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Open accessJournal ArticleDOI
01 Aug 1988-Cognition
102 Citations
Images, even the limited class of images here called diagrams, support inference in a way that is distinct from the way predicative representations support inference.
This enables the exploitation of well-developed inference toolkit in graphical models.
We show that the new inference operations presented here unify inference and initialization.
The inference formalism is flexible and robust, and well-suited to implementation.
In this paper, deep inference is shown to be crucial for the logic BV, that is, any restriction on the ``depth'' of the inference rules of BV would result in a strictly less expressive logical system.
Open accessJournal ArticleDOI
Jaydeep De, Jaydeep De, Huiqi Li, Li Cheng, Li Cheng 
18 Jan 2014-BMC Bioinformatics
27 Citations
This connection enables us to address the tracing problem by exploiting established development in transductive inference.
We show that the inference algorithm of the proposed framework is equivalent to a feed-forward network.
This network corresponds completely to the Yager inference rule and exhibits remarkable generalization abilities.
Extracting propositional information in this way not only permits the model to answer questions for which the relevant facts are explicitly stated in the text but also allows the model to take advantage of “inference by coincidence,” where implicit inference occurs as an emergent property of the mechanism.
However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.
We present a novel view of inference in general networks – where inference is done via a change-of-variables that renders the network tree-structured and amenable to a very simple form of inference.
Open accessJournal ArticleDOI
Yoshida Kenichi, Hiroshi Motoda 
101 Citations
The learned concepts enable an efficient inference on a more abstract level.
In particular, we show how and why the effectiveness of an automated reasoning program can be, and often is, markedly increased by relying on the linked version rather than the more familiar standard version of an inference rule.
This illustrates the good generalization properties of semantic inference.
Empirically, we demonstrate the utility of our inference algorithm, showing that it can be an order-ofmagnitude more efficient than more traditional approaches to exact inference.
This approach to designing inference rules differs from previous efforts where the primary focus has been on obtaining a set of sound and complete inference rules, rather than on intuitiveness.
We first show that the architecture of GNNs is well-matched to inference tasks.
Open accessProceedings ArticleDOI
07 Jun 2015
105 Citations
Our results demonstrate that we successfully address above inference tasks under challenging conditions.
In addition, our formulation of type inference can easily be tailored to different inference algorithms such as W and M. On the technical side, we present concise soundness and completeness results.

Related Questions

What is the methodology used in YOLOv5x single step object detection and classification?5 answersThe methodology employed in YOLOv5x single-step object detection and classification involves innovative techniques to enhance accuracy and efficiency. Firstly, the YOLOv5 architecture serves as the base network, with modifications like the integration of the Ghost module to optimize computing resources and speed up detection. Additionally, a fusion model combining infrared and visible modalities is designed to improve resolution for night-time vehicle recognition and remote surveillance, achieving high metric values like 0.96 for accuracy. Furthermore, a lightweight single-class object detector is developed by excluding the class prediction layer and training the regression layer specifically for the target class, resulting in faster inference speed without compromising precision. Lastly, enhancements like the MS Transformer module and CBAM are incorporated into YOLOv5 for improved feature extraction and attention area identification in dense scenes, leading to higher detection accuracy in challenging remote sensing scenarios.
What is yolov9?5 answersYOLOv9 is not mentioned in any of the provided abstracts.
What are the limitations of YOLOv5?4 answersThe limitations of YOLOv5 include issues such as low accuracy, a high rate of false detection, missed detection, sample imbalance, and difficulty in detecting small objects.The YOLOv5 algorithm may not achieve the desired results when applied to specific tasks such as crack and vehicle detection due to the presence of many small objects in the dataset.Additionally, YOLOv5 has limited feature extraction ability, poor feature integration, and a limited receptive field, which can affect its performance in target detection.Furthermore, YOLOv5 does not output angular predictions, which are crucial for reflecting attitudes and shapes of targets in aerial images.
