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Terence Tao blog RSS This is a guest opinion piece by Grant Sanderson published on Terence Tao's blog, arguing that the mathematics community should more firmly define and grant academic credit to a kind of work it calls a "motivated explanation" — exposition that places definitions in the middle, may begin from a relatable but not-quite-right idea, and aims to answer "how would you think of that?" — and offering concrete institutional suggestions such as making the deliverable of a small problem a talk, enumerating unsolved exposition problems, founding journals focused on understanding, and valuing great textbook writing more in hiring and tenure.
This is a guest opinion piece by Grant Sanderson published on Terence Tao's blog, arguing that the mathematics community should more firmly define and grant academic credit to a kind of work it calls a "motivated explanation" — exposition that places definitions in the middle, may begin from a relatable but not-quite-right idea, and aims to answer "how would you think of that?" — and offering concrete institutional suggestions such as making the deliverable of a small problem a talk, enumerating unsolved exposition problems, founding journals focused on understanding, and valuing great textbook writing more in hiring and tenure.
This is a guest opinion piece by Grant Sanderson published on Terence Tao's blog, arguing that the mathematics community should more firmly define and grant academic credit to a kind of work it calls a "motivated explanation" — exposition that places definitions in the middle, may begin from a relatable but not-quite-right idea, and aims to answer "how would you think of that?" — and offering concrete institutional suggestions such as making the deliverable of a small problem a talk, enumerating unsolved exposition problems, founding journals focused on understanding, and valuing great textbook writing more in hiring and tenure.
This is a guest opinion piece by Grant Sanderson published on Terence Tao's blog, arguing that the mathematics community should more firmly define and grant academic credit to a kind of work it calls a "motivated explanation" — exposition that places definitions in the middle, may begin from a relatable but not-quite-right idea, and aims to answer "how would you think of that?" — and offering concrete institutional suggestions such as making the deliverable of a small problem a talk, enumerating unsolved exposition problems, founding journals focused on understanding, and valuing great textbook writing more in hiring and tenure.
medRxiv This study introduces GenEHR, an autoregressive generative model trained on electronic health records from millions of patients that explicitly represents irregular inter-visit time intervals via RAdix Time Encoding and combines parameter-efficient gated low-rank adaptation for supervised fine-tuning, improving prediction of a first cancer diagnosis within a five-year horizon across five large EHR cohorts and supporting risk-based screening for aggressive cancers such as pancreatic and ovarian cancer.
This study introduces GenEHR, an autoregressive generative model trained on electronic health records from millions of patients that explicitly represents irregular inter-visit time intervals via RAdix Time Encoding and combines parameter-efficient gated low-rank adaptation for supervised fine-tuning, improving prediction of a first cancer diagnosis within a five-year horizon across five large EHR cohorts and supporting risk-based screening for aggressive cancers such as pancreatic and ovarian cancer.
This study introduces GenEHR, an autoregressive generative model trained on electronic health records from millions of patients that explicitly represents irregular inter-visit time intervals via RAdix Time Encoding and combines parameter-efficient gated low-rank adaptation for supervised fine-tuning, improving prediction of a first cancer diagnosis within a five-year horizon across five large EHR cohorts and supporting risk-based screening for aggressive cancers such as pancreatic and ovarian cancer.
This study introduces GenEHR, an autoregressive generative model trained on electronic health records from millions of patients that explicitly represents irregular inter-visit time intervals via RAdix Time Encoding and combines parameter-efficient gated low-rank adaptation for supervised fine-tuning, improving prediction of a first cancer diagnosis within a five-year horizon across five large EHR cohorts and supporting risk-based screening for aggressive cancers such as pancreatic and ovarian cancer.
