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.
bioRxiv The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
Journal of the Physical Society of Japan Under a controlled protocol that fixes the Boltzmann machine model, simulated-annealing sampler, and learning-rate design, the study compares Ising ({−1,+1}) and QUBO ({0,1}) variable encodings, exploits the identity that the Fisher information matrix equals the covariance of sufficient statistics to visualize empirical moments, and finds that QUBO induces larger cross terms between first- and second-order statistics, creating more small-eigenvalue directions and lowering spectral entropy, which explains slower convergence under stochastic gradient descent, whereas natural gradient descent, which rescales updates by the Fisher information matrix metric, achieves similar convergence across encodings due to reparameterization invariance.
Under a controlled protocol that fixes the Boltzmann machine model, simulated-annealing sampler, and learning-rate design, the study compares Ising ({−1,+1}) and QUBO ({0,1}) variable encodings, exploits the identity that the Fisher information matrix equals the covariance of sufficient statistics to visualize empirical moments, and finds that QUBO induces larger cross terms between first- and second-order statistics, creating more small-eigenvalue directions and lowering spectral entropy, which explains slower convergence under stochastic gradient descent, whereas natural gradient descent, which rescales updates by the Fisher information matrix metric, achieves similar convergence across encodings due to reparameterization invariance.
Under a controlled protocol that fixes the Boltzmann machine model, simulated-annealing sampler, and learning-rate design, the study compares Ising ({−1,+1}) and QUBO ({0,1}) variable encodings, exploits the identity that the Fisher information matrix equals the covariance of sufficient statistics to visualize empirical moments, and finds that QUBO induces larger cross terms between first- and second-order statistics, creating more small-eigenvalue directions and lowering spectral entropy, which explains slower convergence under stochastic gradient descent, whereas natural gradient descent, which rescales updates by the Fisher information matrix metric, achieves similar convergence across encodings due to reparameterization invariance.
Under a controlled protocol that fixes the Boltzmann machine model, simulated-annealing sampler, and learning-rate design, the study compares Ising ({−1,+1}) and QUBO ({0,1}) variable encodings, exploits the identity that the Fisher information matrix equals the covariance of sufficient statistics to visualize empirical moments, and finds that QUBO induces larger cross terms between first- and second-order statistics, creating more small-eigenvalue directions and lowering spectral entropy, which explains slower convergence under stochastic gradient descent, whereas natural gradient descent, which rescales updates by the Fisher information matrix metric, achieves similar convergence across encodings due to reparameterization invariance.
发表出处待核验 The study introduces a privacy measurement suite for synthetic network traffic spanning membership inference, data extraction, and network-specific attacks on identifiers, attributes, and topology, and evaluates four representative generators (NetShare, NetDiffusion, TrafficLLM, NetSSM) across five datasets, finding that even with black-box-only attackers and minimal training, membership inference reaches up to 0.88 TPR at FPR≤0.01 and up to 100% of network identifiers can be recovered, while anonymization and DP noise each cover only part of the risk at a utility cost.
The study introduces a privacy measurement suite for synthetic network traffic spanning membership inference, data extraction, and network-specific attacks on identifiers, attributes, and topology, and evaluates four representative generators (NetShare, NetDiffusion, TrafficLLM, NetSSM) across five datasets, finding that even with black-box-only attackers and minimal training, membership inference reaches up to 0.88 TPR at FPR≤0.01 and up to 100% of network identifiers can be recovered, while anonymization and DP noise each cover only part of the risk at a utility cost.
The study introduces a privacy measurement suite for synthetic network traffic spanning membership inference, data extraction, and network-specific attacks on identifiers, attributes, and topology, and evaluates four representative generators (NetShare, NetDiffusion, TrafficLLM, NetSSM) across five datasets, finding that even with black-box-only attackers and minimal training, membership inference reaches up to 0.88 TPR at FPR≤0.01 and up to 100% of network identifiers can be recovered, while anonymization and DP noise each cover only part of the risk at a utility cost.
