group from 2017 to 2024, finding that default-enabled tracking features such as automatic events and first-party cookies reached adoption rates up to 98.4%, that health websites show tracking of potentially sensitive information tied to booking medical appointments and button clicks associated with specific conditions such as erectile dysfunction, and that restriction features like Core Setup were configured on 34.3% of health websites versus 8.
bioRxiv This work presents the first investigation, to the authors' knowledge, of the detectability of computationally modified sequences: the authors generate modified 16S rRNA sequences via random substitutions that pass the SILVA database quality-control inclusion criteria, and build classifiers that distinguish them from natural 16S rRNA using conserved motifs, with the best classifier achieving over 90% sensitivity and specificity on the testing set at a 5% artificial mutation rate, and one feature, gapped k-mers built from universally conserved nucleotides, conserved across all three domains of life despite relying on exact matches to patterns found in E. coli.
This work presents the first investigation, to the authors' knowledge, of the detectability of computationally modified sequences: the authors generate modified 16S rRNA sequences via random substitutions that pass the SILVA database quality-control inclusion criteria, and build classifiers that distinguish them from natural 16S rRNA using conserved motifs, with the best classifier achieving over 90% sensitivity and specificity on the testing set at a 5% artificial mutation rate, and one feature, gapped k-mers built from universally conserved nucleotides, conserved across all three domains of life despite relying on exact matches to patterns found in E. coli.
This work presents the first investigation, to the authors' knowledge, of the detectability of computationally modified sequences: the authors generate modified 16S rRNA sequences via random substitutions that pass the SILVA database quality-control inclusion criteria, and build classifiers that distinguish them from natural 16S rRNA using conserved motifs, with the best classifier achieving over 90% sensitivity and specificity on the testing set at a 5% artificial mutation rate, and one feature, gapped k-mers built from universally conserved nucleotides, conserved across all three domains of life despite relying on exact matches to patterns found in E. coli.
This work presents the first investigation, to the authors' knowledge, of the detectability of computationally modified sequences: the authors generate modified 16S rRNA sequences via random substitutions that pass the SILVA database quality-control inclusion criteria, and build classifiers that distinguish them from natural 16S rRNA using conserved motifs, with the best classifier achieving over 90% sensitivity and specificity on the testing set at a 5% artificial mutation rate, and one feature, gapped k-mers built from universally conserved nucleotides, conserved across all three domains of life despite relying on exact matches to patterns found in E. coli.
发表出处待核验 Using XMap to probe port 11434 across the full IPv4 space daily from 2025-02-14 to 2026-02-13 over 362 observed days, a Nankai University team found 152,137 cumulative exposed Ollama endpoints, with daily alive endpoints rising from 10,473 to 16,059 (+53.3%), 26.4% of IPs appearing on a single day, only 0.43%–2.90% of below-fix IPs upgrading in place across five CVE cutoffs, the top five countries/regions holding about 70% of weighted observations, and the top five ASNs all cloud or hosting providers, complemented by PTR and port-443 TLS probing of operational characteristics.
Using XMap to probe port 11434 across the full IPv4 space daily from 2025-02-14 to 2026-02-13 over 362 observed days, a Nankai University team found 152,137 cumulative exposed Ollama endpoints, with daily alive endpoints rising from 10,473 to 16,059 (+53.3%), 26.4% of IPs appearing on a single day, only 0.43%–2.90% of below-fix IPs upgrading in place across five CVE cutoffs, the top five countries/regions holding about 70% of weighted observations, and the top five ASNs all cloud or hosting providers, complemented by PTR and port-443 TLS probing of operational characteristics.
Using XMap to probe port 11434 across the full IPv4 space daily from 2025-02-14 to 2026-02-13 over 362 observed days, a Nankai University team found 152,137 cumulative exposed Ollama endpoints, with daily alive endpoints rising from 10,473 to 16,059 (+53.3%), 26.4% of IPs appearing on a single day, only 0.43%–2.90% of below-fix IPs upgrading in place across five CVE cutoffs, the top five countries/regions holding about 70% of weighted observations, and the top five ASNs all cloud or hosting providers, complemented by PTR and port-443 TLS probing of operational characteristics.
Using XMap to probe port 11434 across the full IPv4 space daily from 2025-02-14 to 2026-02-13 over 362 observed days, a Nankai University team found 152,137 cumulative exposed Ollama endpoints, with daily alive endpoints rising from 10,473 to 16,059 (+53.3%), 26.4% of IPs appearing on a single day, only 0.43%–2.90% of below-fix IPs upgrading in place across five CVE cutoffs, the top five countries/regions holding about 70% of weighted observations, and the top five ASNs all cloud or hosting providers, complemented by PTR and port-443 TLS probing of operational characteristics.
