29.58)
N , number of data; N , number of participants; n_c , number of correct answers.
We used accuracy to evaluate the representation power. For the P-A connection (P1), the V-Model showed about 32% higher accuracy. The test considered finding the causality relation, which could be interpreted ambiguously in a conventional timeline. The problem-action link made the participants find the right answers significantly better. Ninety percent of the participants in the V-Model group provided correct responses for the non-explicitness (P2) test. This result shows that the non-explicit temporal expression case was successfully modeled and conveyed in our timeline. Next, 92.5% of the V-Model participants could determine the temporal proximity (P3) from the non-temporal TAP expressions. Overall, 94.38% of the participants from both groups agreed that the V-Model represented uneven granularity in one view (P4).
To evaluate reasoning aspects, we used both accuracy and response time as measurements. Accuracy showed whether the timeline sufficiently provided information for temporal reasoning. And response time measured the easiness of the temporal reasoning process. In reasoning, P precedes A (R1), and both groups showed high performance. However, the V-Model group took less than one-third the response time compared to the LifeLines group with the help of the graphical separation of the problem and action. In reasoning the qualitative relation, the two groups showed no statistical difference in accuracy. However, the LifeLines group completed the questions in a statistically significantly less time. All of the V-Model participants provided a correct response to the question that required implicit distance reasoning (R3) (which was impossible in the LifeLines view).
To measure the easy-to-catch property established by the visualization aspects, we compared response times. The response times were statistically significantly faster in the V-Model group for all the evaluation items (V1, V2, and V5). In regards to the dynamic scale preference problem (V4), a prominent number of participants (85%) from both groups selected the dynamic timeline of the V-Model as more appropriate for patient history. Although the LifeLines users were unfamiliar with the V-Model, 75% of the LifeLines users chose this unfamiliar timeline description as better.
Figure 6 shows the answer distribution of the usability questionnaire, grouping the results related to positive questions and negative ones. The V-Model was assessed as superior to LifeLines in terms of usability. In regards to positive questions (questions 1, 3, 5, 7, and 9), a prominent number of participants expressed (strong) agreement (score 4 and 5). Negative opinions were very few, and there was no strong disagreement (score 1). Conversely, in the results for the LifeLines group, we could not find a consensus. In the results for the negative aspect questions (questions 2, 4, 6, 8, and 10), the majority percentage of the V-Modelgroup answers were in disagreement (score 1 and 2), except for question 10. However, all participants could understand the V-Model without any information. We suppose the necessity for an explanation to the multiple P-A link problem affected the result. In the LifeLines group, a different factor affected the result. It is our guess that the lack of causality and difficulties in tracing patient history were the main reasons.
Usability questionnaire results.
The V-Model enables visualization of textual information in a timeline. It represents apparent P-A relations only and describes the other minor relationships in natural language. There might be a criticism that the P-A link is insufficient to cover the various contexts that clinical documents have. However, it is our belief that we compensated well balancing sustaining simplicity and informing the original context at an adequate level.
The V-Model deals with universal natural language problems, such as causality, granularity, and non-explicitness problems. Although the experiment was performed based on Korean EHR data only, the results can be applied to any other language. It was proven that the V-Model functionally achieved our design goals. Furthermore, it outperformed in the overall evaluation aspects. However, recognizing qualitative temporal relations (R2) took more time than conventional timeline representation. Providing qualitative temporal information while preserving the V-Model’s simplicity would be a challenging work.
For the evaluation, we compared manually-visualized V-Model timelines with conventional timelines that were automatically generated by LifeLines. The comparison was fairly performed as we focused on timeline properties, rather than systematic issues. System implementation using the V-Model is not covered by this paper.
We demonstrated that the V-Model reflects design considerations for the NLP. For example, we simplified semantic tags as only 14 categories, clarifying how to determine an event as a Problem or an Action , and how to visualize events in the order of appearance when temporal information is missing.
The ultimate goal of our model is practical use in patient treatment and medical research. We anticipate that the V-Model would play a crucial role in phenotype definition and algorithm development. The model extends our perspective on the data unit from a concept to a sequence of concepts (context block). The V-Model timeline integrates distributed patient data regardless of its original source, type or institution. It enables a user to trace patient history considering semantic, temporal, and causality information in a short time. The view would ease and shorten the unavoidable manual reviewing process accelerating phenotyping more efficiently.
