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Relatório técnico com análise comparativa detalhada entre as metas, métricas e indicadores comuns estabelecidos nos Planos Acadêmicos Institucionais (PAI) do Ciclo V …

Relatório técnico com análise comparativa detalhada entre as metas, métricas e indicadores comuns estabelecidos nos Planos Acadêmicos Institucionais (PAI) do Ciclo V e os 10 Objetivos Estratégicos (OE) definidos no Planejamento Estratégico 2024 da Pró-Reitoria de Cultura e Extensão (PRCEU). A abrangência do estudo contemplou a totalidade das Unidades de Ensino e Pesquisa, Institutos Especializados e Museus Estatutários da Universidade de São Paulo, estruturando um diagnóstico robusto capaz de subsidiar o acompanhamento estratégico e sua governança em nível institucional.

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🌟 Summary YOLO prediction can now load the next batch of images while CUDA processes the current one, alongside performance, export, dataset, and Ultralytics Platform improvements. No model architectu…

🌟 Summary YOLO prediction can now load the next batch of images while CUDA processes the current one, alongside performance, export, dataset, and Ultralytics Platform improvements. No model architectures or weights changed. 📊 Key Changes 🚀 Faster CUDA prediction: For multi-batch, image-only detection using PyTorch weights, prediction prefetches the next batch on a worker thread while the GPU handles the current batch. Other devices, tasks, backends, and mixed image/video sources keep the existing behavior. ⚡ Faster training data preparation: Optimized copy-paste augmentation and semantic-mask target generation while preserving their expected outputs. 🧠 Lower memory use during TensorFlow INT8 export: Calibration images are collected into a preallocated array instead of building several large intermediate copies. ☁️ More Platform annotation and dataset features: Added hosted text-prompted SAM 3 and SAM 3.1 options, expanded cloud-storage imports to 500,000 objects, and enabled image search by annotated class name. 🛠️ More robust workflows: Fixed Windows OpenVINO crashes and GPU ONNX inference silently falling back to CPU; improved dataset archive and .yml support, OOM recovery, and tracking when source files share a name. 🎯 Purpose & Impact ⏱️ Better GPU utilization: Overlapping image loading with inference can reduce idle time during eligible CUDA prediction runs. It complements existing work that speeds up decoding within a batch. 📈 Smoother data workflows: Faster augmentation and reduced calibration memory use can help improve training and export efficiency, especially on constrained systems. 🔍 Easier annotation and dataset management: Text-prompted SAM, larger cloud imports, and class-name search make it simpler to label and browse larger datasets. ✅ Fewer surprises: Export, Windows inference, archive loading, tracking, and training recovery fixes improve reliability without changing model behavior. What's Changed Reduce peak memory during INT8 TensorFlow calibration by @cainiao33 in https://github.com/ultralytics/ultralytics/pull/26288 Point the validator at the rebuilt dataloader after OOM auto-reduce by @cainiao33 in https://github.com/ultralytics/ultralytics/pull/26296 Simplify tar download handling and remove redundant regression fixtures by @cainiao33 in https://github.com/ultralytics/ultralytics/pull/26287 fix incorrect dataset in OBB loss error message by @lmycross in https://github.com/ultralytics/ultralytics/pull/26314 Fix Windows OpenVINO crashes and CI runtime warnings by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26317 Remove headless-unsafe destroyAllWindows from Streamlit Inference by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/26320 Annotate CI test failures and xdist worker crashes on GitHub Actions by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/26318 Speed up overlap_mask=False semantic mask targets with a single min reduction by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/26315 Speed up CopyPaste with a masked cv2.copyTo by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/26310 Discover .yml dataset YAMLs in directories and archives by @Nikhi00718 in https://github.com/ultralytics/ultralytics/pull/26323 Accept list and tuple boxes in xyxy2xywh and xywh2xyxy by @MohammadHijjawi97 in https://github.com/ultralytics/ultralytics/pull/26322 Reset the tracker between sources that share a filename by @aswanth-07 in https://github.com/ultralytics/ultralytics/pull/26312 Document text-prompted SAM, 500K-object cloud imports, endpoint warm-up states and class-name image search by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/26311 Extract .tar.xz, .tar.bz2 and local .tgz dataset archives by @Vaishnavi220506 in https://github.com/ultralytics/ultralytics/pull/26306 Return None for empty image files in imread_unicode by @cainiao33 in https://github.com/ultralytics/ultralytics/pull/26304 Document segments2boxes inputs as (N, 2) point arrays by @MohammadHijjawi97 in https://github.com/ultralytics/ultralytics/pull/26321 Close the replaced train dataloader on OOM auto-reduce by @cainiao33 in https://github.com/ultralytics/ultralytics/pull/26302 ultralytics 8.4.162 Prefetch the next image batch during CUDA prediction by @JESUSROYETH in https://github.com/ultralytics/ultralytics/pull/26319 Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.161...v8.4.162

Human serum ERM-DA470k/IFCC is a reference material certified for 13 serum proteins: alpha-2-macroglobulin, alpha-1-acid glycoprotein, alpha-1-antitrypsin, human serum albumin, beta-2-microglobulin, c…

Human serum ERM-DA470k/IFCC is a reference material certified for 13 serum proteins: alpha-2-macroglobulin, alpha-1-acid glycoprotein, alpha-1-antitrypsin, human serum albumin, beta-2-microglobulin, complement C3, complement C4, haptoglobin, transferrin, transthyretin, and immunoglobulin A, G and M. The material originates from blood collected from volunteer healthy donors. The material is primary intended to be used for the calibration of immunoassay-based in-vitro diagnostic devices. In this SOP, the reference material was used as a quality control sample for quantification of inflammatory proteins listed in Table 1 using UHPLC/SRM-MS assay.