How to develop lightweight yolov5s based model for different maturity strawberry detection and counting?5 answersTo develop a lightweight yolov5s-based model for different maturity strawberry detection and counting, several approaches have been proposed in the literature. Li et al. introduced a real-time multistage strawberry detection algorithm, YOLOv5-ASFF, which achieved high accuracy and robustness in detecting ripe strawberries under complex field conditions. Lawal et al. proposed an improved YOLOv5s model with feature concatenation and an attention mechanism, which demonstrated better performance in terms of mAP, speed, and detection accuracy compared to the original YOLOv5s model. Another study by Xu utilized a triple-layer feed-forward neural network system to detect strawberry maturity, achieving a detection precision ratio of 91.7%. Additionally, a lightweight YOLOv5s-MBLS detection algorithm was proposed, which incorporated the MBL module and SIoU loss function to improve accuracy, reduce model size, and enhance detection speed. These approaches provide insights into developing lightweight yolov5s-based models for strawberry detection and counting, considering factors such as accuracy, speed, and model size.
What is yolov8 in object detection?4 answersYOLOv8 is a state-of-the-art object detection algorithm that is used for real-time detection of flying objects. It is a single-shot detector that aims to find the best tradeoff between inference speed and mean average precision (mAP). YOLOv8 has been developed to address the challenges of object detection, such as large variance in object spatial sizes, rate of speed, occlusion, and clustered backgrounds. Although an official paper for YOLOv8 has not been provided, it has been regarded as the new state-of-the-art in object detection. The algorithm has been refined and improved through transfer learning and the utilization of a current state-of-the-art single-shot detector, resulting in a final refined model with an improved mAP50-95 of 0.835 and an average inference speed of 50 fps on 1080p videos.
How to write inference for Anova test?8 answers

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Which recommendations can be derived to reduce privacy risk in data sharing with da data space?
5 answers
To reduce privacy risks in data sharing within a data space, several recommendations can be derived from the research contexts provided. Firstly, implementing techniques like PrivateSMOTE can effectively protect sensitive data by generating synthetic data to obfuscate high-risk cases while minimizing data utility loss. Additionally, utilizing innovative frameworks such as Representation Learning via autoencoders can help generate privacy-preserving embedded data, enabling collaborative training of ML models without sharing original data sources. Moreover, conducting thorough reviews of clinical publications to identify and minimize reidentification risks, especially concerning direct and indirect identifiers, is crucial for safeguarding participant privacy. Lastly, employing techniques like embedding-aware noise addition (EANA) can mitigate communication overhead and improve training speed in large-scale recommendation systems while maintaining good practical privacy protection.
What are the failures of fuzzy tests based on deep learning?
10 answers
Fuzzy tests based on deep learning, while innovative and powerful, exhibit several failures and limitations across different implementations and applications. One of the primary failures is the Seq2Seq model's inability to effectively handle unstructured cases, which limits its fuzz testing performance and bug detection capabilities in such scenarios. Additionally, mutation-based fuzz testing techniques, particularly those applied to deep neural networks (DNNs), often suffer from a lack of diversity and distribution deflection from the original DNN input space. This deficiency impacts the comprehensive evaluation of DNNs' robustness against adversarial vulnerabilities. Moreover, existing testing approaches for deep learning (DL) libraries fail to comprehensively exercise these libraries, as they primarily utilize existing trained models and focus only on the model inference phase. This approach overlooks potential bugs in the model training phase, thereby limiting the detection of bugs within DL libraries. In the context of testing DL inference engines, the lack of attention to the core underlying frameworks and libraries, as opposed to the DL models themselves, represents another failure. This oversight misses opportunities to improve the quality of DL systems through more targeted fuzz testing methods. Regression fuzzing techniques, such as DRFuzz, although designed to find regression faults in evolving DL systems, may not always guarantee the fidelity of generated test inputs, which is crucial for accurately identifying diverse regression faults. Furthermore, some approaches to enhancing input examples for testing DL systems suffer from low generalization ability across different models and neglect deep feature constraints, which are essential for maintaining the semantic integrity of adversarial examples. Lastly, the application of mutation-based fuzzing for augmenting DNN training data, aimed at enhancing robustness, is challenged by the optimization problem of generating the most suitable input data variant for training, which can complicate the augmentation process and potentially slow down training.