To enable chronological visualization of narrative patient history, we developed the V-Model. We devised a unique v-shaped structure and modeling strategies to solve natural language representation problems. The V-Model displays clinical events in a restrained format. Especially, the Problem-Action relationship is intuitive, and the relation is extensible to neighboring events. This feature facilitates pattern finding, which would promote high-throughput phenotyping. The V-Model was shown to excel in representing narrative medical events, provide sufficient information for temporal reasoning, and outperform in readability. The only disadvantage was taking a longer time in recognizing qualitative relationships. Subjects assessed our model positively on the usability evaluation. We conclude that the V-Model can be a new model for narrative clinical events, and it would make EHR data more reusable.
a Approved by SNUH Institutional Review Board (IRB) (IRB No. 1104-027-357).
This research was supported by a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea. (A112005), and was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIP) (No. 2010–0028631).
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
Both authors contributed to the conception and design of the study, analysis, and interpretation of data, drafting of the article and final approval for submission. HP and JC designed the study. HP carried out data collection, analysis, and drafted the manuscript, with significant contributions from JC. HP and JC performed data interpretation. Both authors read and approved the final manuscript.
Heekyong Park, Email: rk.ca.uns@10erac .
Jinwook Choi, Email: rk.ca.uns@iohcnij .
Sa-v dataset, installation, sam comparison vs yolov8, limitations, citations and acknowledgements.
SAM 2, the successor to Meta's Segment Anything Model (SAM) , is a cutting-edge tool designed for comprehensive object segmentation in both images and videos. It excels in handling complex visual data through a unified, promptable model architecture that supports real-time processing and zero-shot generalization.
Unified model architecture.
SAM 2 combines the capabilities of image and video segmentation in a single model. This unification simplifies deployment and allows for consistent performance across different media types. It leverages a flexible prompt-based interface, enabling users to specify objects of interest through various prompt types, such as points, bounding boxes, or masks.
The model achieves real-time inference speeds, processing approximately 44 frames per second. This makes SAM 2 suitable for applications requiring immediate feedback, such as video editing and augmented reality.
SAM 2 can segment objects it has never encountered before, demonstrating strong zero-shot generalization. This is particularly useful in diverse or evolving visual domains where pre-defined categories may not cover all possible objects.
Users can iteratively refine the segmentation results by providing additional prompts, allowing for precise control over the output. This interactivity is essential for fine-tuning results in applications like video annotation or medical imaging.
SAM 2 includes mechanisms to manage common video segmentation challenges, such as object occlusion and reappearance. It uses a sophisticated memory mechanism to keep track of objects across frames, ensuring continuity even when objects are temporarily obscured or exit and re-enter the scene.
For a deeper understanding of SAM 2's architecture and capabilities, explore the SAM 2 research paper .
SAM 2 sets a new benchmark in the field, outperforming previous models on various metrics:
Metric | SAM 2 | Previous SOTA |
---|---|---|
- | ||
Baseline | ||
SAM | ||
SAM |
Core components.
The memory mechanism allows SAM 2 to handle temporal dependencies and occlusions in video data. As objects move and interact, SAM 2 records their features in a memory bank. When an object becomes occluded, the model can rely on this memory to predict its position and appearance when it reappears. The occlusion head specifically handles scenarios where objects are not visible, predicting the likelihood of an object being occluded.
In situations with ambiguity (e.g., overlapping objects), SAM 2 can generate multiple mask predictions. This feature is crucial for accurately representing complex scenes where a single mask might not sufficiently describe the scene's nuances.
The SA-V dataset, developed for SAM 2's training, is one of the largest and most diverse video segmentation datasets available. It includes:
SAM 2 has demonstrated superior performance across major video segmentation benchmarks:
Dataset | J&F | J | F |
---|---|---|---|
82.5 | 79.8 | 85.2 | |
81.2 | 78.9 | 83.5 |
In interactive segmentation tasks, SAM 2 shows significant efficiency and accuracy:
Dataset | NoC@90 | AUC |
---|---|---|
1.54 | 0.872 |
To install SAM 2, use the following command. All SAM 2 models will automatically download on first use.
The following table details the available SAM 2 models, their pre-trained weights, supported tasks, and compatibility with different operating modes like Inference , Validation , Training , and Export .
Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
---|---|---|---|---|---|---|
SAM 2 tiny | ✅ | ❌ | ❌ | ❌ | ||
SAM 2 small | ✅ | ❌ | ❌ | ❌ | ||
SAM 2 base | ✅ | ❌ | ❌ | ❌ | ||
SAM 2 large | ✅ | ❌ | ❌ | ❌ |
SAM 2 can be utilized across a broad spectrum of tasks, including real-time video editing, medical imaging, and autonomous systems. Its ability to segment both static and dynamic visual data makes it a versatile tool for researchers and developers.