MedScience This article systematically reviews the clinical progress and prospects of neoantigen cancer vaccines for gastrointestinal tumors, noting that these vaccines, owing to their high specificity and strong immunogenicity, can effectively activate specific T-cell immunity and produce synergistic effects when combined with immune checkpoint inhibitors; various vaccine platforms offer distinct advantages, and clinical trials have shown encouraging potential in inducing immune responses and extending progression-free survival, while key challenges involve the accuracy of neoantigen prediction, tumor heterogeneity, and optimal treatment timing, and future directions include AI-assisted multi-omics screening, development of universal vaccines, optimization of novel delivery systems, and multimodal c
This article systematically reviews the clinical progress and prospects of neoantigen cancer vaccines for gastrointestinal tumors, noting that these vaccines, owing to their high specificity and strong immunogenicity, can effectively activate specific T-cell immunity and produce synergistic effects when combined with immune checkpoint inhibitors; various vaccine platforms offer distinct advantages, and clinical trials have shown encouraging potential in inducing immune responses and extending progression-free survival, while key challenges involve the accuracy of neoantigen prediction, tumor heterogeneity, and optimal treatment timing, and future directions include AI-assisted multi-omics screening, development of universal vaccines, optimization of novel delivery systems, and multimodal c
This article systematically reviews the clinical progress and prospects of neoantigen cancer vaccines for gastrointestinal tumors, noting that these vaccines, owing to their high specificity and strong immunogenicity, can effectively activate specific T-cell immunity and produce synergistic effects when combined with immune checkpoint inhibitors; various vaccine platforms offer distinct advantages, and clinical trials have shown encouraging potential in inducing immune responses and extending progression-free survival, while key challenges involve the accuracy of neoantigen prediction, tumor heterogeneity, and optimal treatment timing, and future directions include AI-assisted multi-omics screening, development of universal vaccines, optimization of novel delivery systems, and multimodal c
This article systematically reviews the clinical progress and prospects of neoantigen cancer vaccines for gastrointestinal tumors, noting that these vaccines, owing to their high specificity and strong immunogenicity, can effectively activate specific T-cell immunity and produce synergistic effects when combined with immune checkpoint inhibitors; various vaccine platforms offer distinct advantages, and clinical trials have shown encouraging potential in inducing immune responses and extending progression-free survival, while key challenges involve the accuracy of neoantigen prediction, tumor heterogeneity, and optimal treatment timing, and future directions include AI-assisted multi-omics screening, development of universal vaccines, optimization of novel delivery systems, and multimodal c
Google Research The work introduces and open-sources MilleMiglia, a C++ instance generator serialized with Protocol Buffers that synthesizes middle-mile logistics networks from statistical distributions over spatial placement, demand, and vehicle rotations, embedding hard constraints such as fixed schedules, distribution-center throughput limits, and cross-vehicle synchronization into a single data format, thereby offering reproducible benchmarks from small academic toy problems to continent-wide industrial scale while preserving corporate privacy.
The work introduces and open-sources MilleMiglia, a C++ instance generator serialized with Protocol Buffers that synthesizes middle-mile logistics networks from statistical distributions over spatial placement, demand, and vehicle rotations, embedding hard constraints such as fixed schedules, distribution-center throughput limits, and cross-vehicle synchronization into a single data format, thereby offering reproducible benchmarks from small academic toy problems to continent-wide industrial scale while preserving corporate privacy.
The work introduces and open-sources MilleMiglia, a C++ instance generator serialized with Protocol Buffers that synthesizes middle-mile logistics networks from statistical distributions over spatial placement, demand, and vehicle rotations, embedding hard constraints such as fixed schedules, distribution-center throughput limits, and cross-vehicle synchronization into a single data format, thereby offering reproducible benchmarks from small academic toy problems to continent-wide industrial scale while preserving corporate privacy.
The work introduces and open-sources MilleMiglia, a C++ instance generator serialized with Protocol Buffers that synthesizes middle-mile logistics networks from statistical distributions over spatial placement, demand, and vehicle rotations, embedding hard constraints such as fixed schedules, distribution-center throughput limits, and cross-vehicle synchronization into a single data format, thereby offering reproducible benchmarks from small academic toy problems to continent-wide industrial scale while preserving corporate privacy.