An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations

A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increase owing to the use of both ON and OFF events. AER data acquired by a DVS are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition.The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation

Mudskipper genomes provide insights into the terrestrial adaptation of amphibious fishes

Mudskippers are amphibious fishes that have developed morphological and physiological adaptations to match their unique lifestyles. Here we perform whole-genome sequencing of four representative mudskippers to elucidate the molecular mechanisms underlying these adaptations. We discover an expansion of innate immune system genes in the mudskippers that may provide defence against terrestrial pathogens. Several genes of the ammonia excretion pathway in the gills have experienced positive selection, suggesting their important roles in mudskippers’ tolerance to environmental ammonia. Some vision-related genes are differentially lost or mutated, illustrating genomic changes associated with aerial vision. Transcriptomic analyses of mudskippers exposed to air highlight regulatory pathways that are up- or down-regulated in response to hypoxia. The present study provides a valuable resource for understanding the molecular mechanisms underlying water-to-land transition of vertebrates

Peer to peer English/Chinese cross-language information retrieval

Peer to peer systems have been widely used in the internet. However, most of the peer to peer information systems are still missing some of the important features, for example cross-language IR (Information Retrieval) and collection selection / fusion features. Cross-language IR is the state-of-art research area in IR research community. It has not been used in any real world IR systems yet. Cross-language IR has the ability to issue a query in one language and receive documents in other languages. In typical peer to peer environment, users are from multiple countries. Their collections are definitely in multiple languages. Cross-language IR can help users to find documents more easily. E.g. many Chinese researchers will search research papers in both Chinese and English. With Cross-language IR, they can do one query in Chinese and get documents in two languages. The Out Of Vocabulary (OOV) problem is one of the key research areas in crosslanguage information retrieval. In recent years, web mining was shown to be one of the effective approaches to solving this problem. However, how to extract Multiword Lexical Units (MLUs) from the web content and how to select the correct translations from the extracted candidate MLUs are still two difficult problems in web mining based automated translation approaches. Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalized-score based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database but do not consider the retrieval performance of the remote search engine. This thesis presents research on building a peer to peer IR system with crosslanguage IR and advance collection profiling technique for fusion features. Particularly, this thesis first presents a new Chinese term measurement and new Chinese MLU extraction process that works well on small corpora. An approach to selection of MLUs in a more accurate manner is also presented. After that, this thesis proposes a collection profiling strategy which can discover not only collection content but also retrieval performance of the remote search engine. Based on collection profiling, a web-based query classification method and two collection fusion approaches are developed and presented in this thesis. Our experiments show that the proposed strategies are effective in merging results in uncooperative peer to peer environments. Here, an uncooperative environment is defined as each peer in the system is autonomous. Peer like to share documents but they do not share collection statistics. This environment is a typical peer to peer IR environment. Finally, all those approaches are grouped together to build up a secure peer to peer multilingual IR system that cooperates through X.509 and email system

Translation Disambiguation in Web-Based Translation Extraction for English-Chinese CLIR

Dictionary based translation is a traditional approach in use by cross-language information retrieval systems. However, significant performance degradation is often observed when queries contain words that do not appear in the dictionary. This is called the Out Of Vocabulary (OOV) problem. In recent years, web-based translation extraction was shown to be one of the more effective approaches to the solution of this problem. Previous work focussed on selecting the correct translation from a set of web extracted terms. The common methods for translation selection for web-based translation always rely on word frequency calculation but the results are not always satisfactory. In this paper we present our approach to the selection of terms in a more accurate manner. Our experiments show improvement in translation accuracy over other commonly used approaches

A Bottom-up Term Extraction Approach for Web-Based Translation in Chinese-English IR Systems

The extraction of Multiword Lexical Units (MLUs) in lexica is important to language related methods such as Natural Language Processing (NLP) and machine translation. As one word in one language may be translated into an MLU in another language, the extraction of MLUs plays an important role in Cross-Language Information Retrieval (CLIR), especially in finding the translation for words that are not in a dictionary. Web mining has been used for translating the query terms that are missing from dictionaries. MLU extraction is one of the key parts in search engine based translation. The MLU extraction result will finally affect the transition quality. Most statistical approaches to MLU extraction rely on large statistical information from huge corpora. In the case of search engine based translation, those approaches do not perform well because the size of corpus returned from a search engine is usually small. In this paper, we present a new string measurement and new Chinese MLU extraction process that works well on small corpora

Web-Based Query Translation for English-Chinese CLIR

Dictionary-based translation is a traditional approach in use by cross-language\ud information retrieval systems. However, significant performance degradation is\ud often observed when queries contain words that do not appear in the dictionary.\ud This is called the Out of Vocabulary (OOV) problem. In recent years, Web mining\ud has been shown to be one of the effective approaches for solving this problem.\ud However, the questions of how to extract Multiword Lexical Units (MLUs) from\ud the Web content and how to select the correct translations from the extracted\ud candidate MLUs are still two difficult problems in Web mining based automated\ud translation approaches.\ud Most statistical approaches to MLU extraction rely on statistical information\ud extracted from huge corpora. In the case of using Web mining techniques for\ud automated translations, these approaches do not perform well because the size of\ud the corpus is usually too small and statistical approaches that rely on a large sample\ud can become unreliable. In this paper, we present a new Chinese term measurement\ud and a new Chinese MLU extraction process that work well on small corpora. We\ud also present our approach to the selection of MLUs in a more accurate manner. Our\ud experiments show marked improvement in translation accuracy over other\ud commonly used approaches

A Bottom-up Term Extraction Approach for Web-based Translation in Chinese-English IR Systems

The extraction of Multiword Lexical Units (MLUs) in lexica is important to language related methods such as Natural Language Processing (NLP) and machine translation. As one word in one language may be translated into an MLU in another language, the extraction of MLUs plays an important role in Cross-Language Information Retrieval (CLIR), especially in finding the translation for words that are not in a dictionary. Web mining has been used for translating the query terms that are missing from dictionaries. MLU extraction is one of the key parts in search engine based translation. The MLU extraction result will finally affect the transition quality. Most statistical approaches to MLU extraction rely on large statistical information from huge corpora. In the case of search engine based translation, those approaches do not perform well because the size of corpus returned from a search engine is usually small. In this paper, we present a new string measurement and new Chinese MLU extraction process that works well on small corpora