Segment with Prompts
Use prompts to segment specific objects in images or videos.
Segment Everything
Segment the entire image or video content without specific prompts.
Here we compare Meta's smallest SAM model, SAM-b, with Ultralytics smallest segmentation model, YOLOv8n-seg :
Model | Size | Parameters | Speed (CPU) |
---|---|---|---|
Meta's SAM-b | 358 MB | 94.7 M | 51096 ms/im |
40.7 MB | 10.1 M | 46122 ms/im | |
with YOLOv8 backbone | 23.7 MB | 11.8 M | 115 ms/im |
Ultralytics | (53.4x smaller) | (27.9x less) | (866x faster) |
This comparison shows the order-of-magnitude differences in the model sizes and speeds between models. Whereas SAM presents unique capabilities for automatic segmenting, it is not a direct competitor to YOLOv8 segment models, which are smaller, faster and more efficient.
Tests run on a 2023 Apple M2 Macbook with 16GB of RAM. To reproduce this test:
Auto-annotation is a powerful feature of SAM 2, enabling users to generate segmentation datasets quickly and accurately by leveraging pre-trained models. This capability is particularly useful for creating large, high-quality datasets without extensive manual effort.
To auto-annotate your dataset using SAM 2, follow this example:
Auto-Annotation Example
Argument | Type | Description | Default |
---|---|---|---|
Path to a folder containing images to be annotated. | |||
, optional | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. | ||
, optional | Pre-trained SAM 2 segmentation model. Defaults to 'sam2_b.pt'. | ||
, optional | Device to run the models on. Defaults to an empty string (CPU or GPU, if available). | ||
, , optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. |
This function facilitates the rapid creation of high-quality segmentation datasets, ideal for researchers and developers aiming to accelerate their projects.
Despite its strengths, SAM 2 has certain limitations:
If SAM 2 is a crucial part of your research or development work, please cite it using the following reference:
We extend our gratitude to Meta AI for their contributions to the AI community with this groundbreaking model and dataset.
SAM 2, the successor to Meta's Segment Anything Model (SAM) , is a cutting-edge tool designed for comprehensive object segmentation in both images and videos. It excels in handling complex visual data through a unified, promptable model architecture that supports real-time processing and zero-shot generalization. SAM 2 offers several improvements over the original SAM, including:
For more details on SAM 2's architecture and capabilities, explore the SAM 2 research paper .
SAM 2 can be utilized for real-time video segmentation by leveraging its promptable interface and real-time inference capabilities. Here's a basic example:
For more comprehensive usage, refer to the How to Use SAM 2 section.
SAM 2 is trained on the SA-V dataset, one of the largest and most diverse video segmentation datasets available. The SA-V dataset includes:
This extensive dataset allows SAM 2 to achieve superior performance across major video segmentation benchmarks and enhances its zero-shot generalization capabilities. For more information, see the SA-V Dataset section.
SAM 2 includes a sophisticated memory mechanism to manage temporal dependencies and occlusions in video data. The memory mechanism consists of:
This mechanism ensures continuity even when objects are temporarily obscured or exit and re-enter the scene. For more details, refer to the Memory Mechanism and Occlusion Handling section.
SAM 2 and Ultralytics YOLOv8 serve different purposes and excel in different areas. While SAM 2 is designed for comprehensive object segmentation with advanced features like zero-shot generalization and real-time performance, YOLOv8 is optimized for speed and efficiency in object detection and segmentation tasks. Here's a comparison:
For more details, see the SAM comparison vs YOLOv8 section.
Wu, Y, Han, F, Peng, X, Gao, L, Zhao, Y, & Zhang, J. "Research on a Fast Resistance Performance Prediction Method for SUBOFF Model Based on Physics-Informed Neural Networks (PINNs)." Proceedings of the ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering . Volume 6: Polar and Arctic Sciences and Technology; CFD, FSI, and AI . Singapore, Singapore. June 9–14, 2024. V006T08A041. ASME. https://doi.org/10.1115/OMAE2024-126300
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Physics-Informed Neural Networks (PINNs) represent a novel intelligent algorithm for solving partial differential equations (PDEs), which has been partially validated in solving Navier-Stokes equations. However, numerous challenges remain in the application of PINNs to the calculation of hydrodynamic performance of ships.