发表出处待核验 The authors present and release the IPv6 Punching Bag, a single-machine, low-memory local environment that answers ICMPv6 echo requests with per-prefix configurable response rates and address types, and use it to evaluate six dynamic target generation algorithms (6Hit, 6Sense, 6Scan, 6Tree, AddrMiner-S, and DET), finding that they only partially adhere to scanning budgets while generally respecting rate limits, that all but 6Sense fail to recognize aliased prefixes, and that 6Scan does not adapt to differing response behavior within prefixes.
The authors present and release the IPv6 Punching Bag, a single-machine, low-memory local environment that answers ICMPv6 echo requests with per-prefix configurable response rates and address types, and use it to evaluate six dynamic target generation algorithms (6Hit, 6Sense, 6Scan, 6Tree, AddrMiner-S, and DET), finding that they only partially adhere to scanning budgets while generally respecting rate limits, that all but 6Sense fail to recognize aliased prefixes, and that 6Scan does not adapt to differing response behavior within prefixes.
The authors present and release the IPv6 Punching Bag, a single-machine, low-memory local environment that answers ICMPv6 echo requests with per-prefix configurable response rates and address types, and use it to evaluate six dynamic target generation algorithms (6Hit, 6Sense, 6Scan, 6Tree, AddrMiner-S, and DET), finding that they only partially adhere to scanning budgets while generally respecting rate limits, that all but 6Sense fail to recognize aliased prefixes, and that 6Scan does not adapt to differing response behavior within prefixes.
The authors present and release the IPv6 Punching Bag, a single-machine, low-memory local environment that answers ICMPv6 echo requests with per-prefix configurable response rates and address types, and use it to evaluate six dynamic target generation algorithms (6Hit, 6Sense, 6Scan, 6Tree, AddrMiner-S, and DET), finding that they only partially adhere to scanning budgets while generally respecting rate limits, that all but 6Sense fail to recognize aliased prefixes, and that 6Scan does not adapt to differing response behavior within prefixes.
Communicable diseases intelligence (2018) Using weekly Google Trends search volumes for 2018 and 2019 compared against weekly influenza notifications from Australia's National Notifiable Disease Surveillance System (NNDSS), the study fitted four supervised regression models (elastic net, support vector regression, random forest, and feedforward neural network) independently for each state and territory except the Australian Capital Territory, for nowcast and one- and two-week-ahead predictions, finding that search volumes correlate with reported influenza rates over time, that random forest and elastic net generally performed better than the other models, that every modelled jurisdiction except the Northern Territory and Tasmania had at least two search queries with moderate to strong Pearson correlation with influenza notificatio
Using weekly Google Trends search volumes for 2018 and 2019 compared against weekly influenza notifications from Australia's National Notifiable Disease Surveillance System (NNDSS), the study fitted four supervised regression models (elastic net, support vector regression, random forest, and feedforward neural network) independently for each state and territory except the Australian Capital Territory, for nowcast and one- and two-week-ahead predictions, finding that search volumes correlate with reported influenza rates over time, that random forest and elastic net generally performed better than the other models, that every modelled jurisdiction except the Northern Territory and Tasmania had at least two search queries with moderate to strong Pearson correlation with influenza notificatio
Using weekly Google Trends search volumes for 2018 and 2019 compared against weekly influenza notifications from Australia's National Notifiable Disease Surveillance System (NNDSS), the study fitted four supervised regression models (elastic net, support vector regression, random forest, and feedforward neural network) independently for each state and territory except the Australian Capital Territory, for nowcast and one- and two-week-ahead predictions, finding that search volumes correlate with reported influenza rates over time, that random forest and elastic net generally performed better than the other models, that every modelled jurisdiction except the Northern Territory and Tasmania had at least two search queries with moderate to strong Pearson correlation with influenza notificatio
Using weekly Google Trends search volumes for 2018 and 2019 compared against weekly influenza notifications from Australia's National Notifiable Disease Surveillance System (NNDSS), the study fitted four supervised regression models (elastic net, support vector regression, random forest, and feedforward neural network) independently for each state and territory except the Australian Capital Territory, for nowcast and one- and two-week-ahead predictions, finding that search volumes correlate with reported influenza rates over time, that random forest and elastic net generally performed better than the other models, that every modelled jurisdiction except the Northern Territory and Tasmania had at least two search queries with moderate to strong Pearson correlation with influenza notificatio
Journal of Machine Learning The work introduces OptimAI, an LLM-powered multi-agent framework that takes a natural-language optimization problem through four stages—formulation, planning, solver code generation, and reflective debugging—and adds UCB-based debug scheduling to switch dynamically among candidate plans; under zero-shot prompting it reaches 88.1% accuracy on NLP4LP with GPT-4o+o1-mini and 82.3% on Optibench with DeepSeek-R1, reducing error rates by 58% and 52% over the prior best, while ablations show that removing the planner or code critic drops productivity by 5.8× and 3.1× and that enabling UCB debug scheduling adds a further 3.3× productivity gain.
