30,841 research outputs found
A semantic approach to interpolation
Craig interpolation is investigated for various types of formulae. By shifting the focus from syntactic to semantic interpolation, we generate, prove and classify a series of interpolation results for first-order logic. A few of these results non-trivially
generalize known interpolation results; all the others are new. We also discuss someapplications of our results to the theory of institutions and of algebraic specifications,and a Craig-Robinson version of these results
A semantic approach to interpolation
Craig interpolation is investigated for various types of formulae. By shifting the focus from syntactic to semantic interpolation, we generate, prove and classify a series of interpolation results for first-order logic. A few of these results non-trivially
generalize known interpolation results; all the others are new. We also discuss someapplications of our results to the theory of institutions and of algebraic specifications,and a Craig-Robinson version of these results
LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
LiDAR-based semantic segmentation is critical in the fields of robotics and
autonomous driving as it provides a comprehensive understanding of the scene.
This paper proposes a lightweight and efficient projection-based semantic
segmentation network called LENet with an encoder-decoder structure for
LiDAR-based semantic segmentation. The encoder is composed of a novel
multi-scale convolutional attention (MSCA) module with varying receptive field
sizes to capture features. The decoder employs an Interpolation And Convolution
(IAC) mechanism utilizing bilinear interpolation for upsampling
multi-resolution feature maps and integrating previous and current dimensional
features through a single convolution layer. This approach significantly
reduces the network's complexity while also improving its accuracy.
Additionally, we introduce multiple auxiliary segmentation heads to further
refine the network's accuracy. Extensive evaluations on publicly available
datasets, including SemanticKITTI, SemanticPOSS, and nuScenes, show that our
proposed method is lighter, more efficient, and robust compared to
state-of-the-art semantic segmentation methods. Full implementation is
available at https://github.com/fengluodb/LENet
Clustering of syntactic and discursive information for the dynamic adaptation of Language Models
Presentamos una estrategia de agrupamiento de elementos de diálogo, de tipo semántico y discursivo. Empleando Latent Semantic Analysis (LSA) agru- pamos los diferentes elementos de acuerdo a un criterio de distancia basado en correlación. Tras seleccionar un conjunto de grupos que forman una partición del espacio semántico o discursivo considerado, entrenamos unos modelos de lenguaje estocásticos (LM) asociados a cada modelo. Dichos modelos se emplearán en la adaptación dinámica del modelo de lenguaje empleado por el reconocedor de habla incluido en un sistema de diálogo. Mediante el empleo de información de diálogo (las probabilidades a posteriori que el gestor de diálogo asigna a cada elemento de diálogo en cada turno), estimamos los pesos de interpolación correspondientes a cada LM. Los experimentos iniciales muestran una reducción de la tasa de error de palabra al emplear la información obtenida a partir de una frase para reestimar la misma frase
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Semantic smoothing for Twitter sentiment analysis
Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifier from tweets data often faces the data sparsity problem partly due to the large variety of short forms introduced to tweets because of the 140-character limit. In this work we propose using semantic smoothing to alleviate the data sparseness problem. Our approach extracts semantically hidden concepts from the training documents and then incorporates these concepts as additional features for classifier training. We tested our approach using two different methods. One is shallow semantic smoothing where words are replaced with their corresponding semantic concepts; another is to interpolate the original unigram language model in the Naive Bayes NB classifier with the generative model of words given semantic concepts. Preliminary results show that with shallow semantic smoothing the vocabulary size has been reduced by 20%. Moreover, the interpolation method improves upon shallow semantic smoothing by over 5% in sentiment classification and slightly outperforms NB trained on unigrams only without semantic smoothing
SEGCloud: Semantic Segmentation of 3D Point Clouds
3D semantic scene labeling is fundamental to agents operating in the real
world. In particular, labeling raw 3D point sets from sensors provides
fine-grained semantics. Recent works leverage the capabilities of Neural
Networks (NNs), but are limited to coarse voxel predictions and do not
explicitly enforce global consistency. We present SEGCloud, an end-to-end
framework to obtain 3D point-level segmentation that combines the advantages of
NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields
(FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are
transferred back to the raw 3D points via trilinear interpolation. Then the
FC-CRF enforces global consistency and provides fine-grained semantics on the
points. We implement the latter as a differentiable Recurrent NN to allow joint
optimization. We evaluate the framework on two indoor and two outdoor 3D
datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance
comparable or superior to the state-of-the-art on all datasets.Comment: Accepted as a spotlight at the International Conference of 3D Vision
(3DV 2017
Conditionals and modularity in general logics
In this work in progress, we discuss independence and interpolation and
related topics for classical, modal, and non-monotonic logics
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