What are the minimal points required for skeleton based action recognition?
5 answers
Skeleton-based action recognition requires the extraction of key frames to accurately classify human actions while minimizing computational costs. Traditional methods demand hundreds of frames for analysis, leading to high computational expenses. To address this, a fusion sampling network is proposed to generate fused frames, reducing the number of frames needed to just 16.7% while maintaining competitive performance levels. Additionally, converting videos into skeleton-based frames enhances action detection accuracy and reduces computational complexity, enabling precise classification of human behaviors based on actions. Furthermore, Adaptive Cross-Form Learning (ACFL) empowers Graph Convolutional Networks (GCNs) to generate complementary representations from single-form skeletons, improving action recognition without the need for all skeleton forms during inference.
What is the current state of research on entity matching using graph-based methods?
5 answers
Current research on entity matching using graph-based methods is advancing rapidly. Studies focus on addressing challenges like incomplete knowledge graphs and cross-graph matching. Novel approaches like SVNM-GAT, hybrid methods combining embedding techniques with graph convolutional neural networks, and frameworks like WOGCLaim to enhance entity alignment by leveraging graph structures effectively. These methods incorporate mechanisms such as virtual node matching, subgraph-awareness, and optimal transport learning to improve matching accuracy. Research also delves into biomedical entity linking, categorizing methods into rule-based, machine learning, and deep learning models. Overall, the field is evolving to overcome issues like vocabulary heterogeneity, entity ambiguity, and the presence of dangling entities in knowledge graphs.
How are implicit representations of shape used with dep learning?
5 answers
Implicit representations of shape, such as Implicit Neural Representations (INRs) and Neural Vector Fields (NVF), are integrated with deep learning to encode various signals like 3D shapes efficiently. INRs, represented by neural networks, can be embedded effectively into deep learning pipelines for downstream tasks. Similarly, NVF combines explicit learning processes with the powerful representation ability of implicit functions, specifically unsigned distance functions, to enhance 3D surface reconstruction tasks. NVF predicts displacements towards surfaces, encoding distance and direction fields to simplify calculations and improve model generalization. These approaches showcase how implicit shape representations can be seamlessly integrated into deep learning frameworks for tasks like shape analysis, dimensionality reduction, and surface reconstruction.
How to analyze metagenomic data extracted from microorganisms?
5 answers
Analyzing metagenomic data from microorganisms involves various computational methods and techniques. Initially, nucleotide sequencing is performed to obtain DNA fragments, which are then decomposed into k-mers for further analysis. These k-mers and their frequencies help identify the organisms present in the sample, aiding in taxonomic classification. To enhance metagenomic analysis capabilities, a genetic algorithm can be utilized to select a subset of valuable k-mer features for classification, as demonstrated in the detection of type 2 diabetes from human gut samples. Additionally, graph-based approaches can be employed to identify false-positive identifications of operational taxonomic units within an environment, contributing to more accurate analyses. Overall, a combination of data processing, classification frameworks, and graph-based methods are essential for comprehensive metagenomic data analysis.
What are the common methods used to categorize nodes and leaves in Abstract Syntax Trees (ASTs)?
4 answers
Common methods used to categorize nodes and leaves in Abstract Syntax Trees (ASTs) include techniques based on abstract syntax tree (AST) and graph neural network (GNN) for code classification. Additionally, AST-based Recursive Neural Networks are implemented for code clone detection, where pretrained embeddings representing nodes in ASTs play a crucial role in aggregation schemes. Furthermore, AST matching for plagiarism detection involves generating ASTs from modified grammars for languages like C, C++, and Java, allowing subtree matching and comparison using sequence matching algorithms like Needleman-Wunsch and longest common subsequence. These methods leverage the structural and semantic information within ASTs to classify and detect patterns in code effectively.