The work introduces OptimAI, an LLM-powered multi-agent framework that takes a natural-language optimization problem through four stages—formulation, planning, solver code generation, and reflective debugging—and adds UCB-based debug scheduling to switch dynamically among candidate plans; under zero-shot prompting it reaches 88.1% accuracy on NLP4LP with GPT-4o+o1-mini and 82.3% on Optibench with DeepSeek-R1, reducing error rates by 58% and 52% over the prior best, while ablations show that removing the planner or code critic drops productivity by 5.8× and 3.1× and that enabling UCB debug scheduling adds a further 3.3× productivity gain.
The work introduces OptimAI, an LLM-powered multi-agent framework that takes a natural-language optimization problem through four stages—formulation, planning, solver code generation, and reflective debugging—and adds UCB-based debug scheduling to switch dynamically among candidate plans; under zero-shot prompting it reaches 88.1% accuracy on NLP4LP with GPT-4o+o1-mini and 82.3% on Optibench with DeepSeek-R1, reducing error rates by 58% and 52% over the prior best, while ablations show that removing the planner or code critic drops productivity by 5.8× and 3.1× and that enabling UCB debug scheduling adds a further 3.3× productivity gain.
The work introduces OptimAI, an LLM-powered multi-agent framework that takes a natural-language optimization problem through four stages—formulation, planning, solver code generation, and reflective debugging—and adds UCB-based debug scheduling to switch dynamically among candidate plans; under zero-shot prompting it reaches 88.1% accuracy on NLP4LP with GPT-4o+o1-mini and 82.3% on Optibench with DeepSeek-R1, reducing error rates by 58% and 52% over the prior best, while ablations show that removing the planner or code critic drops productivity by 5.8× and 3.1× and that enabling UCB debug scheduling adds a further 3.3× productivity gain.
发表出处待核验 Using a Puppeteer crawler on AWS Lambda, the authors ran a 40-day longitudinal measurement (March 13–April 21, 2026) over 55,393 trending Google queries across 19 topical categories and found that AI Overviews activate on 13.7% of queries overall (64.7% for question-form queries), that cited domains are more credible than co-displayed first-page results yet 29.8% do not appear on the first page at all, that 11.0% of 98,020 atomic claims are unsupported by the cited pages with omission as the dominant failure mode, and that at least 50.6% of cited pages carry display advertising.
Using a Puppeteer crawler on AWS Lambda, the authors ran a 40-day longitudinal measurement (March 13–April 21, 2026) over 55,393 trending Google queries across 19 topical categories and found that AI Overviews activate on 13.7% of queries overall (64.7% for question-form queries), that cited domains are more credible than co-displayed first-page results yet 29.8% do not appear on the first page at all, that 11.0% of 98,020 atomic claims are unsupported by the cited pages with omission as the dominant failure mode, and that at least 50.6% of cited pages carry display advertising.
Using a Puppeteer crawler on AWS Lambda, the authors ran a 40-day longitudinal measurement (March 13–April 21, 2026) over 55,393 trending Google queries across 19 topical categories and found that AI Overviews activate on 13.7% of queries overall (64.7% for question-form queries), that cited domains are more credible than co-displayed first-page results yet 29.8% do not appear on the first page at all, that 11.0% of 98,020 atomic claims are unsupported by the cited pages with omission as the dominant failure mode, and that at least 50.6% of cited pages carry display advertising.
Using a Puppeteer crawler on AWS Lambda, the authors ran a 40-day longitudinal measurement (March 13–April 21, 2026) over 55,393 trending Google queries across 19 topical categories and found that AI Overviews activate on 13.7% of queries overall (64.7% for question-form queries), that cited domains are more credible than co-displayed first-page results yet 29.8% do not appear on the first page at all, that 11.0% of 98,020 atomic claims are unsupported by the cited pages with omission as the dominant failure mode, and that at least 50.6% of cited pages carry display advertising.