This paper utilizes discrete solutions of the Navier-Stokes (N-S) equations obtained via the Finite Volume Method (FVM) and Reynolds Averaged Navier-Stokes (RANS) approach of the CFD method for training PINNs to directly solve flow field information. This approach achieves a solution with approximate accuracy to that based on the CFD method, but with significantly reduced time consumption. The development of this algorithm could offer a novel perspective for calculating the hydrodynamic performance of ships.
The study focuses on the DARPA SUBOFF-B model, using field information obtained at specific speeds through the CFD method, including pressure and velocity fields, as the training set to train the neural parameters within PINNs. This enables the PINNs model to predict the flow field information around the hull and calculate the hull resistance through integration. The results predicted by the PINNs method are compared and analyzed against those calculated via the CFD method, Shows the comparison of the predicted results of the hull surface pressure field, as well as the relative error of the predicted total resistance., thereby proving the feasibility of the method.
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Skeleton-based action recognition methods have become a research hotspot due to their robustness against variations in lighting, complex backgrounds, and viewpoint changes. Addressing the issues of long-distance joint associations and time-varying joint correlations in skeleton data, this paper proposes a Multi-Stream Attention-Enhanced Recursive Graph Convolution method for action recognition. This method extracts four types of features from the skeleton data: joints, bones, joint movements, and bone movements. It models the potential relationships between non-adjacent nodes through adaptive graph convolution and utilizes a long short-term memory network for recursive learning of the graph structure to capture the temporal correlations of joints. Additionally, a spatio-temporal channel attention module is introduced to enable the model to focus more on important joints, frames, and channel features, further improving performance. Finally, the recognition results of the four branches are fused at the decision level to complete the action recognition. Experimental results on public datasets (UTD-MHAD, CZU-MHAD) and a self-constructed dataset (KTH-Skeleton) demonstrate that the proposed method achieves accuracies of 94.65%, 95.01%, and 97.50%, respectively, fully proving the good performance of the method.
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The data sets analyzed in the current study are publicly available data sets: https://personal.utdallas.edu/~kehtar/UTD-MHAD.html , https://github.com/yujmo/CZU_MHAD .
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This work was supported by the National Natural Science Foundation of China (No. 61971347), Doctoral Innovation Foundation of Xi’an University of Technology (No. 252072118), Natural Science Foundation of Shaanxi Province of China (No. 2021JM-344), Xi’an Science and Technology Plan Project (2022JH-RYFW-007) (24DCYJSGG0020), Key research and development program of Shaanxi Province (2022SF-353).
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School of Computer Science and Engineering, Xi’an University of Technology, No. 5 South Jinhua Road, Xi’an, 710048, Shaanxi, China
Huaijun Wang, Bingqian Bai, Junhuai Li & Hui Ke
Shaanxi Key Laboratory for Network Computing and Security Technology, No. 5 South Jinhua Road, Xi’an, 710048, Shaanxi, China
Huaijun Wang & Junhuai Li
School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, 3086, Australia
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All authors contributed to the study conception and design. Huaijun Wang and Junhuai Li are responsible for the overall research design and guiding research work, providing research direction and guidance, and assisting in the writing and proofreading of papers. Hui Ke participated in the experiment, data collection, data analysis and other work, and also participated in the writing of the paper. Xiang Wei not only provided resource support, but also provided academic support and guidance. All authors read and approved the fnal manuscript.
Correspondence to Junhuai Li .
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Wang, H., Bai, B., Li, J. et al. Action recognition method based on multi-stream attention-enhanced recursive graph convolution. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05719-0
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DOI : https://doi.org/10.1007/s10489-024-05719-0
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Title: exaone 3.0 7.8b instruction tuned language model.
Abstract: We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote open research and innovations. Through extensive evaluations across a wide range of public and in-house benchmarks, EXAONE 3.0 demonstrates highly competitive real-world performance with instruction-following capability against other state-of-the-art open models of similar size. Our comparative analysis shows that EXAONE 3.0 excels particularly in Korean, while achieving compelling performance across general tasks and complex reasoning. With its strong real-world effectiveness and bilingual proficiency, we hope that EXAONE keeps contributing to advancements in Expert AI. Our EXAONE 3.0 instruction-tuned model is available at this https URL
Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
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IMAGES
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(component) model [15]. The remainder of this paper is organized as follows. The V-model lifecycle and the modified V -model lifecycle are explained in Sections 2 and 3, respectively.
The main contribution in this paper is Sawtooth model and Sharktooth model. Sawtooth model is another variation of the V model. The Sawtooth model is actually an extension of the V-model. The only ...
The V model is also known as validation and verification. The V model is an extension of the Waterfall model based on the associated testing phase and development stages.
