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French Visual Dictionary Learn French more quickly with pictures! You're more likely to remember something when you see it. So this Visual Dictionary helps you speed up your language learning by including a full-color photo with every term, letting you build your French vocabulary faster. Whether you want to get ahead in a class or dream of chatting with the locals on that long-planned trip to Paris, this book is what you need! Organized around themes such as simple conversation, food and dining, essential accommodations, and more, it can be your secret weapon. Inside * Navigating a city * Shopping and dealing with money * Dining out * Handling emergencies

Latin American Women Writers: An Encyclopedia presents the lives and critical works of over 170 women writers in Latin America between the sixteenth and twentieth centuries. This features thematic entries as well as biographies of female writers whose works were originally published in Spanish or Portuguese, and who have had an impact on literary, political, and social studies.Focusing on drama, poetry, and fiction, this work includes authors who have published at least three literary texts that have had a significant impact on Latin American literature and culture. Each entry is followed by extensive bibliographic references, including primary and secondary sources.Coverage consists of critical appreciation and analysis of the writers' works. Brief biographical data is included, but the main focus is on the meanings and contexts of the works as well as their cultural and political impact. In addition to author entries, other themes are explored, such as humor in contemporary Latin American fiction, lesbian literature in Latin America, magic, realism, or mother images in Latin American literature. The aim is to provide a unique, thorough, scholarly survey of women writers and their works in Latin America. This Encyclopedia will be of interest to both to the student of literature as well as to any reader interested in understanding more about Latin American culture, literature, and how women have represented gender and national issues throughout the centuries.

Since its publication in 1969, Émile Benveniste's Vocabulaire --here in a new translation as the Dictionary of Indo-European Concepts and Society --has been the classic reference for tracing the institutional and conceptual genealogy of the sociocultural worlds of gifts, contracts, sacrifice, hospitality, authority, freedom, ancient economy, and.

This comprehensive dictionary covers wastewater processes, pollution control, and every major area of environmental engineering used in industry. The alphabetically arranged entries cover key terms used in daily communications and documentation in all research and industrial activities. The several thousand key technical terms are written in easy-to-understand, practical language. The volume is an ideal reference for students and practitioners.

Deep neural network (DNN) obtained satisfactory results on different vision tasks; however, they usually suffer from large models and massive parameters during model deployment. While DNN compression can reduce the memory footprint of deep model effectively, so that the deep model can be deployed on portable devices. However, most of the existing model compression methods cost lots of time, e.g., vector quantization or pruning, which makes them inept to the application that needs fast computation. In this paper, we therefore explore how to accelerate the model compression process by reducing the computation cost. Then, we propose a new model compression method, termed dictionary-pair-based fast data-free DNN compression, which aims at reducing the memory consumption of DNNs without extra training and can greatly improve the compression efficiency. Specifically, our method performs tensor decomposition of DNN model with a fast dictionary-pair learning-based reconstruction approach, which can be deployed on different weight layers (e.g., convolution and fully connected layers). Given a pre-trained DNN model, we first divide the parameters (i.e., weights) of each layer into a series of partitions for dictionary pair-driven fast reconstruction, which can potentially discover more fine-grained information and provide the possibility for parallel model compression. Then, dictionaries of less memory occupation are learned to reconstruct the weights. Moreover, automatic hyper-parameter tuning and shared-dictionary mechanism is proposed to improve the model performance and availability. Extensive experiments on popular DNN models (i.e., VGG-16, ResNet-18 and ResNet-50) showed that our proposed weight compression method can significantly reduce the memory footprint and speed up the compression process, with less performance loss.

Over 3,200 entries This dictionary provides definitions and explanations for chemical engineering terms in areas including: materials, energy balances, reactions, separations, sustainability, safety, and ethics. Comprehensively cross-referenced and complemented by over 60 line drawings, this dictionary is the most authoritative of its kind. It also covers many pertinent terms from the fields of chemistry, physics, biology, and mathematics.

The accurate intelligent identification and detection of road cracks is a key issue in road maintenance, and it has become popular to perform this task through the field of computer vision. In this paper, we proposed a deep learning-based crack detection method that initially uses the idea of image sparse representation and compressed sensing to preprocess the datasets. Only the pixels that represent the crack features remain, while most pixels of non-crack features are relatively sparse, which can significantly improve the accuracy and efficiency of crack identification. The proposed method achieved good results based on the limited datasets of crack images. Various algorithms were tested, namely, linear smooth, median filtering, Gaussian smooth, and grayscale threshold, where the optimal parameters of the various algorithms were analyzed and trained with faster regions with convolutional neural network features (faster R-CNN). The results of the experiments showed that the proposed method has good robustness, with higher detection efficiency in the presence of, for example, road markings, shallow cracks, multiple cracks, and blurring. The result shows that the improvement of mean average precision (mAP) can reach 5% compared with the original method.

Practical applications of compressed sensing often restrict the choice of its two main ingredients. They may (i) prescribe the use of particular redundant dictionaries for certain classes of signals to become sparsely represented or (ii) dictate specific measurement mechanisms which exploit certain physical principles. On the problem of RIP measurement matrix design in compressed sensing with redundant dictionaries, we give a simple construction to derive sensing matrices whose compositions with a prescribed dictionary have with high probability the RIP in the klog(n/k) regime. Our construction thus provides recovery guarantees usually only attainable for sensing matrices from random ensembles with sparsifying orthonormal bases. Moreover, we use the dictionary factorization idea that our construction rests on in the application of magnetic resonance imaging, in which also the sensing matrix is prescribed by quantum mechanical principles. We propose a recovery algorithm based on transforming the acquired measurements such that the compressed sensing theory for RIP embeddings can be utilized to recover wavelet coefficients of the target image, and show its performance on examples from the fastMRI dataset.

This paper introduces a new dictionary learning strategy based on atoms obtained by translating the composition of K convolutions with S -sparse kernels of known support. The dictionary update step associated with this strategy is a non-convex optimization problem. We propose a practical formulation of this problem and introduce a Gauss–Seidel type algorithm referred to as alternative least square algorithm for its resolution. The search space of the proposed algorithm is of dimension K S , which is typically smaller than the size of the target atom and much smaller than the size of the image. Moreover, the complexity of this algorithm is linear with respect to the image size, allowing larger atoms to be learned (as opposed to small patches). The conducted experiments show that we are able to accurately approximate atoms such as wavelets, curvelets, sinc functions or cosines for large values of K. The proposed experiments also indicate that the algorithm generally converges to a global minimum for large values of K and S .