The study introduces a privacy measurement suite for synthetic network traffic spanning membership inference, data extraction, and network-specific attacks on identifiers, attributes, and topology, and evaluates four representative generators (NetShare, NetDiffusion, TrafficLLM, NetSSM) across five datasets, finding that even with black-box-only attackers and minimal training, membership inference reaches up to 0.88 TPR at FPR≤0.01 and up to 100% of network identifiers can be recovered, while anonymization and DP noise each cover only part of the risk at a utility cost.
bioRxiv The authors developed SpatialTRACE, comprising graph- and image-based models: SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict crypt-villus and epithelial-distance axis coordinates and to identify Peyer's patches in mouse small-intestine sections using as few as 10 annotated training villi, while SpatialTRACE-Image, a multiscale vision transformer that learns from the coordinate and region predictions generated by SpatialTRACE-Graph, predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone and was applied to immunofluorescence images to map antigen-specific P14 CD8 T cells responding to acute systemic LCMV Armstrong infection in the small intestine, showing that a retinoic acid receptor inhibitor-treated
The authors developed SpatialTRACE, comprising graph- and image-based models: SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict crypt-villus and epithelial-distance axis coordinates and to identify Peyer's patches in mouse small-intestine sections using as few as 10 annotated training villi, while SpatialTRACE-Image, a multiscale vision transformer that learns from the coordinate and region predictions generated by SpatialTRACE-Graph, predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone and was applied to immunofluorescence images to map antigen-specific P14 CD8 T cells responding to acute systemic LCMV Armstrong infection in the small intestine, showing that a retinoic acid receptor inhibitor-treated
The authors developed SpatialTRACE, comprising graph- and image-based models: SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict crypt-villus and epithelial-distance axis coordinates and to identify Peyer's patches in mouse small-intestine sections using as few as 10 annotated training villi, while SpatialTRACE-Image, a multiscale vision transformer that learns from the coordinate and region predictions generated by SpatialTRACE-Graph, predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone and was applied to immunofluorescence images to map antigen-specific P14 CD8 T cells responding to acute systemic LCMV Armstrong infection in the small intestine, showing that a retinoic acid receptor inhibitor-treated
The authors developed SpatialTRACE, comprising graph- and image-based models: SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict crypt-villus and epithelial-distance axis coordinates and to identify Peyer's patches in mouse small-intestine sections using as few as 10 annotated training villi, while SpatialTRACE-Image, a multiscale vision transformer that learns from the coordinate and region predictions generated by SpatialTRACE-Graph, predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone and was applied to immunofluorescence images to map antigen-specific P14 CD8 T cells responding to acute systemic LCMV Armstrong infection in the small intestine, showing that a retinoic acid receptor inhibitor-treated
bioRxiv The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
发表出处待核验 The authors introduce GateScope, a lightweight black-box auditing framework that uses only public APIs to evaluate LLM API gateways along response content, multi-turn conversation consistency, billing accuracy, and latency characteristics; controlled validation on official endpoints yields an average F1 of 0.968±0.085 across 24 models, and auditing 10 commercial gateways reveals identification rates as low as 13.09% for gpt-5, degraded multi-turn memory checkpoints, a 62.8% billing gap for o*ey on gpt-4o, and markedly higher latency variation for b*ie.
The authors introduce GateScope, a lightweight black-box auditing framework that uses only public APIs to evaluate LLM API gateways along response content, multi-turn conversation consistency, billing accuracy, and latency characteristics; controlled validation on official endpoints yields an average F1 of 0.968±0.085 across 24 models, and auditing 10 commercial gateways reveals identification rates as low as 13.09% for gpt-5, degraded multi-turn memory checkpoints, a 62.8% billing gap for o*ey on gpt-4o, and markedly higher latency variation for b*ie.
The authors introduce GateScope, a lightweight black-box auditing framework that uses only public APIs to evaluate LLM API gateways along response content, multi-turn conversation consistency, billing accuracy, and latency characteristics; controlled validation on official endpoints yields an average F1 of 0.968±0.085 across 24 models, and auditing 10 commercial gateways reveals identification rates as low as 13.09% for gpt-5, degraded multi-turn memory checkpoints, a 62.8% billing gap for o*ey on gpt-4o, and markedly higher latency variation for b*ie.