ML-enabled systems spiral, or agile models when building . In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems. By interviewing practitioners from software companies, we established a set of 8 propositions for using V-Model to manage interdisciplinary col ...
In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems.
The content of this paper is based especially on current and previous research activities on the field of model-based development at the Institute of Virtual Product Engineering.
Software testing is an important link of software quality guarantee, regarding to the embedded software testing that is much more compelxed, needing a clear stage development test model that better controlled. The article discussed the model that already existed, further excavates the parallelism between test stages, trys to proposes a improved V model, This model make the software testing ...
V-Model and Axiomatic Design are two proven systems engineering methodologies to support the design process of complex systems. Both methodologies originated in the late seventies. The V-Model is the more successful of the two, partly due to its simplicity and...
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The V-Model is an approach model that was developed by commissioning of the State of Germany for planning and implementing system development projects. It considers the entire lifecycle of a system nicely fitting the line of thinking in systems engineering. The current V-Model XT from 2004 is based on V-Model 97, its predecessor.
The V-Model is an international recognized development standard for IT-systems which was developed by Industrieanlagen — Betriebsgesellschaft (IABG) on behalf of the German Ministery of Defense (BMVg) and was also taken over by the German Federal Ministery of the Interior. It is a generic process framework, that can be adapted to each type of ...
In this Paper, we propose a solution to reduce risks using UML visualization of software processes in detail, and V-Model (Software Development Lifecycle Model) techniques are performed to produce verified results in each phase of development lifecycle. Using all above aspects and research findings, we propose "Risk Assessment V-Model".
The purpose of V model is to Improve efficiency and effectiveness of software development and reflect the relationship between test activities and development activities as shown in Figure 1. This model is very simple and is perhaps the most traditional model followed for management of software tests.
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The goal of the research is to explore the use of V-Model for building ML-enabled systems. Thus, we used an explanatory approach to develop a set of research propositions. The reasons we chose this approach are as follows: first, there are very few studies on the process lifecycle models for ML-enabled systems.
This paper explains about the delivery issues being faced in the software industry - specifically how delivery is impacted at companies that use V Process Model as the Chosen model for Software Development Life Cycle.
The V-model is a graphical representation of a systems development lifecycle. It is used to produce rigorous development lifecycle models and project management models. The V-model falls into three broad categories, the German V-Modell, a general testing model, and the US government standard. [2]
Further descriptions are provided in upcoming sections. Section 2 addresses the research motivation and the gaps between academic curriculum and industry demand in the field of control engineering. In Section 3, a V-model for control engineering theses progress Section 4 outlines the Agile subsequently, the proposed Scrum management is introduced.
The V-Model of the VDI 2206 from 2004 is an important basis for the industrial application of mechatronic product development. This paper shows which changes need to be integrated into the updated V-Model and in which areas the focused topics have to be changed to be prepared for future challenges.
In this paper, we propose a temporal model named the 'V-Model' which visualizes clinical narratives into a timeline. Methods: We developed the V-Model which models temporal clinical events in v-like graphical structure. It visualizes patient history on a timeline in an intuitive way. For the design, the representation, reasoning, and ...
In this paper, we propose a novel model to visualize narrative patient history called the V-Model. The V-Model displays narrative patient data and causal relation on a timeline in a patterned format.
This paper introd uces and discusses Agile Software Processes and V-shaped model.The process of developing and supporting software often requires many distinct tasks to
Discover SAM 2, the next generation of Meta's Segment Anything Model, supporting real-time promptable segmentation in both images and videos with state-of-the-art performance. Learn about its key features, datasets, and how to use it.
Abstract. Physics-Informed Neural Networks (PINNs) represent a novel intelligent algorithm for solving partial differential equations (PDEs), which has been partially validated in solving Navier-Stokes equations. However, numerous challenges remain in the application of PINNs to the calculation of hydrodynamic performance of ships.This paper utilizes discrete solutions of the Navier-Stokes (N ...
The paper analyzes and discusses the most commonly used software testing models, including waterfall, incremental, V-model, agile, and spiral models, and identifies several areas for improvement ...
Skeleton-based action recognition methods have become a research hotspot due to their robustness against variations in lighting, complex backgrounds, and viewpoint changes. Addressing the issues of long-distance joint associations and time-varying joint correlations in skeleton data, this paper proposes a Multi-Stream Attention-Enhanced Recursive Graph Convolution method for action recognition ...
We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote open research and innovations. Through extensive evaluations across a wide range of public and in-house benchmarks, EXAONE 3.0 demonstrates highly ...