The terminological work for the translation of enoturistic Texts (German-Spanish): The use of Chinese as bridging language

La traducción de textos enoturísticos supone el conocimiento de una terminología específica, que pertenece a dos ámbitos especializados: por una parte, el sector vitivinícola y de la enología, que reproduce textos y discursos centrados en el cultivo de la vid y la elaboración del vino; por otra, el lenguaje propio del sector del turismo y del uso y disfrute de productos y servicios turísticos, cuya principal finalidad es producir en el lector la necesidad de consumir el producto turístico que se ofrece en los textos y los discursos. En estas páginas abordamos la dificultad que plantea la traducción de textos enoturísticos desde el idioma alemán al español para un traductor no germanoparlante y no hispanoparlante, sino sinoparlante. Veremos en qué medida el chino puede servir como lengua puente para realizar con éxito este tipo de traducción.Translation of wine-tourism texts requires knowledge of a specific terminology, which belongs to two specialized fields: on the one hand, the winegrowing and oenology sector, which reproduces texts and speeches centred on vinegrowing and wine-making; on the other hand, the language specific to the tourism sector and the use and enjoyment of tourism products and services, whose main purpose is to produce the reader the need to consume the tourist product offered in texts and speeches. In these pages we address the difficulty of translating winetourist texts from German to Spanish for a non-German- speaking and non-Spanish speaking translator. We will see to what extent Chinese can serve as a bridge language for this type of translation successfully

Bank lending and CEO turnover: Evidence from China

To maintain bank relationship, borrowers have motives to discipline themselves by forcing out underperforming CEOs. In this paper, we show that the state ownership in emerging markets renders this disciplinary mechanism ineffective. Using the contract information of bank loans for Chinese listed firms, we find that higher bank loan intensity overall does not affect the probability of forcing out an underperforming CEO. The absence of disciplinary effect is driven by the bank-firm pairs in which either the borrower or the lender is state-owned. However, the disciplinary effect is significant if a firm’s bank loans mostly consist of secured and short-term bank loans. Bank loans increase the likelihood of a forced CEO turnover, especially when joint-equity banks serve as the main lender. Overall, we propose that state ownership is an important factor driving the inefficiency of credit market in emerging countries

Dynamic routing and information sharing for connected and autonomous vehicles

This thesis models dynamic routing behaviors for connected and autonomous vehicles under stochastic situation of receiving incident information. Markov decision process for a single CAV and related model assumptions are introduced by the freeway instance. We formulate a generalized model based on the freeway instance and employ the value iteration algorithm to solve the problem by finding optimal policy. Numerical experiments, which are conducted on different networks, reveal the similar results about MDP model for a single CAV: if the vehicle gets the incident information, the best actions are always to travel the alternative routes to avoid the increased link cost. While for the uncertain states without receiving incident information, the best actions are always to travel on the direct links.Civil, Architectural, and Environmental Engineerin

SpOctA: A 3D Sparse Convolution Accelerator with Octree-Encoding-Based Map Search and Inherent Sparsity-Aware Processing

Point-cloud-based 3D perception has attracted great attention in various applications including robotics, autonomous driving and AR/VR. In particular, the 3D sparse convolution (SpConv) network has emerged as one of the most popular backbones due to its excellent performance. However, it poses severe challenges to real-time perception on general-purpose platforms, such as lengthy map search latency, high computation cost, and enormous memory footprint. In this paper, we propose SpOctA, a SpConv accelerator that enables high-speed and energy-efficient point cloud processing. SpOctA parallelizes the map search by utilizing algorithm-architecture co-optimization based on octree encoding, thereby achieving 8.8-21.2x search speedup. It also attenuates the heavy computational workload by exploiting inherent sparsity of each voxel, which eliminates computation redundancy and saves 44.4-79.1% processing latency. To optimize on-chip memory management, a SpConv-oriented non-uniform caching strategy is introduced to reduce external memory access energy by 57.6% on average. Implemented on a 40nm technology and extensively evaluated on representative benchmarks, SpOctA rivals the state-of-the-art SpConv accelerators by 1.1-6.9x speedup with 1.5-3.1x energy efficiency improvement.Comment: Accepted to ICCAD 202

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Open access articleSemantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable

A review of the therapeutic role of the new third-generation TKI olverembatinib in chronic myeloid leukemia

Several tyrosine kinase inhibitors (TKIs) have been developed as targeted therapies to inhibit the oncogenic activity of several tyrosine kinases in chronic myeloid leukemia (CML), acute lymphoid leukemia (ALL), gastrointestinal stromal tumor (GIST), and other diseases. TKIs have significantly improved the overall survival of these patients and changed the treatment strategy in the clinic. However, approximately 50% of patients develop resistance or intolerance to imatinib. For second-generation TKIs, approximately 30%–40% of patients need to change therapy by 5 years when they are used as first-line treatment. Clinical study analysis showed that the T315I mutation is highly associated with TKI resistance. Developing new drugs that target the T315I mutation will address the dilemma of treatment failure. Olverembatinib, as a third-generation TKI designed for the T315I mutation, is being researched in China. Preliminary clinical data show the safety and efficacy in treating CML patients harboring the T315I mutation or who are resistant to first- or second-line TKI treatment. Herein, we review the characteristics and clinical trials of olverembatinib. We also discuss its role in the management of CML patients