The authors introduce GateScope, a lightweight black-box auditing framework that uses only public APIs to evaluate LLM API gateways along response content, multi-turn conversation consistency, billing accuracy, and latency characteristics; controlled validation on official endpoints yields an average F1 of 0.968±0.085 across 24 models, and auditing 10 commercial gateways reveals identification rates as low as 13.09% for gpt-5, degraded multi-turn memory checkpoints, a 62.8% billing gap for o*ey on gpt-4o, and markedly higher latency variation for b*ie.
Claude 产品博客 Asana Chief Product Officer Arnab Bose describes how Asana runs AI agents as "AI teammates" inside its existing Work Graph: agents are built around roles (such as content writer, insights analyst, project manager, work intake specialist, campaign analyst, or campaign coordinator) with pre-built skills and integrations, distinct identities and controlled permissions, effective access bounded by the permissions of the person who triggers them, and coachable shared memory in which only admins and editors can commit feedback to permanent memory; the piece gives three deployments—Slack channel questions turned into Asana tasks and routed to product backlog or enablement documentation updates, an At-Risk Renewal agent producing a daily global renewal-risk digest for executives and regional leade
Asana Chief Product Officer Arnab Bose describes how Asana runs AI agents as "AI teammates" inside its existing Work Graph: agents are built around roles (such as content writer, insights analyst, project manager, work intake specialist, campaign analyst, or campaign coordinator) with pre-built skills and integrations, distinct identities and controlled permissions, effective access bounded by the permissions of the person who triggers them, and coachable shared memory in which only admins and editors can commit feedback to permanent memory; the piece gives three deployments—Slack channel questions turned into Asana tasks and routed to product backlog or enablement documentation updates, an At-Risk Renewal agent producing a daily global renewal-risk digest for executives and regional leade
Asana Chief Product Officer Arnab Bose describes how Asana runs AI agents as "AI teammates" inside its existing Work Graph: agents are built around roles (such as content writer, insights analyst, project manager, work intake specialist, campaign analyst, or campaign coordinator) with pre-built skills and integrations, distinct identities and controlled permissions, effective access bounded by the permissions of the person who triggers them, and coachable shared memory in which only admins and editors can commit feedback to permanent memory; the piece gives three deployments—Slack channel questions turned into Asana tasks and routed to product backlog or enablement documentation updates, an At-Risk Renewal agent producing a daily global renewal-risk digest for executives and regional leade
Asana Chief Product Officer Arnab Bose describes how Asana runs AI agents as "AI teammates" inside its existing Work Graph: agents are built around roles (such as content writer, insights analyst, project manager, work intake specialist, campaign analyst, or campaign coordinator) with pre-built skills and integrations, distinct identities and controlled permissions, effective access bounded by the permissions of the person who triggers them, and coachable shared memory in which only admins and editors can commit feedback to permanent memory; the piece gives three deployments—Slack channel questions turned into Asana tasks and routed to product backlog or enablement documentation updates, an At-Risk Renewal agent producing a daily global renewal-risk digest for executives and regional leade
bioRxiv On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, the work shows that marginal split-conformal calibration reached 0.9000 empirical coverage against a nominal 0.90 while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818, and that class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size (under 3%), making set size at matched coverage interpretable as a property of the subject and separating subjects into confident, boundary, ambiguous and unresolved strata, with the proportion independently flagged as label-ambiguous by a structural-MRI model rising monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18).
On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, the work shows that marginal split-conformal calibration reached 0.9000 empirical coverage against a nominal 0.90 while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818, and that class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size (under 3%), making set size at matched coverage interpretable as a property of the subject and separating subjects into confident, boundary, ambiguous and unresolved strata, with the proportion independently flagged as label-ambiguous by a structural-MRI model rising monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18).