What are the specific financial decisions that senior high school students are influenced by on social media?
5 answers
Senior high school students are influenced by social media in various financial decisions. Research indicates that teenagers are impacted by the spread of social media, leading to misconceptions about money and increased spending. Additionally, platforms like Instagram play a role in shaping consumptive behavior among students. Furthermore, social media can affect the financial attitudes of young adults, influencing their choices in personal finance information sources. Moreover, the reliance on social media for financial decision-making can result in poor choices due to the dissemination of inaccurate information, highlighting the risks associated with consumer vulnerability to false information. Overall, social media influences senior high school students in spending habits, financial attitudes, and information source choices, emphasizing the need for financial education and awareness among this demographic.
What are the advantages of visual symptomps detection in plant?
4 answers
Visual symptoms detection in plants offer several advantages in disease management. Traditional methods relying on visual symptoms, backed by Deep Neural Networks (DNN) models, are effective but often detect diseases after several asymptomatic phases. Additionally, the use of an internet of things based visual sensor network combined with neural network classifiers has shown high accuracy in detecting early onset plant diseases through visual symptoms. Moreover, advancements in technology have enabled the application of spectroscopy for early plant stress detection, leading to significant accuracy improvements in detecting dehydration and achieving good species separation. These approaches aid in timely disease identification, allowing for prompt intervention to minimize crop damage and maximize yield, crucial for food security and sustainable agriculture.
How does transfer learning improve the efficiency of edge computing for face recognition?
10 answers
Transfer learning significantly enhances the efficiency of edge computing for face recognition by leveraging pre-trained models to achieve high accuracy with less computational resource requirement and quicker adaptation to new, specific tasks. This approach is particularly beneficial in edge computing environments where computational resources are limited, and latency is a critical factor. The EdgeFace network, inspired by the hybrid architecture of EdgeNeXt, demonstrates how a lightweight model can achieve state-of-the-art face recognition results on edge devices, benefiting from a combination of CNN and Transformer models optimized through transfer learning techniques. Similarly, the use of transfer learning in facial expression recognition (FER) systems, as shown with the EfficientNet architectures, allows for high accuracy in recognizing facial expressions from small datasets, showcasing the method's power in enhancing model performance without the need for extensive data. In the context of smart UAV delivery systems, a multi-UAV-edge collaborative framework utilizes feature extraction and storage on edge devices, showcasing how transfer learning can streamline the identification process in real-world applications by efficiently handling face recognition tasks at the edge. Moreover, the application of transfer learning in optimizing models for specific small and medium-sized datasets, as seen in the comparison of VGG16 and MobileNet's performance, further illustrates its role in improving the efficiency and accuracy of face recognition systems in edge computing scenarios. Additionally, the integration of transfer learning with novel architectures, such as the combination of attention modules and lightweight backbone networks in an edge-cloud joint inference architecture, demonstrates a balanced approach to achieving high classification accuracy while maintaining low-latency inference, crucial for edge computing applications. In summary, transfer learning enhances the efficiency of edge computing for face recognition by enabling the use of compact, yet powerful models that require less computational power and can be quickly adapted to new tasks, thereby improving both the speed and accuracy of face recognition on edge devices.
How does synthetic data improve training results?
5 answers
Synthetic data enhances training results by providing a valuable alternative to real-world datasets, addressing challenges like data annotation efforts and limited generalization capabilities. Various techniques have been proposed to generate high-quality synthetic datasets, such as creating digital twins of real-world data, employing Generative Adversarial Networks with post-processing techniques, and utilizing frameworks like GRADE for realistic environment simulation. These approaches aim to reduce domain gaps between synthetic and real data, leading to improved model performance. By leveraging synthetic data for training, models can benefit from increased diversity, reduced annotation burdens, and enhanced generalization capabilities, ultimately resulting in better training outcomes and more robust algorithms for various computer vision tasks.