Neuroscience increasingly depends on the interplay of code, data, and computationalenvironments, yet the record of how they were used together is often incomplete, scatteredacross repositories, wikis,…

Neuroscience increasingly depends on the interplay of code, data, and computationalenvironments, yet the record of how they were used together is often incomplete, scatteredacross repositories, wikis, and notebooks, or lost entirely. This fragmentation undermines rigor,reproducibility, reusability, and efficiency in BRAIN Initiative pipelines that routinely spanmultiple institutions, archives, and compute platforms. Existing frameworks such as FAIR andFAIR4RS govern discovery and interoperability of digital objects, but do not specify howresearch objects should be structured and managed so they can be re-executed, extended,and audited. The community lacks a shared vocabulary for this operational layer. Building on the YODA and VAMP traditions from neuroimaging, and on patterns that haveindependently converged across geophysics, genomics, statistics, and neuroscience overthree decades, we formalize seven principles a research object should satisfy:Self-containment, Tracking, Actionability, Modularity, Portability, Ephemerality, andDistributability, collectively STAMPED. Each spans a spectrum from practical minimum toaspirational ideal, so adoption is non-prescriptive and incremental. Formal LinkML schemas, aninteractive compliance checklist, and the curated collection of examples are provided asenabling tools to this end. We demonstrate STAMPED through two major neuroscience pipelines. OpenNeuroDerivativesreorganized derivative neuroimaging datasets so they exist as independent Ephemeral unitsthat reference raw inputs as subdatasets rather than nesting under them, removing an upwarddependency that previously violated Self-containment, Modularity, and Portability. DANDICompute, utilizing the Allen Institute for Neural Dynamics electrophysiology pipeline, packagesspike-sorting outputs into nested BIDS-derivative units in which each leaf contains the exactcode, runtime logs, outputs, and provenance metadata needed to re-execute the analysis,satisfying STAMPED end-to-end. These adoptions show that STAMPED provides a tool-agnostic, incrementally adoptablevocabulary that lets researchers, reviewers, collaborators, and emerging AI agents evaluate andimprove the operational maturity of computational neuroscience. By making research objectsre-executable and inspectable by construction, STAMPED converts reproducibility from anaspiration into a measurable property of everyday neuroscience practice.

Ushbu maqolada, sun’iy intelektdan foydalanish natijasida yetkazilgan zarar uchun fuqorolik-huquqiy javobgarlikning umumiy asoslari haqida fikr va mulohazalar yuritiladi. Sun’iy intellekt …

Ushbu maqolada, sun’iy intelektdan foydalanish natijasida yetkazilgan zarar uchun fuqorolik-huquqiy javobgarlikning umumiy asoslari haqida fikr va mulohazalar yuritiladi. Sun’iy intellekt texnologiyalarining jadal rivojlanishi zamonaviy ijtimoiy, iqtisodiy va huquqiy munosabatlarning mazmunini tubdan o‘zgartirmoqda. Bugungi kunda sun’iy intellekt tizimlari sog‘liqni saqlash, moliyaviy xizmatlar, transport, ta’lim, davlat boshqaruvi, ishlab chiqarish, savdo va inson faoliyatining boshqa ko‘plab sohalariga tobora keng joriy etilmoqda.

Multicomponent Access to Oxa-β-Lactam Chemical Space and Its Emergence as a Covalent Modifier Scaffold

Documento de trabajo. Se establece una descomposición exacta del conteo de losnúmeros primos en dos partes —una componente de densidad, en forma cerrada, y unacomponente de fluctua…

Documento de trabajo. Se establece una descomposición exacta del conteo de losnúmeros primos en dos partes —una componente de densidad, en forma cerrada, y unacomponente de fluctuación— y se muestra que esta segunda parte coincide con elobjeto descrito por los ceros no triviales de la función zeta de Riemann. Eln-ésimo primo se obtiene de forma exacta por esta vía. Cada afirmación va con suderivación paso a paso y con el código que la reproduce. Acceso restringido;registro con fines de fecha y autoría.

This folder contains the notebooks used to produce all figures and the data used in the paper "Independent density and coherence skin effects in adaptive fermion circuits".Each file corresponds to one…

This folder contains the notebooks used to produce all figures and the data used in the paper "Independent density and coherence skin effects in adaptive fermion circuits".Each file corresponds to one figure and includes all subfigures. All data is produced either from analytical expressions or by exactly diagonalizing the Liouvillian.