On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, the work shows that marginal split-conformal calibration reached 0.9000 empirical coverage against a nominal 0.90 while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818, and that class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size (under 3%), making set size at matched coverage interpretable as a property of the subject and separating subjects into confident, boundary, ambiguous and unresolved strata, with the proportion independently flagged as label-ambiguous by a structural-MRI model rising monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18).
On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, the work shows that marginal split-conformal calibration reached 0.9000 empirical coverage against a nominal 0.90 while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818, and that class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size (under 3%), making set size at matched coverage interpretable as a property of the subject and separating subjects into confident, boundary, ambiguous and unresolved strata, with the proportion independently flagged as label-ambiguous by a structural-MRI model rising monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18).
bioRxiv The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
bioRxiv The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.
The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.
The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.
The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.
发表出处待核验 The authors built an automated detection system combining lexical URL filtering, dynamic rendering, OCR-based extraction, and content classification, applied it to 6,094,475 public URLs collected from VirusTotal, URLScan.io, Hybrid Analysis, the Wayback Machine, and RedHunt paste sites, and identified 12,331 potential exposures across authentication, financial, personal, and document-related categories, including 26 live password-reset links, 83 API keys, 12 publicly accessible e-signature workflows, 7 fully visible 2FA backup codes, and 62 JWTs lacking expiration constraints.
The authors built an automated detection system combining lexical URL filtering, dynamic rendering, OCR-based extraction, and content classification, applied it to 6,094,475 public URLs collected from VirusTotal, URLScan.io, Hybrid Analysis, the Wayback Machine, and RedHunt paste sites, and identified 12,331 potential exposures across authentication, financial, personal, and document-related categories, including 26 live password-reset links, 83 API keys, 12 publicly accessible e-signature workflows, 7 fully visible 2FA backup codes, and 62 JWTs lacking expiration constraints.
The authors built an automated detection system combining lexical URL filtering, dynamic rendering, OCR-based extraction, and content classification, applied it to 6,094,475 public URLs collected from VirusTotal, URLScan.io, Hybrid Analysis, the Wayback Machine, and RedHunt paste sites, and identified 12,331 potential exposures across authentication, financial, personal, and document-related categories, including 26 live password-reset links, 83 API keys, 12 publicly accessible e-signature workflows, 7 fully visible 2FA backup codes, and 62 JWTs lacking expiration constraints.
The authors built an automated detection system combining lexical URL filtering, dynamic rendering, OCR-based extraction, and content classification, applied it to 6,094,475 public URLs collected from VirusTotal, URLScan.io, Hybrid Analysis, the Wayback Machine, and RedHunt paste sites, and identified 12,331 potential exposures across authentication, financial, personal, and document-related categories, including 26 live password-reset links, 83 API keys, 12 publicly accessible e-signature workflows, 7 fully visible 2FA backup codes, and 62 JWTs lacking expiration constraints.
CSIAM Transactions on Applied Mathematics The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
bioRxiv The work introduces DeepFisFis, a waveform-based neural network that classifies consecutive 5 ms audio segments to detect mouse ultrasonic vocalizations (USVs) while they are being produced, processing each segment in approximately 2.5 ms and thus faster than the incoming audio stream; in a deployed closed-loop system, detections triggered an external stimulus, demonstrating online control of ongoing vocal behaviour, and event-triggered acquisition preserved more than 99% of vocalization time while retaining only approximately 22% of the continuous recording.
The work introduces DeepFisFis, a waveform-based neural network that classifies consecutive 5 ms audio segments to detect mouse ultrasonic vocalizations (USVs) while they are being produced, processing each segment in approximately 2.5 ms and thus faster than the incoming audio stream; in a deployed closed-loop system, detections triggered an external stimulus, demonstrating online control of ongoing vocal behaviour, and event-triggered acquisition preserved more than 99% of vocalization time while retaining only approximately 22% of the continuous recording.
The work introduces DeepFisFis, a waveform-based neural network that classifies consecutive 5 ms audio segments to detect mouse ultrasonic vocalizations (USVs) while they are being produced, processing each segment in approximately 2.5 ms and thus faster than the incoming audio stream; in a deployed closed-loop system, detections triggered an external stimulus, demonstrating online control of ongoing vocal behaviour, and event-triggered acquisition preserved more than 99% of vocalization time while retaining only approximately 22% of the continuous recording.
