Search Results for "stem" - Page 2

Simple Task Execution Manager (STEM) is an OSGi UPnP service that generates events and/or performs actions on UPnP devices and services, when a set of conditions on available UPnP services or devices is evaluated true.

The National STEM Distributed Learning (NSDL) program is a national network dedicated to advancing STEM teaching and learning for all learners in both formal and informal settings.

...Our approach is based on modelling a very large set of Arabic morphological rules, and also on integrating linguistic resources, such as the root database, vocalized patterns associated with roots, and proclitic and enclitic tables. As an output of the analysis, we have a highly informative table mainly containing vocalization of the stem, its grammatical category, its possible roots associated with corresponding patterns, proclitics and enclitics. A new version is available on the following link: http://oujda-nlp-team.net/?p=1299&lang=en How to cite the project: Boudlal, A., Lakhouaja, A., Mazroui, A., Meziane, A., Bebah, M. O. A. O., & Shoul, M. (2010). Alkhalil morpho sys1: A morphosyntactic analysis system for arabic texts. ...

OSR is an open source scan and read program. It is able to read text as well as mathematical and other Science, Technology, Engineering, and Mathematics (STEM) text. OSR also integrates organization by allowing input of key dates, assignments, etc.

snlanalytic is a small Python script that takes a stem-and-leaf plot as input and returns basic statistics (sum, mean, median, mode) to the user.

A jotter like kjots but using gtk. Also you can have multiple and arbitary depths for each stems. It also can export each stem and it's children neatly as a formatted html file. Saves all data in a simple heirarchy of text files.

Stem-Les (Lexicon Extraction Suite) extracts lexical chunks that are relevant in a corpus of documents. If the corpus is bilingual, Stem-Les also finds translation equivalents for the lexical solution selected by the user.

The STEM cells of PHP provide PHP developers with a set of code modules in PHP, extensions in C and User Interface blocks in JavaScript. Each module is as independent as possible, but functions extremely well in conjunction with the other modules.

...Architecturally, it introduces optimizations to reduce compute and memory costs while improving stability across long sequences. DeepSeek-V4-Pro is positioned as the high-end variant of the V4 family, outperforming most open-source models in areas such as agentic coding, STEM reasoning, and world knowledge, and approaching the performance of leading closed-source systems. It also supports advanced reasoning modes and tool-based workflows, enabling autonomous task execution.

...It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling it to process both text and images for advanced reasoning tasks. This version is specifically post-trained for reasoning, making it well-suited for math, coding, and STEM applications requiring multi-step logic and problem-solving. Despite its reasoning-focused training, the model remains edge-optimized and can run locally on a single 24GB GPU in BF16, or under 12GB when quantized. It supports dozens of languages, adheres reliably to system prompts, and provides native function calling and structured JSON output—key capabilities for agentic and automation workflows. ...

...It pairs a 13.5B-parameter language model with a 0.4B vision encoder, enabling strong multimodal reasoning across both text and images. This version is specifically post-trained for reasoning tasks, making it highly effective for math, coding, STEM workloads, and complex multi-step problem-solving. Despite its scale, the model is engineered for practical deployment and can run locally on 32GB of VRAM in BF16 or under 24GB when quantized. It maintains robust system-prompt adherence, supports dozens of languages, and provides native function calling with clean JSON output for agentic workflows. ...

...It pairs a 3.4B-parameter language model with a 0.4B-parameter vision encoder, enabling it to understand both text and image inputs. This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require logical reasoning, analysis, or structured thinking. Despite its modest size, the model is designed for edge deployment and can run locally, fitting in ~16 GB of VRAM in BF16 or under 8 GB of RAM/VRAM when quantized. It supports dozens of languages, allowing it to function across global and multilingual contexts. ...

...It introduces a hybrid reasoning mode with explicit <think> segments, enabling the model to deliberate deeply when needed and switch to faster responses when desired. Post-training improvements include a vastly expanded corpus with ~60B tokens, boosting performance across math, code, STEM, logic, creativity, and structured outputs. The model is designed for schema adherence, producing valid JSON and repairing malformed outputs, making it highly suitable for tool use and function calling. Hermes 4 is engineered for superior steerability with reduced refusal rates, aligning responses to user values while preserving assistant quality. ...

...Built on the Qwen3.5 architecture, it contains 27B parameters and combines Gated DeltaNet with gated attention across 64 layers. The model natively understands text, images, and videos, including documents, STEM diagrams, and hour-scale video content. Agent capabilities emphasize autonomous planning, environment feedback, computer and browser use, and reliable completion of complex multi-step workflows. Qwen3.8-27B supports a native 262,144-token context window that can be extended to one million tokens. Thinking is enabled by default, with low, medium, and xhigh reasoning-effort settings and preserved reasoning across conversations. ...