The work introduces DeepFisFis, a waveform-based neural network that classifies consecutive 5 ms audio segments to detect mouse ultrasonic vocalizations (USVs) while they are being produced, processing each segment in approximately 2.5 ms and thus faster than the incoming audio stream; in a deployed closed-loop system, detections triggered an external stimulus, demonstrating online control of ongoing vocal behaviour, and event-triggered acquisition preserved more than 99% of vocalization time while retaining only approximately 22% of the continuous recording.
MIT Technology Review An MIT Technology Review investigation documented over a thousand people who moved through areas watched by surveillance towers along the southern US border without being reached or apprehended and who ultimately died there, some under newly installed AI-powered towers designed to spot people automatically, revealing repeated failures of the virtual wall's basic security promise.
An MIT Technology Review investigation documented over a thousand people who moved through areas watched by surveillance towers along the southern US border without being reached or apprehended and who ultimately died there, some under newly installed AI-powered towers designed to spot people automatically, revealing repeated failures of the virtual wall's basic security promise.
An MIT Technology Review investigation documented over a thousand people who moved through areas watched by surveillance towers along the southern US border without being reached or apprehended and who ultimately died there, some under newly installed AI-powered towers designed to spot people automatically, revealing repeated failures of the virtual wall's basic security promise.
An MIT Technology Review investigation documented over a thousand people who moved through areas watched by surveillance towers along the southern US border without being reached or apprehended and who ultimately died there, some under newly installed AI-powered towers designed to spot people automatically, revealing repeated failures of the virtual wall's basic security promise.
Microsoft Research Microsoft Research Asia – Singapore reviews its first year since opening on July 24, 2025: it pursues a "research-to-impact flywheel" across four pillars (next-generation AI models and agentic systems, domain-specific AI for real-world impact, AI-native research practices, and ecosystem and talent development), launched nine new projects with the National University of Singapore and Nanyang Technological University, signed a five-year Framework Research Agreement with NUS, reached more than 300 students through its summer school since 2025, and saw its first Industrial Postgraduate Programme student Qiming Huang have a RobotSeg paper selected for an oral presentation at CVPR 2026, with the second year focused on scaling what works.
Microsoft Research Asia – Singapore reviews its first year since opening on July 24, 2025: it pursues a "research-to-impact flywheel" across four pillars (next-generation AI models and agentic systems, domain-specific AI for real-world impact, AI-native research practices, and ecosystem and talent development), launched nine new projects with the National University of Singapore and Nanyang Technological University, signed a five-year Framework Research Agreement with NUS, reached more than 300 students through its summer school since 2025, and saw its first Industrial Postgraduate Programme student Qiming Huang have a RobotSeg paper selected for an oral presentation at CVPR 2026, with the second year focused on scaling what works.
Microsoft Research Asia – Singapore reviews its first year since opening on July 24, 2025: it pursues a "research-to-impact flywheel" across four pillars (next-generation AI models and agentic systems, domain-specific AI for real-world impact, AI-native research practices, and ecosystem and talent development), launched nine new projects with the National University of Singapore and Nanyang Technological University, signed a five-year Framework Research Agreement with NUS, reached more than 300 students through its summer school since 2025, and saw its first Industrial Postgraduate Programme student Qiming Huang have a RobotSeg paper selected for an oral presentation at CVPR 2026, with the second year focused on scaling what works.
Microsoft Research Asia – Singapore reviews its first year since opening on July 24, 2025: it pursues a "research-to-impact flywheel" across four pillars (next-generation AI models and agentic systems, domain-specific AI for real-world impact, AI-native research practices, and ecosystem and talent development), launched nine new projects with the National University of Singapore and Nanyang Technological University, signed a five-year Framework Research Agreement with NUS, reached more than 300 students through its summer school since 2025, and saw its first Industrial Postgraduate Programme student Qiming Huang have a RobotSeg paper selected for an oral presentation at CVPR 2026, with the second year focused on scaling what works.